2022
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Masters Theses
|
KARTIK SURYAVANSHI ADAPT: A 3 Degrees of Freedom Reconfigurable Force Balanced Parallel Manipulator for Aerial Applications (Masters Thesis) TU Delft Mechanical, Maritime and Materials Engineering, 2022, (van der Wijk, V. (mentor); Hamaza, S. (graduation committee); Herder, J.L. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:4e4e333d-643f-43b9-99cb-650d697f5baa,
title = {ADAPT: A 3 Degrees of Freedom Reconfigurable Force Balanced Parallel Manipulator for Aerial Applications},
author = {KARTIK SURYAVANSHI},
url = {http://resolver.tudelft.nl/uuid:4e4e333d-643f-43b9-99cb-650d697f5baa},
year = {2022},
date = {2022-01-01},
school = {TU Delft Mechanical, Maritime and Materials Engineering},
abstract = {In this work, we present the ADAPT, a novel reconfigurable force-balanced parallel manipulator with pantograph legs for spatial motions applied underneath a drone. The reconfigurable aspect allows different motion-based 3-DoF operation modes like translational, rotational, mixed, planar without disassembly. For the purpose of this study, the manipulator is used in translation mode only. A kinematic model is developed and validated for the manipulator. The design and motion capabilities are also validated both by conducting dynamics simulations of a simplified model on MSC ADAMS, and experiments on the physical setup.
The force-balanced nature of this novel design decouples the motion of the manipulator’s end-effector from the base, zeroing the reaction forces, making this design ideally suited for aerial manipulation in unmanned aerial vehicles (UAVs) applications, or generic floating-base applications.},
note = {van der Wijk, V. (mentor); Hamaza, S. (graduation committee); Herder, J.L. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
In this work, we present the ADAPT, a novel reconfigurable force-balanced parallel manipulator with pantograph legs for spatial motions applied underneath a drone. The reconfigurable aspect allows different motion-based 3-DoF operation modes like translational, rotational, mixed, planar without disassembly. For the purpose of this study, the manipulator is used in translation mode only. A kinematic model is developed and validated for the manipulator. The design and motion capabilities are also validated both by conducting dynamics simulations of a simplified model on MSC ADAMS, and experiments on the physical setup.<br/>The force-balanced nature of this novel design decouples the motion of the manipulator’s end-effector from the base, zeroing the reaction forces, making this design ideally suited for aerial manipulation in unmanned aerial vehicles (UAVs) applications, or generic floating-base applications. |
Alessandro Collicelli Incremental Nonlinear Dynamic Inversion controller - structural vibration coupling: Study of the phenomenon and the existing solutions (Masters Thesis) TU Delft Aerospace Engineering, 2022, (Smeur, E.J.J. (mentor); Pollack, T.S.C. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:66c34a84-5b47-49dd-b560-2836d9696e3c,
title = {Incremental Nonlinear Dynamic Inversion controller - structural vibration coupling: Study of the phenomenon and the existing solutions},
author = {Alessandro Collicelli},
url = {http://resolver.tudelft.nl/uuid:66c34a84-5b47-49dd-b560-2836d9696e3c},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Incremental Nonlinear Dynamics Inversion (INDI) flight controllers are sensor-based control systems, that are robust towards model uncertainty and with good disturbance rejection characteristics. These controllers show coupling effects in structural modes when implemented in specific flying vehicles with low-frequency structural motions. This paper investigates different INDI implementations, standard INDI, hybrid INDI, and notch filter placement in the INDI loop via simulation and flight tests on the Nederdrone. System identification of the structural characteristics of the vehicle and the system’s yaw dynamics are executed via ground vibration and hover flight tests. Closed-loop behaviour of theINDI inner-loop, disturbance rejection performance, and outer loop step-tracking performance was assessed with dedicated flight tests. The investigated INDI solutions show similar disturbance rejection and outer-loop step tracking performance, while the hybrid INDI solution performs a better nonlinear dynamic inversion.
Index Terms—INDI, complementary filter, unmanned vehicle, flight control system structural motion coupling},
note = {Smeur, E.J.J. (mentor); Pollack, T.S.C. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Incremental Nonlinear Dynamics Inversion (INDI) flight controllers are sensor-based control systems, that are robust towards model uncertainty and with good disturbance rejection characteristics. These controllers show coupling effects in structural modes when implemented in specific flying vehicles with low-frequency structural motions. This paper investigates different INDI implementations, standard INDI, hybrid INDI, and notch filter placement in the INDI loop via simulation and flight tests on the Nederdrone. System identification of the structural characteristics of the vehicle and the system’s yaw dynamics are executed via ground vibration and hover flight tests. Closed-loop behaviour of theINDI inner-loop, disturbance rejection performance, and outer loop step-tracking performance was assessed with dedicated flight tests. The investigated INDI solutions show similar disturbance rejection and outer-loop step tracking performance, while the hybrid INDI solution performs a better nonlinear dynamic inversion. <br/>Index Terms—INDI, complementary filter, unmanned vehicle, flight control system structural motion coupling |
Chenyao Wang A Bio-inspired Sensing Approach to in-Gust Flight of Flapping Wing MAVs (Masters Thesis) TU Delft Aerospace Engineering; TU Delft Control & Simulation, 2022, (Hamaza, S. (mentor); de Croon, G.C.H.E. (mentor); Wang, S. (graduation committee); de Wagter, C. (graduation committee); van Oudheusden, B.W. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:6215dd57-8d16-466b-a286-341538675d2d,
title = {A Bio-inspired Sensing Approach to in-Gust Flight of Flapping Wing MAVs},
author = {Chenyao Wang},
url = {http://resolver.tudelft.nl/uuid:6215dd57-8d16-466b-a286-341538675d2d},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering; TU Delft Control & Simulation},
abstract = {Flapping wing micro aerial vehicles (FWMAVs) are known for their flight agility and maneuverability. However, their in-gust flight performance and stability is still inferior to their biological counterparts. To this end, a simplified in-gust dynamic model, which could capture the main gust effects on FWMAVs, has been identified with real in-gust flights' data of a FWMAV, the Flapper Drone. Based on this model, an adaptive position and velocity controller was proposed with gain scheduling and implemented for in-gust flights under gust speeds up to 2.4 m/s. With this airflow-sensing based adaptive controller, the in-gust hovering stability of the Flapper Drone has been improved when the gust's intensity and frequency changes, comparing with the original fixed-gain cascaded PID controller case.},
note = {Hamaza, S. (mentor); de Croon, G.C.H.E. (mentor); Wang, S. (graduation committee); de Wagter, C. (graduation committee); van Oudheusden, B.W. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Flapping wing micro aerial vehicles (FWMAVs) are known for their flight agility and maneuverability. However, their in-gust flight performance and stability is still inferior to their biological counterparts. To this end, a simplified in-gust dynamic model, which could capture the main gust effects on FWMAVs, has been identified with real in-gust flights' data of a FWMAV, the Flapper Drone. Based on this model, an adaptive position and velocity controller was proposed with gain scheduling and implemented for in-gust flights under gust speeds up to 2.4 m/s. With this airflow-sensing based adaptive controller, the in-gust hovering stability of the Flapper Drone has been improved when the gust's intensity and frequency changes, comparing with the original fixed-gain cascaded PID controller case. |
Midas Gossye Developing a modular tool to simulate regeneration power potential using orographic wind-hovering UAVs (Masters Thesis) TU Delft Aerospace Engineering, 2022, (Remes, B.D.W. (mentor); Hwang, S. (graduation committee); de Croon, G.C.H.E. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:05f743a5-39c8-4860-9976-1eee532184a9,
title = {Developing a modular tool to simulate regeneration power potential using orographic wind-hovering UAVs},
author = {Midas Gossye},
url = {http://resolver.tudelft.nl/uuid:05f743a5-39c8-4860-9976-1eee532184a9},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Applications of Unmanned Aerial Vehicles (UAV's) are often limited by flight endurance. To address the limitation of endurance, we propose a regenerative soaring method in this paper. The atmospheric energy from updrafts generated by obstacles such as hills and ships can be harvested by UAV's using a regenerative electric drivetrain. With fixed-wing aircraft, the vehicle can hover with specific wind conditions, and the battery can be recharged in the air while wind hovering. In order to research the feasibility of this regenerative soaring method, we present a model to estimate hovering locations and the amount of extractable power using the proposed method. The resulting modular regeneration simulation tool can efficiently determine the possible hovering locations and provide an estimate of the power regeneration potential for each hovering location, given the UAV's aerodynamic characteristics and wind-field conditions. Furthermore, a working regenerative drivetrain test setup was constructed and characterised that showcased promising conversion efficiencies and can be incorporated into existing UAV's easily.},
note = {Remes, B.D.W. (mentor); Hwang, S. (graduation committee); de Croon, G.C.H.E. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Applications of Unmanned Aerial Vehicles (UAV's) are often limited by flight endurance. To address the limitation of endurance, we propose a regenerative soaring method in this paper. The atmospheric energy from updrafts generated by obstacles such as hills and ships can be harvested by UAV's using a regenerative electric drivetrain. With fixed-wing aircraft, the vehicle can hover with specific wind conditions, and the battery can be recharged in the air while wind hovering. In order to research the feasibility of this regenerative soaring method, we present a model to estimate hovering locations and the amount of extractable power using the proposed method. The resulting modular regeneration simulation tool can efficiently determine the possible hovering locations and provide an estimate of the power regeneration potential for each hovering location, given the UAV's aerodynamic characteristics and wind-field conditions. Furthermore, a working regenerative drivetrain test setup was constructed and characterised that showcased promising conversion efficiencies and can be incorporated into existing UAV's easily. |
Hani Abu-Jurji Sensorless Impedance Control for Curved Surface Inspections Using the Omni-Drone Aerial Manipulator (Masters Thesis) TU Delft Aerospace Engineering, 2022, (Hamaza, S. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:41222049-fb57-4f26-9b9e-85939af9fa63,
title = {Sensorless Impedance Control for Curved Surface Inspections Using the Omni-Drone Aerial Manipulator},
author = {Hani Abu-Jurji},
url = {http://resolver.tudelft.nl/uuid:41222049-fb57-4f26-9b9e-85939af9fa63},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {In this thesis, we develop a novel aerial manipulator system with an omni-directional workspace. The system comprises of a quadrotor platform equipped with a rotating five-bar linkage and serves the purpose of demonstrating the ability to perform contour tracing tasks on complex shapes, whilst airborne. In order to remove the dependency on additional force sensors and keep the design lightweight, an onboard force estimation scheme is implemented based on the generalized momentum of the system, using the torque feedback from the manipulator's motors. The computed force estimate feeds in a position-based impedance controller with the purpose of maintaining continuous contact through the manipulator's end-effector as the system traces contours of unknown curved geometry. Results demonstrate the estimator's ability to track the applied forces, while the impedance controller shows adequate contour following. The preliminary results obtained on both stationery and flight experiments validate this approach and show potential for aerial contact inspections of more complex structures.},
note = {Hamaza, S. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
In this thesis, we develop a novel aerial manipulator system with an omni-directional workspace. The system comprises of a quadrotor platform equipped with a rotating five-bar linkage and serves the purpose of demonstrating the ability to perform contour tracing tasks on complex shapes, whilst airborne. In order to remove the dependency on additional force sensors and keep the design lightweight, an onboard force estimation scheme is implemented based on the generalized momentum of the system, using the torque feedback from the manipulator's motors. The computed force estimate feeds in a position-based impedance controller with the purpose of maintaining continuous contact through the manipulator's end-effector as the system traces contours of unknown curved geometry. Results demonstrate the estimator's ability to track the applied forces, while the impedance controller shows adequate contour following. The preliminary results obtained on both stationery and flight experiments validate this approach and show potential for aerial contact inspections of more complex structures. |
Tomaso De Ponti Incremental Nonlinear Dynamic Inversion Controller for a Variable Skew Quad Plane (Masters Thesis) TU Delft Aerospace Engineering, 2022, (Smeur, E.J.J. (mentor); Remes, B.D.W. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:df815057-9ab6-42ee-8290-ce8099ffda68,
title = {Incremental Nonlinear Dynamic Inversion Controller for a Variable Skew Quad Plane},
author = {Tomaso De Ponti},
url = {http://resolver.tudelft.nl/uuid:df815057-9ab6-42ee-8290-ce8099ffda68},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {This paper presents the design of an Incremental Nonlinear Dynamic Inversion (INDI) controller for the novel platform VSQP. Part of the identified challenges is the develop- ment of a model for the actuator effectiveness and lift especially as a function of skew, the newly added degree of freedom. In particular it is assumed that the actuator effectiveness changes linearly with actuator state and that aerodynamic forces change quadratically with airspeed and depend mainly on the chordwise component of airspeed. Moreover, the position of the moving actuators is expressed as a function of the corresponding moment arm and the skew angle. The models and assumptions are verified through static and dynamic wind tunnel tests at the OJF of TuDelft. A WLS routine is used to solve the control allocation for the overactuated guidance loop. A lower cost is assigned to the use of the push motor so to steer the control allocation in its favor rather than commanding changes in attitude. A gradual switch of the hover motors in transition is achieved by scheduling the lift-pitch effectiveness with airspeed. Therefore, as airspeed increases the outerloop INDI controller evaluates that changing pitch to achieve a certain vertical acceleration set point results in an increasingly cheaper command allocation than changing thrust. An automatic skew controller is designed based on the developed control moment and lift models. The skew angle is scheduled with airspeed so to perform transition while also maximizing control authority. Finally, the controller is validated by performing multiple transitions inside the OJF windtunnel.},
note = {Smeur, E.J.J. (mentor); Remes, B.D.W. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
This paper presents the design of an Incremental Nonlinear Dynamic Inversion (INDI) controller for the novel platform VSQP. Part of the identified challenges is the develop- ment of a model for the actuator effectiveness and lift especially as a function of skew, the newly added degree of freedom. In particular it is assumed that the actuator effectiveness changes linearly with actuator state and that aerodynamic forces change quadratically with airspeed and depend mainly on the chordwise component of airspeed. Moreover, the position of the moving actuators is expressed as a function of the corresponding moment arm and the skew angle. The models and assumptions are verified through static and dynamic wind tunnel tests at the OJF of TuDelft. A WLS routine is used to solve the control allocation for the overactuated guidance loop. A lower cost is assigned to the use of the push motor so to steer the control allocation in its favor rather than commanding changes in attitude. A gradual switch of the hover motors in transition is achieved by scheduling the lift-pitch effectiveness with airspeed. Therefore, as airspeed increases the outerloop INDI controller evaluates that changing pitch to achieve a certain vertical acceleration set point results in an increasingly cheaper command allocation than changing thrust. An automatic skew controller is designed based on the developed control moment and lift models. The skew angle is scheduled with airspeed so to perform transition while also maximizing control authority. Finally, the controller is validated by performing multiple transitions inside the OJF windtunnel. |
Prawien Kanhai Adaptive control with Multivariate B-Splines and INDI: A case study for Vertical take-off and landing drones (Masters Thesis) TU Delft Aerospace Engineering, 2022, (Smeur, E.J.J. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:fdd8e2fa-1372-4f79-aa05-6ab152e848e1,
title = {Adaptive control with Multivariate B-Splines and INDI: A case study for Vertical take-off and landing drones},
author = {Prawien Kanhai},
url = {http://resolver.tudelft.nl/uuid:fdd8e2fa-1372-4f79-aa05-6ab152e848e1},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {In recent years the popularity of VTOL (Vertical Take-Off and Landing) drones has increased significantly. Due to their hybrid design, these drones can take off and land vertically and fly horizontally, enabling them to land in difficult terrain and have a more extensive range than the Quadcopter counterpart. However, this hybrid design also introduces complex dynamics that are difficult to model. For adequate control, this requires an adaptive element that can compensate for the modeling errors. Due to the significant change in flight conditions, adaptations must be made effectively over the entire flight envelope of a VTOL drone. This thesis introduces an adaptive controller that can cope with the large flight envelope and varying flight conditions of the VTOL drone and can adapt the controller effectively and store previous adaptations with multivariate B-splines during real-time flights.},
note = {Smeur, E.J.J. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
In recent years the popularity of VTOL (Vertical Take-Off and Landing) drones has increased significantly. Due to their hybrid design, these drones can take off and land vertically and fly horizontally, enabling them to land in difficult terrain and have a more extensive range than the Quadcopter counterpart. However, this hybrid design also introduces complex dynamics that are difficult to model. For adequate control, this requires an adaptive element that can compensate for the modeling errors. Due to the significant change in flight conditions, adaptations must be made effectively over the entire flight envelope of a VTOL drone. This thesis introduces an adaptive controller that can cope with the large flight envelope and varying flight conditions of the VTOL drone and can adapt the controller effectively and store previous adaptations with multivariate B-splines during real-time flights. |
Fréderic Dupon UWB Localisation: Distributed UWB inter-ranging for MAV swarms in large GNSS-denied environments (Masters Thesis) TU Delft Aerospace Engineering, 2022, (de Croon, G.C.H.E. (mentor); Pfeiffer, S.U. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:b7070c31-9db1-4a0c-8605-fb871914501b,
title = {UWB Localisation: Distributed UWB inter-ranging for MAV swarms in large GNSS-denied environments},
author = {Fréderic Dupon},
url = {http://resolver.tudelft.nl/uuid:b7070c31-9db1-4a0c-8605-fb871914501b},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {The use of micro air vehicles (MAV) is becoming increasingly mainstream and with them their applications have become more demanding across the board. The application of MAV’s in large GNSS-denied environments often asks for a distributed and scalable localisation system with minimal reliance on static localisation hardware. In this research a distributed ultra-wideband (UWB) localisation system that takes advantage of the collaborative capabilities of a swarm of MAV’s has been developed and tested in both simulation and practice. Additionally, a modular UWB simulator has been developed which enables researchers to test UWB localisation schemes for a swarm of MAV’s. It has been found that when taking advantage of the UWB inter-agent ranging capabilities of a swarm of micro air vehicles, one can increase the coverage of an UWB setup in spaces with coverage-issues and conversely increase the accuracy of an existing UWB setup that has full UWB coverage.},
note = {de Croon, G.C.H.E. (mentor); Pfeiffer, S.U. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
The use of micro air vehicles (MAV) is becoming increasingly mainstream and with them their applications have become more demanding across the board. The application of MAV’s in large GNSS-denied environments often asks for a distributed and scalable localisation system with minimal reliance on static localisation hardware. In this research a distributed ultra-wideband (UWB) localisation system that takes advantage of the collaborative capabilities of a swarm of MAV’s has been developed and tested in both simulation and practice. Additionally, a modular UWB simulator has been developed which enables researchers to test UWB localisation schemes for a swarm of MAV’s. It has been found that when taking advantage of the UWB inter-agent ranging capabilities of a swarm of micro air vehicles, one can increase the coverage of an UWB setup in spaces with coverage-issues and conversely increase the accuracy of an existing UWB setup that has full UWB coverage. |
Pietro Campolucci Model and Actuator Based Trajectory Tracking for Incremental Nonlinear Dynamic Inversion Controllers (Masters Thesis) TU Delft Aerospace Engineering, 2022, (Smeur, E.J.J. (mentor); Mancinelli, A. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:41895fac-aa59-47db-9c01-5e2879460b57,
title = {Model and Actuator Based Trajectory Tracking for Incremental Nonlinear Dynamic Inversion Controllers},
author = {Pietro Campolucci},
url = {http://resolver.tudelft.nl/uuid:41895fac-aa59-47db-9c01-5e2879460b57},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {This paper proposes a control strategy based on incremental nonlinear dynamic inversion (INDI), meant for trajectory tracking purposes. The controller extends the conven- tional capabilities of INDI by including actuator dynamics in the inversion law and introducing a state dependent compensation term to reduce the effort of the error controller. A complementary filter is employed to reduce the degrading effect introduced by the filtering-induced delay in the feedback loop. Both simulated and real flight tests are conducted on a quadrotor configuration with artificially slowed down actuators and a drag plate mounted on top, to better observe the effect of actuator dynamics and state dependent dynamics in trajectory tracking accuracy. Simulations show that the combination of the two additional features increases tracking accuracy both in the short and long term response. It is also found that an overestimation of the state compensation term leads to instability, which makes the strategy not robust to model mismatch. Real flight tests, involving the tracking of a series of doublets on the pitch attitude and a lemniscate of Bernoulli, show that, as the complexity of the maneuver increases, the less the state compensation term effectively contributes to an improved tracking when the model is incomplete. On the other hand, trajectory tracking accuracy due to the consideration of actuator dynamics shows consistency and improvement respect to conventional INDI solutions.},
note = {Smeur, E.J.J. (mentor); Mancinelli, A. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
This paper proposes a control strategy based on incremental nonlinear dynamic inversion (INDI), meant for trajectory tracking purposes. The controller extends the conven- tional capabilities of INDI by including actuator dynamics in the inversion law and introducing a state dependent compensation term to reduce the effort of the error controller. A complementary filter is employed to reduce the degrading effect introduced by the filtering-induced delay in the feedback loop. Both simulated and real flight tests are conducted on a quadrotor configuration with artificially slowed down actuators and a drag plate mounted on top, to better observe the effect of actuator dynamics and state dependent dynamics in trajectory tracking accuracy. Simulations show that the combination of the two additional features increases tracking accuracy both in the short and long term response. It is also found that an overestimation of the state compensation term leads to instability, which makes the strategy not robust to model mismatch. Real flight tests, involving the tracking of a series of doublets on the pitch attitude and a lemniscate of Bernoulli, show that, as the complexity of the maneuver increases, the less the state compensation term effectively contributes to an improved tracking when the model is incomplete. On the other hand, trajectory tracking accuracy due to the consideration of actuator dynamics shows consistency and improvement respect to conventional INDI solutions. |
Alejandro Barberia Chueca Onboard Drone Detection with Event Cameras (Masters Thesis) TU Delft Aerospace Engineering, 2022, (Dupeyroux, J.J.G. (mentor); de Croon, G.C.H.E. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:be142c0a-3475-4571-b9c5-9118d397c51a,
title = {Onboard Drone Detection with Event Cameras},
author = {Alejandro Barberia Chueca},
url = {http://resolver.tudelft.nl/uuid:be142c0a-3475-4571-b9c5-9118d397c51a},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {In an effort to develop a new relative sensing method for drone swarms, the suitability of event cameras is assessed for propeller detection. Benchmark tests were conducted for different propellers under different lighting and background conditions, varying the observation distance and spinning frequency. The different tests were evaluated on event count, frequency, and clustering, as these are the most characteristic properties of the propeller-generated signal. A propeller detection metric was derived as a fuzzy classifier to assess detectability. It was observed that the sensor employed is limiting the detection range due to low resolution, with a maximum detection range of 75 cm. While at low spinning frequencies it is possible to detect the propeller at such distance, for higher frequences (6000 to 8000 RPMs) the range decreases to 45 cm for the tests with highest blade to background contrast and two-blade propellers. It was observed that lower contrasts reduce the successful detections only to low frequencies, and three-blade propellers become completely indetectable due to the static overlap between the blades. Therefore, it is concluded that, at this stage of the technology, the use case of event cameras for relative sensing is constrained to close distances with high contrast.},
note = {Dupeyroux, J.J.G. (mentor); de Croon, G.C.H.E. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
In an effort to develop a new relative sensing method for drone swarms, the suitability of event cameras is assessed for propeller detection. Benchmark tests were conducted for different propellers under different lighting and background conditions, varying the observation distance and spinning frequency. The different tests were evaluated on event count, frequency, and clustering, as these are the most characteristic properties of the propeller-generated signal. A propeller detection metric was derived as a fuzzy classifier to assess detectability. It was observed that the sensor employed is limiting the detection range due to low resolution, with a maximum detection range of 75 cm. While at low spinning frequencies it is possible to detect the propeller at such distance, for higher frequences (6000 to 8000 RPMs) the range decreases to 45 cm for the tests with highest blade to background contrast and two-blade propellers. It was observed that lower contrasts reduce the successful detections only to low frequencies, and three-blade propellers become completely indetectable due to the static overlap between the blades. Therefore, it is concluded that, at this stage of the technology, the use case of event cameras for relative sensing is constrained to close distances with high contrast. |
Yvonne Eggers Intrinsic Plasticity for Robust Event-Based Optic Flow Estimation (Masters Thesis) TU Delft Aerospace Engineering, 2022, (de Croon, G.C.H.E. (mentor); Dupeyroux, J.J.G. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:3ffa7f45-8631-4224-a16b-4e2be097e35b,
title = {Intrinsic Plasticity for Robust Event-Based Optic Flow Estimation},
author = {Yvonne Eggers},
url = {http://resolver.tudelft.nl/uuid:3ffa7f45-8631-4224-a16b-4e2be097e35b},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Event cameras and spiking neural networks (SNNs) allow for a highly bio-inspired, low-latency and power efficient implementation of optic flow estimation. Just recently, a hierarchical SNN was proposed in which motion selectivity is learned from raw event data in an unsupervised manner using spike-timing-dependent plasticity (STDP). However, real-life applications of this SNN are currently still limited by the fact that the exact choice of neuron parameters depends on the spatiotemporal properties of the input. Furthermore, tuning the network is a challenging task due to the high degree of coupling between the various parameters. Inspired by neurons in biological brains that modify their intrinsic parameters through a process called intrinsic plasticity, this research proposes update rules which adapt the voltage threshold and maximum synaptic delay during inference. This allows applying the already trained network to a wider range of operating conditions and simplifies the tuning process. Starting with a detailed parameter analysis, primary functions and undesired side effects are assigned to each parameter. The update rules are then designed in such a way as to eliminate these side effects. Unlike existing update rules for the voltage threshold, this work does not attempt to keep the firing activity of output neurons within a specific range, but instead aims to adjust the threshold such that only the correct output maps spike. In particular, the voltage threshold is adapted such that output spikes occur in no more than two maps per retinotopic location. The maximum synaptic delay is adapted such that the resulting apparent pixel velocities of the input match those of the data used during training. A sensitivity analysis is presented which illustrates the effects of newly introduced parameters on the network performance. Furthermore, the adapted network is tested on real event data recorded onboard a drone avoiding obstacles. Due to the difficulties in matching the output of the adapted SNN to the ground truth data, quantitative results are inconclusive. However, qualitative results show a clear improvement in both the density and correctness of optic flow estimates.},
note = {de Croon, G.C.H.E. (mentor); Dupeyroux, J.J.G. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Event cameras and spiking neural networks (SNNs) allow for a highly bio-inspired, low-latency and power efficient implementation of optic flow estimation. Just recently, a hierarchical SNN was proposed in which motion selectivity is learned from raw event data in an unsupervised manner using spike-timing-dependent plasticity (STDP). However, real-life applications of this SNN are currently still limited by the fact that the exact choice of neuron parameters depends on the spatiotemporal properties of the input. Furthermore, tuning the network is a challenging task due to the high degree of coupling between the various parameters. Inspired by neurons in biological brains that modify their intrinsic parameters through a process called intrinsic plasticity, this research proposes update rules which adapt the voltage threshold and maximum synaptic delay during inference. This allows applying the already trained network to a wider range of operating conditions and simplifies the tuning process. Starting with a detailed parameter analysis, primary functions and undesired side effects are assigned to each parameter. The update rules are then designed in such a way as to eliminate these side effects. Unlike existing update rules for the voltage threshold, this work does not attempt to keep the firing activity of output neurons within a specific range, but instead aims to adjust the threshold such that only the correct output maps spike. In particular, the voltage threshold is adapted such that output spikes occur in no more than two maps per retinotopic location. The maximum synaptic delay is adapted such that the resulting apparent pixel velocities of the input match those of the data used during training. A sensitivity analysis is presented which illustrates the effects of newly introduced parameters on the network performance. Furthermore, the adapted network is tested on real event data recorded onboard a drone avoiding obstacles. Due to the difficulties in matching the output of the adapted SNN to the ground truth data, quantitative results are inconclusive. However, qualitative results show a clear improvement in both the density and correctness of optic flow estimates. |
Robin Ferede An Adaptive Control Strategy for Neural Network based Optimal Quadcopter Controllers (Masters Thesis) TU Delft Aerospace Engineering, 2022, (de Wagter, C. (mentor); de Croon, G.C.H.E. (mentor); Izzo, Dario (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:b43a9703-082c-47c7-a56e-d50794ee8c1c,
title = {An Adaptive Control Strategy for Neural Network based Optimal Quadcopter Controllers},
author = {Robin Ferede},
url = {http://resolver.tudelft.nl/uuid:b43a9703-082c-47c7-a56e-d50794ee8c1c},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Developing optimal controllers for aggressive high speed quadcopter flight remains a major challenge in the field of robotics. Recent work has shown that neural networks trained with supervised learning are a good candidate for real-time optimal quadcopter control. In these methods, the networks (termed G&CNets) are trained using optimal trajectories obtained from a dynamical model of the quadcopter by means of a direct transcription method. A major problem with these methods is the effects of unmodeled dynamics. In this work we identify these effects for G&CNets trained for power optimal full state-to-rpm feedback. We propose an adaptive control strategy to mitigate the effects of unmodeled roll, pitch and yaw moments. Our method works by generating optimal trajectories with constant external moments added to the model and training a network to learn the policy that maps state and external moments to the corresponding optimal rpm command. We demonstrate the effectiveness of our method by performing power-optimal hover-to-hover flights with and without moment feedback. The flight tests show that the inclusion of this moment feedback significantly improves the controller's performance. Additionally we compare the adaptive controller's performance to a time optimal Bang-Bang controller for consecutive waypoint flight and show significantly faster lap times on a 3x4m track.},
note = {de Wagter, C. (mentor); de Croon, G.C.H.E. (mentor); Izzo, Dario (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Developing optimal controllers for aggressive high speed quadcopter flight remains a major challenge in the field of robotics. Recent work has shown that neural networks trained with supervised learning are a good candidate for real-time optimal quadcopter control. In these methods, the networks (termed G&CNets) are trained using optimal trajectories obtained from a dynamical model of the quadcopter by means of a direct transcription method. A major problem with these methods is the effects of unmodeled dynamics. In this work we identify these effects for G&CNets trained for power optimal full state-to-rpm feedback. We propose an adaptive control strategy to mitigate the effects of unmodeled roll, pitch and yaw moments. Our method works by generating optimal trajectories with constant external moments added to the model and training a network to learn the policy that maps state and external moments to the corresponding optimal rpm command. We demonstrate the effectiveness of our method by performing power-optimal hover-to-hover flights with and without moment feedback. The flight tests show that the inclusion of this moment feedback significantly improves the controller's performance. Additionally we compare the adaptive controller's performance to a time optimal Bang-Bang controller for consecutive waypoint flight and show significantly faster lap times on a 3x4m track. |
Jan Verheyen Insect-Inspired Visual Guidance: are current familiarity-based models ready for long-ranged navigation? (Masters Thesis) TU Delft Aerospace Engineering, 2022, (de Croon, G.C.H.E. (mentor); Dupeyroux, J.J.G. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:823d959a-17b8-4fd9-bc45-a0ace45d29ca,
title = {Insect-Inspired Visual Guidance: are current familiarity-based models ready for long-ranged navigation?},
author = {Jan Verheyen},
url = {http://resolver.tudelft.nl/uuid:823d959a-17b8-4fd9-bc45-a0ace45d29ca},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Insects have — over millions of years of evolution — perfected many of the systems that roboticists aim to achieve; they can swiftly and robustly navigate through different environments under various conditions while at the same time being highly energy efficient. To reach this level of performance and efficiency one might want to look at and take inspiration from how these insects achieve their feats. Currently, no dataset exists that allows bio-inspired navigation models to be evaluated over long real- life routes. We present a novel dataset containing omnidirectional event vision, frame-based vision, depth frames, inertial measurement (IMU) readings, and centimeter-accurate GNSS positioning over kilometer long stretches in and around the TUDelft campus. The dataset is used to evaluate familiarity-based insect-inspired neural navigation models on their performance over longer sequences. It demonstrates that current scene familiarity models are not suited for long-ranged navigation, at least not in their current form.},
note = {de Croon, G.C.H.E. (mentor); Dupeyroux, J.J.G. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Insects have — over millions of years of evolution — perfected many of the systems that roboticists aim to achieve; they can swiftly and robustly navigate through different environments under various conditions while at the same time being highly energy efficient. To reach this level of performance and efficiency one might want to look at and take inspiration from how these insects achieve their feats. Currently, no dataset exists that allows bio-inspired navigation models to be evaluated over long real- life routes. We present a novel dataset containing omnidirectional event vision, frame-based vision, depth frames, inertial measurement (IMU) readings, and centimeter-accurate GNSS positioning over kilometer long stretches in and around the TUDelft campus. The dataset is used to evaluate familiarity-based insect-inspired neural navigation models on their performance over longer sequences. It demonstrates that current scene familiarity models are not suited for long-ranged navigation, at least not in their current form. |
Erik Oever An artificial neural network based method for grid-free acoustic source localization using multiple input frequencies (Masters Thesis) TU Delft Aerospace Engineering, 2022, (de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:a5713055-c4a4-4a6e-8cdc-4c2ac1e4e300,
title = {An artificial neural network based method for grid-free acoustic source localization using multiple input frequencies},
author = {Erik Oever},
url = {http://resolver.tudelft.nl/uuid:a5713055-c4a4-4a6e-8cdc-4c2ac1e4e300},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {In recent years, efforts are focused on developing an acoustic based autonomous detect and avoidance system for UAVs to minimize interference with other air traffic. The purpose of this research is to study the potential of artificial neural networks for fast, grid-free acoustic source localization. A multi-layer perceptron has been trained to localize simulated white noise acoustic point sources using a converted version of the cross spectral matrix. The ANN based method shows similar localization behaviour to different frequencies as conventional beamforming. A new ANN architecture is proposed that uses the converted cross spectral matrices of multiple different frequencies as input to improve the localization accuracy. The multi input model has shown to have a mean absolute error of approximately 0.27[m]. The proposed model has also been applied on real world recording data of an aircraft flyover. The ANN based method has shown to be able to obtain a prediction within approximately 0.05[s], compared to approximately 1000-2000[s] for conventional beamforming. However, the magnitude and inconsistency of the localization error for the recording is higher compared to the simulated white noise source.},
note = {de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
In recent years, efforts are focused on developing an acoustic based autonomous detect and avoidance system for UAVs to minimize interference with other air traffic. The purpose of this research is to study the potential of artificial neural networks for fast, grid-free acoustic source localization. A multi-layer perceptron has been trained to localize simulated white noise acoustic point sources using a converted version of the cross spectral matrix. The ANN based method shows similar localization behaviour to different frequencies as conventional beamforming. A new ANN architecture is proposed that uses the converted cross spectral matrices of multiple different frequencies as input to improve the localization accuracy. The multi input model has shown to have a mean absolute error of approximately 0.27[m]. The proposed model has also been applied on real world recording data of an aircraft flyover. The ANN based method has shown to be able to obtain a prediction within approximately 0.05[s], compared to approximately 1000-2000[s] for conventional beamforming. However, the magnitude and inconsistency of the localization error for the recording is higher compared to the simulated white noise source. |
Shawn Schröter We fly as one: Design and Joint Control of a Conjoined Biplane and Quadrotor (Masters Thesis) TU Delft Aerospace Engineering, 2022, (Smeur, E.J.J. (mentor); Remes, B.D.W. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:703d5b28-75c1-4d8b-a1a6-93510aed7b29,
title = {We fly as one: Design and Joint Control of a Conjoined Biplane and Quadrotor},
author = {Shawn Schröter},
url = {http://resolver.tudelft.nl/uuid:703d5b28-75c1-4d8b-a1a6-93510aed7b29},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Unmanned Aerial Vehicles, UAVs, serve many purposes
these days, such as short-range inspections
and long-distance search and rescue missions. Long-distance missions can entail a search in a building. Such missions require a large aircraft for endurance and a small aircraft for manoeuvrability in a building.
This paper proposes a novel combination of a quadrotor and a hybrid biplane capable of joint hover, joint forward flight, and mid-air disassembly followed by separate flight. During joint flight, the quadcopter and the biplane have no intercommunication.
This paper covers the design of a release system and a joint control strategy. Firstly, the in-flight
release is successfully tested in joint hover up to a forward pitch angle of -18 [deg]. Secondly, three control strategies for the quadrotor are compared:
a proportional angular rate damper, a proportional angular acceleration damper, and constant thrust without attitude control.
In all cases, the biplane uses a cascaded INDI attitude controller. Simulation and practical tests show that for intentional attitude changes, the different strategies
are of minimal influence. However, the angular rate damper
strategy for disturbance rejection has the lowest roll angle error and requires the smallest input command.
note = {Smeur, E.J.J. (mentor); Remes, B.D.W. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Unmanned Aerial Vehicles, UAVs, serve many purposes<br/>these days, such as short-range inspections<br/>and long-distance search and rescue missions. Long-distance missions can entail a search in a building. Such missions require a large aircraft for endurance and a small aircraft for manoeuvrability in a building.<br/><br/>This paper proposes a novel combination of a quadrotor and a hybrid biplane capable of joint hover, joint forward flight, and mid-air disassembly followed by separate flight. During joint flight, the quadcopter and the biplane have no intercommunication.<br/><br/>This paper covers the design of a release system and a joint control strategy. Firstly, the in-flight<br/>release is successfully tested in joint hover up to a forward pitch angle of -18 [deg]. Secondly, three control strategies for the quadrotor are compared:<br/>a proportional angular rate damper, a proportional angular acceleration damper, and constant thrust without attitude control.<br/>In all cases, the biplane uses a cascaded INDI attitude controller. Simulation and practical tests show that for intentional attitude changes, the different strategies<br/>are of minimal influence. However, the angular rate damper<br/>strategy for disturbance rejection has the lowest roll angle error and requires the smallest input command.<br |
Jingyi LU Evolving-to-Learn with Spiking Neural Networks (Masters Thesis) TU Delft Aerospace Engineering, 2022, (de Croon, G.C.H.E. (mentor); Hagenaars, J.J. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:3e2b645f-5ef2-41f5-9e8f-70d64fc8b2a6,
title = {Evolving-to-Learn with Spiking Neural Networks},
author = {Jingyi LU},
url = {http://resolver.tudelft.nl/uuid:3e2b645f-5ef2-41f5-9e8f-70d64fc8b2a6},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Inspired by the natural nervous system, synaptic plasticity rules are applied to train spiking neural networks. Different from learning algorithms such as propagation and evolution that are widely used to train spiking neural networks, synaptic plasticity rules learn the parameters with local information, making them suitable for online learning on neuromorphic hardware. However, when such rules are implemented to learn different new tasks, they usually require a significant amount of work on task-dependent fine-tuning. This thesis aims to make this process easier by employing an evolutionary algorithm that evolves suitable synaptic plasticity rules for the task at hand. More specifically, we provide a set of various local signals, a set of mathematical operators, and a global reward signal, after which a Cartesian genetic programming process finds an optimal learning rule from these components. In this work, we first test the algorithm in basic binary pattern classification tasks. Then, using this approach, we find learning rules that successfully solve an XOR and cart-pole task, and discover new learning rules that outperform the baseline rules from literature.},
note = {de Croon, G.C.H.E. (mentor); Hagenaars, J.J. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Inspired by the natural nervous system, synaptic plasticity rules are applied to train spiking neural networks. Different from learning algorithms such as propagation and evolution that are widely used to train spiking neural networks, synaptic plasticity rules learn the parameters with local information, making them suitable for online learning on neuromorphic hardware. However, when such rules are implemented to learn different new tasks, they usually require a significant amount of work on task-dependent fine-tuning. This thesis aims to make this process easier by employing an evolutionary algorithm that evolves suitable synaptic plasticity rules for the task at hand. More specifically, we provide a set of various local signals, a set of mathematical operators, and a global reward signal, after which a Cartesian genetic programming process finds an optimal learning rule from these components. In this work, we first test the algorithm in basic binary pattern classification tasks. Then, using this approach, we find learning rules that successfully solve an XOR and cart-pole task, and discover new learning rules that outperform the baseline rules from literature. |
Tommy Tran Semantic Segmentation using Deep Neural Networks for MAVs (Masters Thesis) TU Delft Aerospace Engineering, 2022, (de Croon, G.C.H.E. (mentor); Xu, Y. (mentor); de Wagter, C. (graduation committee); van Gemert, J.C. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:7735d01c-b4cd-4173-a584-652f269c078c,
title = {Semantic Segmentation using Deep Neural Networks for MAVs},
author = {Tommy Tran},
url = {http://resolver.tudelft.nl/uuid:7735d01c-b4cd-4173-a584-652f269c078c},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Semantic segmentation methods have been developed and applied to single images for object segmentation. However, for robotic applications such as high-speed agile Micro Air Vehicles (MAVs) in Autonomous Drone Racing (ADR), it is more interesting to consider temporal information as video sequences are correlated over time. In this work, we evaluate the performance of state-of-the-art methods such as Recurrent Neural Networks (RNNs), 3D Convolutional Neural Networks (CNNs), and optical flow for video semantic segmentation in terms of accuracy and inference speed on three datasets with different camera motion configurations. The results show that using an RNN with convolutional operators outperforms all methods and achieves a performance boost of 10.8% on the KITTI (MOTS) dataset with 3 degrees of freedom (DoF) motion and a small 0.6% improvement on the CyberZoo dataset with 6 DoF motion over the single-frame-based semantic segmentation method. The inference speed was measured on the CyberZoo dataset, achieving 321 fps on an NVIDIA GeForce RTX 2060 GPU and 30 fps on an NVIDIA Jetson TX2 mobile computer.},
note = {de Croon, G.C.H.E. (mentor); Xu, Y. (mentor); de Wagter, C. (graduation committee); van Gemert, J.C. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Semantic segmentation methods have been developed and applied to single images for object segmentation. However, for robotic applications such as high-speed agile Micro Air Vehicles (MAVs) in Autonomous Drone Racing (ADR), it is more interesting to consider temporal information as video sequences are correlated over time. In this work, we evaluate the performance of state-of-the-art methods such as Recurrent Neural Networks (RNNs), 3D Convolutional Neural Networks (CNNs), and optical flow for video semantic segmentation in terms of accuracy and inference speed on three datasets with different camera motion configurations. The results show that using an RNN with convolutional operators outperforms all methods and achieves a performance boost of 10.8% on the KITTI (MOTS) dataset with 3 degrees of freedom (DoF) motion and a small 0.6% improvement on the CyberZoo dataset with 6 DoF motion over the single-frame-based semantic segmentation method. The inference speed was measured on the CyberZoo dataset, achieving 321 fps on an NVIDIA GeForce RTX 2060 GPU and 30 fps on an NVIDIA Jetson TX2 mobile computer. |
Chris Groen Grammatical Evolution for Optimising Drone Behaviors (Masters Thesis) TU Delft Aerospace Engineering, 2022, (Li, S. (mentor); de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:0fc90d7b-7aa3-4501-be7f-ac31330957b6,
title = {Grammatical Evolution for Optimising Drone Behaviors},
author = {Chris Groen},
url = {http://resolver.tudelft.nl/uuid:0fc90d7b-7aa3-4501-be7f-ac31330957b6},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {This paper reviews the application of grammatical evolution for the optimisation of low level parameters and high level behaviors for two drone behaviors, namely wall-following and navigation. In order to optimise these low level parameters and high level behaviors, grammatical evolution was applied to behavior trees. Grammatical evolution provided a significant improvement in the wall-following behavior of a drone, creating a more robust behavior. There was no improvement for the navigation behavior however, with the success rate of navigating deteriorating in some cases. The evolved wallfollowing behavior was compared and tested against another wall-following controller from literature, and shown to be superior. A real-life experiment was also conducted for the wall-following behavior, which led to positive results after correcting for the reality gap. For the wall-following behavior, the grammatical evolution promoted a continuous scanning behavior, which greatly increased it’s awareness of obstacles. Significant recommendations were given to improve the results of the grammatical evolution for both behaviors.},
note = {Li, S. (mentor); de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
This paper reviews the application of grammatical evolution for the optimisation of low level parameters and high level behaviors for two drone behaviors, namely wall-following and navigation. In order to optimise these low level parameters and high level behaviors, grammatical evolution was applied to behavior trees. Grammatical evolution provided a significant improvement in the wall-following behavior of a drone, creating a more robust behavior. There was no improvement for the navigation behavior however, with the success rate of navigating deteriorating in some cases. The evolved wallfollowing behavior was compared and tested against another wall-following controller from literature, and shown to be superior. A real-life experiment was also conducted for the wall-following behavior, which led to positive results after correcting for the reality gap. For the wall-following behavior, the grammatical evolution promoted a continuous scanning behavior, which greatly increased it’s awareness of obstacles. Significant recommendations were given to improve the results of the grammatical evolution for both behaviors. |
Miscellaneous
|
Yilun Wu; Federico Paredes-Vallés; Guido C. H. E. Croon Lightweight Event-based Optical Flow Estimation via Iterative Deblurring (Miscellaneous) 2022. @misc{2211.13726,
title = {Lightweight Event-based Optical Flow Estimation via Iterative Deblurring},
author = {Yilun Wu and Federico Paredes-Vallés and Guido C. H. E. Croon},
url = {https://arxiv.org/abs/2211.13726},
year = {2022},
date = {2022-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Rik J. Bouwmeester; Federico Paredes-Vallés; Guido C. H. E. Croon NanoFlowNet: Real-time Dense Optical Flow on a Nano Quadcopter (Miscellaneous) 2022. @misc{2209.06918,
title = {NanoFlowNet: Real-time Dense Optical Flow on a Nano Quadcopter},
author = {Rik J. Bouwmeester and Federico Paredes-Vallés and Guido C. H. E. Croon},
url = {https://arxiv.org/abs/2209.06918},
year = {2022},
date = {2022-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Yingfu Xu; Guido C. H. E. Croon CUAHN-VIO: Content-and-Uncertainty-Aware Homography Network for Visual-Inertial Odometry (Miscellaneous) 2022. @misc{2208.13935,
title = {CUAHN-VIO: Content-and-Uncertainty-Aware Homography Network for Visual-Inertial Odometry},
author = {Yingfu Xu and Guido C. H. E. Croon},
url = {https://arxiv.org/abs/2208.13935},
year = {2022},
date = {2022-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Sabrina M. Neuman; Brian Plancher; Bardienus P. Duisterhof; Srivatsan Krishnan; Colby Banbury; Mark Mazumder; Shvetank Prakash; Jason Jabbour; Aleksandra Faust; Guido C. H. E. Croon; Vijay Janapa Reddi Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots (Miscellaneous) 2022. @misc{2205.05748,
title = {Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots},
author = {Sabrina M. Neuman and Brian Plancher and Bardienus P. Duisterhof and Srivatsan Krishnan and Colby Banbury and Mark Mazumder and Shvetank Prakash and Jason Jabbour and Aleksandra Faust and Guido C. H. E. Croon and Vijay Janapa Reddi},
url = {https://arxiv.org/abs/2205.05748},
year = {2022},
date = {2022-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Cheng Liu; Erik-Jan Kampen; Guido C. H. E. Croon Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learning (Miscellaneous) 2022. @misc{2203.14749,
title = {Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learning},
author = {Cheng Liu and Erik-Jan Kampen and Guido C. H. E. Croon},
url = {https://arxiv.org/abs/2203.14749},
year = {2022},
date = {2022-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Diana A. Olejnik; Sunyi Wang; Julien Dupeyroux; Stein Stroobants; Matěj Karásek; Christophe De Wagter; Guido Croon An Experimental Study of Wind Resistance and Power Consumption in MAVs with a Low-Speed Multi-Fan Wind System (Miscellaneous) 2022. @misc{2202.06723,
title = {An Experimental Study of Wind Resistance and Power Consumption in MAVs with a Low-Speed Multi-Fan Wind System},
author = {Diana A. Olejnik and Sunyi Wang and Julien Dupeyroux and Stein Stroobants and Matěj Karásek and Christophe De Wagter and Guido Croon},
url = {https://arxiv.org/abs/2202.06723},
year = {2022},
date = {2022-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
PhD Theses
|
C. Wagter Hover and fast flight of minimum-mass mission-capable flying robots (PhD Thesis) Delft University of Technology, 2022, ISBN: 978-94-6384-333-1. @phdthesis{3d15049bf69542d8b8d18d4bac1c8abd,
title = {Hover and fast flight of minimum-mass mission-capable flying robots},
author = {C. Wagter},
url = {https://research.tudelft.nl/en/publications/hover-and-fast-flight-of-minimum-mass-mission-capable-flying-robo},
doi = {10.4233/uuid:3d15049b-f695-42d8-b8d1-8d4bac1c8abd},
isbn = {978-94-6384-333-1},
year = {2022},
date = {2022-01-01},
school = {Delft University of Technology},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
|
2021
|
Journal Articles
|
D. Wijnker, T. van Dijk, G.C.H.E. de Croon, C. De Wagter Hear-and-avoid for unmanned air vehicles using convolutional neural networks (Journal Article) In: International Journal of Micro Air Vehicles, vol. 13, pp. 1-15, 2021. @article{audio_ijmav_2021,
title = { Hear-and-avoid for unmanned air vehicles using convolutional neural networks},
author = {D. Wijnker, T. van Dijk, G.C.H.E. de Croon, C. De Wagter},
url = {https://journals.sagepub.com/doi/full/10.1177/1756829321992137},
doi = {10.1177/1756829321992137},
year = {2021},
date = {2021-02-10},
journal = {International Journal of Micro Air Vehicles},
volume = {13},
pages = {1-15},
abstract = {To investigate how an unmanned air vehicle can detect manned aircraft with a single microphone, an audio data set is created in which unmanned air vehicle ego-sound and recorded aircraft sound are mixed together. A convolutional neural network is used to perform air traffic detection. Due to restrictions on flying unmanned air vehicles close to aircraft, the data set has to be artificially produced, so the unmanned air vehicle sound is captured separately from the aircraft sound. They are then mixed with unmanned air vehicle recordings, during which labels are given indicating whether the mixed recording contains aircraft audio or not. The model is a convolutional neural network that uses the features Mel frequency cepstral coefficient, spectrogram or Mel spectrogram as input. For each feature, the effect of unmanned air vehicle/aircraft amplitude ratio, the type of labeling, the window length and the addition of third party aircraft sound database recordings are explored. The results show that the best performance is achieved using the Mel spectrogram feature. The performance increases when the unmanned air vehicle/aircraft amplitude ratio is decreased, when the time window is increased or when the data set is extended with aircraft audio recordings from a third party sound database. Although the currently presented approach has a number of false positives and false negatives that is still too high for real-world application, this study indicates multiple paths forward that can lead to an interesting performance. Finally, the data set is provided as open access.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
To investigate how an unmanned air vehicle can detect manned aircraft with a single microphone, an audio data set is created in which unmanned air vehicle ego-sound and recorded aircraft sound are mixed together. A convolutional neural network is used to perform air traffic detection. Due to restrictions on flying unmanned air vehicles close to aircraft, the data set has to be artificially produced, so the unmanned air vehicle sound is captured separately from the aircraft sound. They are then mixed with unmanned air vehicle recordings, during which labels are given indicating whether the mixed recording contains aircraft audio or not. The model is a convolutional neural network that uses the features Mel frequency cepstral coefficient, spectrogram or Mel spectrogram as input. For each feature, the effect of unmanned air vehicle/aircraft amplitude ratio, the type of labeling, the window length and the addition of third party aircraft sound database recordings are explored. The results show that the best performance is achieved using the Mel spectrogram feature. The performance increases when the unmanned air vehicle/aircraft amplitude ratio is decreased, when the time window is increased or when the data set is extended with aircraft audio recordings from a third party sound database. Although the currently presented approach has a number of false positives and false negatives that is still too high for real-world application, this study indicates multiple paths forward that can lead to an interesting performance. Finally, the data set is provided as open access. |
G.C.H.E. de Croon, C. De Wagter, T. Seidl Enhancing optical-flow-based control by learning visual appearance cues for flying robots (Journal Article) In: Nature Machine Intelligence, vol. 3, no. 1, 2021. @article{nature_ai_optical_flow,
title = {Enhancing optical-flow-based control by learning visual appearance cues for flying robots},
author = {G.C.H.E. de Croon, C. De Wagter, T. Seidl},
doi = {10.1038/s42256-020-00279-7},
year = {2021},
date = {2021-01-19},
journal = {Nature Machine Intelligence},
volume = {3},
number = {1},
abstract = {Flying insects employ elegant optical-flow-based strategies to solve complex tasks such as landing or obstacle avoidance. Roboticists have mimicked these strategies on flying robots with only limited success, because optical flow (1) cannot disentangle distance from velocity and (2) is less informative in the highly important flight direction. Here, we propose a solution to these fundamental shortcomings by having robots learn to estimate distances to objects by their visual appearance. The learning process obtains supervised targets from a stability-based distance estimation approach. We have successfully implemented the process on a small flying robot. For the task of landing, it results in faster, smooth landings. For the task of obstacle avoidance, it results in higher success rates at higher flight speeds. Our results yield improved robotic visual navigation capabilities and lead to a novel hypothesis on insect intelligence: behaviours that were described as optical-flow-based and hardwired actually benefit from learning processes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Flying insects employ elegant optical-flow-based strategies to solve complex tasks such as landing or obstacle avoidance. Roboticists have mimicked these strategies on flying robots with only limited success, because optical flow (1) cannot disentangle distance from velocity and (2) is less informative in the highly important flight direction. Here, we propose a solution to these fundamental shortcomings by having robots learn to estimate distances to objects by their visual appearance. The learning process obtains supervised targets from a stability-based distance estimation approach. We have successfully implemented the process on a small flying robot. For the task of landing, it results in faster, smooth landings. For the task of obstacle avoidance, it results in higher success rates at higher flight speeds. Our results yield improved robotic visual navigation capabilities and lead to a novel hypothesis on insect intelligence: behaviours that were described as optical-flow-based and hardwired actually benefit from learning processes. |
Patricia P Parlevliet; Andrey Kanaev; Chou P Hung; Andreas Schweiger; Frederick D Gregory; Ryad Benosman; Guido C H E de Croon; Yoram Gutfreund; Chung Chuan Lo; Cynthia F Moss Autonomous Flying With Neuromorphic Sensing (Journal Article) In: Frontiers in Neuroscience, vol. 15, 2021, ISSN: 1662-4548. @article{64b98a51388b454fb60cf6e9e02842c8,
title = {Autonomous Flying With Neuromorphic Sensing},
author = {{Patricia P } Parlevliet and Andrey Kanaev and {Chou P } Hung and Andreas Schweiger and {Frederick D } Gregory and Ryad Benosman and {Guido C H E } {de Croon} and Yoram Gutfreund and {Chung Chuan} Lo and {Cynthia F } Moss},
url = {https://research.tudelft.nl/en/publications/autonomous-flying-with-neuromorphic-sensing},
doi = {10.3389/fnins.2021.672161},
issn = {1662-4548},
year = {2021},
date = {2021-01-01},
journal = {Frontiers in Neuroscience},
volume = {15},
publisher = {Frontiers Media},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Pulkit Goyal; Antoine Cribellier; Guido C H E de Croon; Martin J Lankheet; Johan L van Leeuwen; Remco P M Pieters; Florian T Muijres Bumblebees land rapidly and robustly using a sophisticated modular flight control strategy (Journal Article) In: iScience, vol. 24, no. 5, 2021, ISSN: 2589-0042. @article{b8ea5aa24527426491652d2a82f84732,
title = {Bumblebees land rapidly and robustly using a sophisticated modular flight control strategy},
author = {Pulkit Goyal and Antoine Cribellier and {Guido C H E } {de Croon} and {Martin J } Lankheet and {Johan L } {van Leeuwen} and {Remco P M } Pieters and {Florian T } Muijres},
url = {https://research.tudelft.nl/en/publications/bumblebees-land-rapidly-and-robustly-using-a-sophisticated-modula},
doi = {10.1016/j.isci.2021.102407},
issn = {2589-0042},
year = {2021},
date = {2021-01-01},
journal = {iScience},
volume = {24},
number = {5},
publisher = {Cell Press},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
C De Wagter; B Remes; E Smeur; F van Tienen; R Ruijsink; K van Hecke; E van der Horst The NederDrone: A hybrid lift, hybrid energy hydrogen UAV (Journal Article) In: International Journal of Hydrogen Energy, vol. 46, no. 29, pp. 16003–16018, 2021, ISSN: 0360-3199. @article{0bbd9df59c4842f2b3a6284876642d15,
title = {The NederDrone: A hybrid lift, hybrid energy hydrogen UAV},
author = {C {De Wagter} and B Remes and E Smeur and F {van Tienen} and R Ruijsink and K {van Hecke} and E {van der Horst}},
url = {https://research.tudelft.nl/en/publications/the-nederdrone-a-hybrid-lift-hybrid-energy-hydrogen-uav},
doi = {10.1016/j.ijhydene.2021.02.053},
issn = {0360-3199},
year = {2021},
date = {2021-01-01},
journal = {International Journal of Hydrogen Energy},
volume = {46},
number = {29},
pages = {16003--16018},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Christophe De Wagter; Federico Paredes-Vallés; Nilay Sheth; Guido Croon The Artificial Intelligence behind the winning entry to the 2019 AI Robotic Racing Competition (Journal Article) In: 2021. @article{2109.14985,
title = {The Artificial Intelligence behind the winning entry to the 2019 AI Robotic Racing Competition},
author = {Christophe De Wagter and Federico Paredes-Vallés and Nilay Sheth and Guido Croon},
url = {https://arxiv.org/abs/2109.14985},
doi = {10.55417/fr.2022042},
year = {2021},
date = {2021-01-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Chris P. L. Jong; Bart D. W. Remes; Sunyou Hwang; Christophe De Wagter Never landing drone: Autonomous soaring of a unmanned aerial vehicle in front of a moving obstacle (Journal Article) In: International Journal of Micro Air Vehicles, vol. 13, 2021, ISSN: 1756-8293. @article{1989fa30b9d54c99b63424f881be428b,
title = {Never landing drone: Autonomous soaring of a unmanned aerial vehicle in front of a moving obstacle},
author = {Chris P. L. Jong and Bart D. W. Remes and Sunyou Hwang and Christophe De Wagter},
url = {https://research.tudelft.nl/en/publications/never-landing-drone-autonomous-soaring-of-a-unmanned-aerial-vehic},
doi = {10.1177/17568293211060500},
issn = {1756-8293},
year = {2021},
date = {2021-01-01},
journal = {International Journal of Micro Air Vehicles},
volume = {13},
publisher = {Multi-Science Publishing Co. Ltd},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Poramate Manoonpong; Luca Patan`e; Xiaofeng Xiong; Ilya Brodoline; Julien Dupeyroux; Stéphane Viollet; Paolo Arena; Julien R. Serres Insect-inspired robots: Bridging biological and artificial systems (Journal Article) In: Sensors, vol. 21, no. 22, 2021, ISSN: 1424-8220. @article{0c29f45f148247258d2b4b154c12b645,
title = {Insect-inspired robots: Bridging biological and artificial systems},
author = {Poramate Manoonpong and Luca Patan`e and Xiaofeng Xiong and Ilya Brodoline and Julien Dupeyroux and Stéphane Viollet and Paolo Arena and Julien R. Serres},
url = {https://research.tudelft.nl/en/publications/insect-inspired-robots-bridging-biological-and-artificial-systems},
doi = {10.3390/s21227609},
issn = {1424-8220},
year = {2021},
date = {2021-01-01},
journal = {Sensors},
volume = {21},
number = {22},
publisher = {MDPI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
C. De Wagter; F. Paredes-Vallés; N. Sheth; G. Croon Learning fast in autonomous drone racing (Journal Article) In: Nature Machine Intelligence, vol. 3, no. 10, pp. 923, 2021, ISSN: 2522-5839, (Copyright: Copyright 2021 Elsevier B.V., All rights reserved.). @article{fabcc0c2a2c34c9fade7b4b03119bb55,
title = {Learning fast in autonomous drone racing},
author = {C. De Wagter and F. Paredes-Vallés and N. Sheth and G. Croon},
url = {https://research.tudelft.nl/en/publications/learning-fast-in-autonomous-drone-racing},
doi = {10.1038/s42256-021-00405-z},
issn = {2522-5839},
year = {2021},
date = {2021-01-01},
journal = {Nature Machine Intelligence},
volume = {3},
number = {10},
pages = {923},
publisher = {Springer Nature},
note = {Copyright: Copyright 2021 Elsevier B.V., All rights reserved.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Raoul Dinaux; Nikhil Wessendorp; Julien Dupeyroux; Guido C. H. E. De Croon FAITH: Fast Iterative Half-Plane Focus of Expansion Estimation Using Optic Flow (Journal Article) In: IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 7627–7634, 2021, ISSN: 2377-3766. @article{6df8a47b66df4a229bfe02ef54874887,
title = {FAITH: Fast Iterative Half-Plane Focus of Expansion Estimation Using Optic Flow},
author = {Raoul Dinaux and Nikhil Wessendorp and Julien Dupeyroux and Guido C. H. E. De Croon},
url = {https://research.tudelft.nl/en/publications/faith-fast-iterative-half-plane-focus-of-expansion-estimation-usi},
doi = {10.1109/LRA.2021.3100153},
issn = {2377-3766},
year = {2021},
date = {2021-01-01},
journal = {IEEE Robotics and Automation Letters},
volume = {6},
number = {4},
pages = {7627--7634},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Sven Pfeiffer; Christophe De Wagter; Guido C. H. E. De Croon A Computationally Efficient Moving Horizon Estimator for Ultra-Wideband Localization on Small Quadrotors (Journal Article) In: IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 6725–6732, 2021, ISSN: 2377-3766, (Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.). @article{db62ab4841ed4e768c8aa61bf0e4f86f,
title = {A Computationally Efficient Moving Horizon Estimator for Ultra-Wideband Localization on Small Quadrotors},
author = {Sven Pfeiffer and Christophe De Wagter and Guido C. H. E. De Croon},
url = {https://research.tudelft.nl/en/publications/a-computationally-efficient-moving-horizon-estimator-for-ultra-wi},
doi = {10.1109/LRA.2021.3095519},
issn = {2377-3766},
year = {2021},
date = {2021-01-01},
journal = {IEEE Robotics and Automation Letters},
volume = {6},
number = {4},
pages = {6725--6732},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
note = {Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Ye Zhou; Hann Woei Ho; Qiping Chu Extended incremental nonlinear dynamic inversion for optical flow control of micro air vehicles (Journal Article) In: Aerospace Science and Technology, vol. 116, 2021, ISSN: 1270-9638. @article{e8893d1b300e42249a42fc879c94169b,
title = {Extended incremental nonlinear dynamic inversion for optical flow control of micro air vehicles},
author = {Ye Zhou and Hann Woei Ho and Qiping Chu},
url = {https://research.tudelft.nl/en/publications/extended-incremental-nonlinear-dynamic-inversion-for-optical-flow},
doi = {10.1016/j.ast.2021.106889},
issn = {1270-9638},
year = {2021},
date = {2021-01-01},
journal = {Aerospace Science and Technology},
volume = {116},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Seng Man Wong; Hann Woei Ho; Mohd Zulkifly Abdullah Design and Fabrication of a Dual Rotor-Embedded Wing Vertical Take-Off and Landing Unmanned Aerial Vehicle (Journal Article) In: Unmanned Systems, vol. 9, no. 1, pp. 45–63, 2021, ISSN: 2301-3850. @article{3ef0950c1f824e58b1c847e69a6adae5,
title = {Design and Fabrication of a Dual Rotor-Embedded Wing Vertical Take-Off and Landing Unmanned Aerial Vehicle},
author = {Seng Man Wong and Hann Woei Ho and Mohd Zulkifly Abdullah},
url = {https://research.tudelft.nl/en/publications/design-and-fabrication-of-a-dual-rotor-embedded-wing-vertical-tak},
doi = {10.1142/S2301385021500096},
issn = {2301-3850},
year = {2021},
date = {2021-01-01},
journal = {Unmanned Systems},
volume = {9},
number = {1},
pages = {45--63},
publisher = {World Scientific Publishing},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
H Y Lee; H W Ho; Y Zhou Deep Learning-based Monocular Obstacle Avoidance for Unmanned Aerial Vehicle Navigation in Tree Plantations: Faster Region-based Convolutional Neural Network Approach (Journal Article) In: Journal of Intelligent and Robotic Systems: Theory and Applications, vol. 101, no. 1, 2021, ISSN: 0921-0296. @article{41bbc3e560d1448093ab2ce6220da811,
title = {Deep Learning-based Monocular Obstacle Avoidance for Unmanned Aerial Vehicle Navigation in Tree Plantations: Faster Region-based Convolutional Neural Network Approach},
author = {{H Y } Lee and {H W } Ho and Y Zhou},
url = {https://research.tudelft.nl/en/publications/deep-learning-based-monocular-obstacle-avoidance-for-unmanned-aer},
doi = {10.1007/s10846-020-01284-z},
issn = {0921-0296},
year = {2021},
date = {2021-01-01},
journal = {Journal of Intelligent and Robotic Systems: Theory and Applications},
volume = {101},
number = {1},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Inproceedings
|
M. Gossye; S. Hwang; B. D. W. Remes Developing a modular tool to simulate regeneration power potential using orographic wind-hovering uavs (Inproceedings) In: Martinez-Carranza, Jose (Ed.): Proceedings of the 12th International Micro Air Vehicle Conference, pp. 116–123, 2021, (12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021). @inproceedings{9cee2bfb6e0d475a83b8afcd52e8f69f,
title = {Developing a modular tool to simulate regeneration power potential using orographic wind-hovering uavs},
author = {M. Gossye and S. Hwang and B. D. W. Remes},
editor = {Jose Martinez-Carranza},
url = {https://research.tudelft.nl/en/publications/developing-a-modular-tool-to-simulate-regeneration-power-potentia-2},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings of the 12th International Micro Air Vehicle Conference},
pages = {116--123},
note = {12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
G. Gonzalez Archundia; G. C. H. E. Croon; D. A. Olejnik; M. Karasek Position controller for a flapping wing drone using uwb (Inproceedings) In: Martinez-Carranza, Jose (Ed.): Proceedings of the 12th International Micro Air Vehicle Conference, pp. 85–92, 2021, (12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021). @inproceedings{ecb50cf0e6cc4373932035df09604749,
title = {Position controller for a flapping wing drone using uwb},
author = {G. Gonzalez Archundia and G. C. H. E. Croon and D. A. Olejnik and M. Karasek},
editor = {Jose Martinez-Carranza},
url = {https://research.tudelft.nl/en/publications/position-controller-for-a-flapping-wing-drone-using-uwb-2},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings of the 12th International Micro Air Vehicle Conference},
pages = {85--92},
note = {12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
E. D. Vroon; Jim Rojer; G. C. H. E. Croon Motion-based mav detection in gps-denied environments (Inproceedings) In: Martinez-Carranza, Jose (Ed.): Proceedings of the 12th International Micro Air Vehicle Conference, pp. 42–49, 2021, (12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021). @inproceedings{2c157cfccbba4651801c4ddcd5e46e3f,
title = {Motion-based mav detection in gps-denied environments},
author = {E. D. Vroon and Jim Rojer and G. C. H. E. Croon},
editor = {Jose Martinez-Carranza},
url = {https://research.tudelft.nl/en/publications/motion-based-mav-detection-in-gps-denied-environments},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings of the 12th International Micro Air Vehicle Conference},
pages = {42--49},
note = {12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
D. C. Wijngaarden; E. J. J. Smeur; B. D. W. Remes Flight code convergence: fixedwing, rotorcraft, hybrid (Inproceedings) In: 12th International Micro Air Vehicle Conference, pp. 21–27, 2021, (12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021). @inproceedings{35c4b1cbfa7e4776a59740eba912776f,
title = {Flight code convergence: fixedwing, rotorcraft, hybrid},
author = {D. C. Wijngaarden and E. J. J. Smeur and B. D. W. Remes},
url = {https://research.tudelft.nl/en/publications/flight-code-convergence-fixedwing-rotorcraft-hybrid},
year = {2021},
date = {2021-01-01},
booktitle = {12th International Micro Air Vehicle Conference},
pages = {21--27},
note = {12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
L. F. A. Dellemann; C. Wagter Hybrid UAV Attitude Control using INDI and Dynamic Tilt-Twist (Inproceedings) In: Martinez-Carranza, Jose (Ed.): Proceedings of the 12th International Micro Air Vehicle Conference, pp. 131–136, 2021, (12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021). @inproceedings{acaf7267c5bd410fb20346333bdea387,
title = {Hybrid UAV Attitude Control using INDI and Dynamic Tilt-Twist},
author = {L. F. A. Dellemann and C. Wagter},
editor = {Jose Martinez-Carranza},
url = {https://research.tudelft.nl/en/publications/hybrid-uav-attitude-control-using-indi-and-dynamic-tilt-twist},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings of the 12th International Micro Air Vehicle Conference},
pages = {131--136},
note = {12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
J. M. Westenberger; C. Wagter; G. C. H. E. Croon Onboard Time-Optimal Control for Tiny Quadcopters (Inproceedings) In: Martinez-Carranza, Jose (Ed.): Proceedings of the 12th International Micro Air Vehicle Conference, pp. 93–100, 2021, (12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021). @inproceedings{dcdd83ec14574013abe6ff37bcbcee04,
title = {Onboard Time-Optimal Control for Tiny Quadcopters},
author = {J. M. Westenberger and C. Wagter and G. C. H. E. Croon},
editor = {Jose Martinez-Carranza},
url = {https://research.tudelft.nl/en/publications/onboard-time-optimal-control-for-tiny-quadcopters},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings of the 12th International Micro Air Vehicle Conference},
pages = {93--100},
note = {12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
H. J. Karssies; C. Wagter XINCA: Extended Incremental Non-linear Control Allocation on the TU Delft Quadplane (Inproceedings) In: Martinez-Carranza, Jose (Ed.): Proceedings of the 12th International Micro Air Vehicle Conference, pp. 74–84, 2021, (12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021). @inproceedings{a0639ab725c0448ba2f4a9cc77f02c00,
title = {XINCA: Extended Incremental Non-linear Control Allocation on the TU Delft Quadplane},
author = {H. J. Karssies and C. Wagter},
editor = {Jose Martinez-Carranza},
url = {https://research.tudelft.nl/en/publications/xinca-extended-incremental-non-linear-control-allocation-on-the-t},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings of the 12th International Micro Air Vehicle Conference},
pages = {74--84},
note = {12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
S. Li; C. Wagter; G. C. H. E. Croon Nonlinear model predictive control for improving range-based relative localization by maximizing observability (Inproceedings) In: Martinez-Carranza, Jose (Ed.): Proceedings of the 12th International Micro Air Vehicle Conference, pp. 28–34, 2021, (12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021). @inproceedings{b592bef42a74410d9118080d75f09dc1,
title = {Nonlinear model predictive control for improving range-based relative localization by maximizing observability},
author = {S. Li and C. Wagter and G. C. H. E. Croon},
editor = {Jose Martinez-Carranza},
url = {https://research.tudelft.nl/en/publications/nonlinear-model-predictive-control-for-improving-range-based-rela},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings of the 12th International Micro Air Vehicle Conference},
pages = {28--34},
note = {12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Daniel Willemsen; Mario Coppola; Guido C. H. E. Croon MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world models (Inproceedings) In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021, pp. 5635–5640, IEEE, United States, 2021, ISBN: 978-1-6654-1715-0, (2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ; Conference date: 27-09-2021 Through 01-10-2021). @inproceedings{d3f8712a70684f63812db25c62c65604,
title = {MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world models},
author = {Daniel Willemsen and Mario Coppola and Guido C. H. E. Croon},
url = {https://research.tudelft.nl/en/publications/mambpo-sample-efficient-multi-robot-reinforcement-learning-using-},
doi = {10.1109/IROS51168.2021.9635836},
isbn = {978-1-6654-1715-0},
year = {2021},
date = {2021-01-01},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021},
pages = {5635--5640},
publisher = {IEEE},
address = {United States},
series = {IEEE International Conference on Intelligent Robots and Systems},
note = {2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ; Conference date: 27-09-2021 Through 01-10-2021},
keywords = {},
pubstate = {published},
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Nikhil Wessendorp; Raoul Dinaux; Julien Dupeyroux; Guido C. H. E. Croon Obstacle Avoidance onboard MAVs using a FMCW Radar (Inproceedings) In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021, pp. 117–122, IEEE, United States, 2021, ISBN: 978-1-6654-1715-0, (2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ; Conference date: 27-09-2021 Through 01-10-2021). @inproceedings{b721b22bbeb84ee5a17bebd74fc0d962,
title = {Obstacle Avoidance onboard MAVs using a FMCW Radar},
author = {Nikhil Wessendorp and Raoul Dinaux and Julien Dupeyroux and Guido C. H. E. Croon},
url = {https://research.tudelft.nl/en/publications/obstacle-avoidance-onboard-mavs-using-a-fmcw-radar},
doi = {10.1109/IROS51168.2021.9635901},
isbn = {978-1-6654-1715-0},
year = {2021},
date = {2021-01-01},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021},
pages = {117--122},
publisher = {IEEE},
address = {United States},
series = {IEEE International Conference on Intelligent Robots and Systems},
note = {2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ; Conference date: 27-09-2021 Through 01-10-2021},
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Stavrow Bahnam; Sven Pfeiffer; Guido C. H. E. Croon Stereo Visual Inertial Odometry for Robots with Limited Computational Resources* (Inproceedings) In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021, pp. 9154–9159, IEEE, United States, 2021, ISBN: 978-1-6654-1715-0, (This work was supported by Royal Brinkman Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. ; 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ; Conference date: 27-09-2021 Through 01-10-2021). @inproceedings{6e78308d25d147c1b66c2f105ce09c1b,
title = {Stereo Visual Inertial Odometry for Robots with Limited Computational Resources*},
author = {Stavrow Bahnam and Sven Pfeiffer and Guido C. H. E. Croon},
url = {https://research.tudelft.nl/en/publications/stereo-visual-inertial-odometry-for-robots-with-limited-computati},
doi = {10.1109/IROS51168.2021.9636807},
isbn = {978-1-6654-1715-0},
year = {2021},
date = {2021-01-01},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021},
pages = {9154--9159},
publisher = {IEEE},
address = {United States},
series = {IEEE International Conference on Intelligent Robots and Systems},
note = {This work was supported by Royal Brinkman Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. ; 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ; Conference date: 27-09-2021 Through 01-10-2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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