Publications

@mastersthesis{uuid:ffa1ec41-3930-4dfe-b454-e11c3517a7f4,

title = {Vertical Landing for Micro Air Vehicles using Event-Based Optical Flow},

author = {B. J. Pijnacker Hordijk},

url = {http://resolver.tudelft.nl/uuid:ffa1ec41-3930-4dfe-b454-e11c3517a7f4},

year  = {2016},

date = {2016-01-01},

school = {Delft University of Technology},

abstract = {Small flying robots can perform landing maneuvers using bio-inspired optical flow by maintaining a constant divergence. However, optical flow is typically estimated from frame sequences recorded by standard miniature cameras. This requires processing full images on-board, limiting the update rate of divergence measurements, thus the speed of the control loop and the robot. Event-based cameras overcome these limitations by only measuring pixel-level brightness changes at microsecond temporal accuracy, hence providing an efficient mechanism for optical flow estimation. This thesis presents, to the best of our knowledge, the first research integrating event-based optical flow estimation into the control loop of a flying robot. We extend an existing 'local plane fitting' algorithm to obtain an improved and more computationally efficient optical flow estimation method, valid for a wide range of optical flow velocities. This method is validated for real event sequences. In addition, a method for estimating the divergence from event-based optical flow is introduced, which accounts for the aperture problem. The developed algorithms are implemented in a constant divergence landing controller on-board of a quadrotor. Flight tests demonstrate that, using event-based optical flow, accurate divergence estimates can be obtained over a wide range of speeds. This enables the quadrotor to perform very fast landing maneuvers.},

note = {de Croon, G.C.H.E. (mentor)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:8efab9c5-e78b-40ff-ab37-a563366d22f9,

title = {The Exploring DelFly: How to increase the indoor explored area of the DelFly Explorer by means of computationally efficient routing decisions?},

author = {C. R. Fonville},

url = {http://resolver.tudelft.nl/uuid:8efab9c5-e78b-40ff-ab37-a563366d22f9},

year  = {2016},

date = {2016-01-01},

school = {Delft University of Technology},

abstract = {Small robots, such as Micro Aerial Vehicles, form an increasingly popular field of interests in research, industry and the consumer market. The autonomous capabilities of these systems keep evolving and one of the main research goals is to reach full autonomy. However, this is often achieved at the cost of growing hardware demands. In this study a computationally light and efficient way to enhance autonomous on-board exploration capabilities for the DelFly Explorer, a 20-gram flapping wing Micro Aerial Vehicle (FWMAV), is presented. Both theory and new insights were combined to design an exploration algorithm for the on-board stereo-vision system. The algorithm primarily consists of a disparity map based decision tree, different exploration phases and computationally light odometry. Computer simulations proved the effectiveness of the algorithm to enable autonomous exploration capabilities for the FWMAV system. Initial flight tests also show that the proposed algorithm increases its exploration capabilities and form a foundation for future research.},

note = {de Croon, G.C.H.E. (mentor)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{Pijnacker2016,

title = {Vertical Landing for Micro Air Vehicles using Event-Based Optical Flow},

author = {B J Pijnacker Hordijk},

url = {http://resolver.tudelft.nl/uuid:ffa1ec41-3930-4dfe-b454-e11c3517a7f4},

year  = {2016},

date = {2016-01-01},

address = {Delft, NL},

school = {Delft University of Technology},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:b76bd35d-9d56-472e-8ff8-35fd453b6a49,

title = {Incremental Nonlinear Dynamic Inversion and Multihole Pressure Probes for Disturbance Rejection Control of Fixed-Wing Micro Air Vehicles},

author = {E. S. Van der Sman},

url = {http://resolver.tudelft.nl/uuid:b76bd35d-9d56-472e-8ff8-35fd453b6a49},

year  = {2016},

date = {2016-01-01},

school = {Delft University of Technology},

abstract = {Maintaining stable flight during high turbulence intensities is challenging for fixed-wing micro air vehicles. Two methods have been identified to improve the disturbance rejection performance of the MAV: incremental nonlinear dynamic inversion and phase-advanced pitch probes. Incremental nonlinear dynamic inversion uses the angular acceleration measurements to counteract disturbances. Multihole pressure probes measure the incoming flow angle and velocity ahead of the wing in order to react to gusts before an inertial response has occurred. The performance of incremental nonlinear dynamic inversion is compared to a traditional proportional integral derivative controller with and without the multihole pressure probes. The attitude controllers are tested by performing autonomous wind tunnel flights and stability augmented outdoor flights. This thesis shows that nonlinear dynamic inversion improves the disturbance rejection performance of fixed-wing MAVs compared to traditional proportional integral derivative controllers.},

note = {Chu, Q.P. (mentor); Remes, B. (mentor); Smeur, E.J.J. (mentor)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:55f9ab7a-2651-4a90-93a0-a8c9ddc7c6a9,

title = {Self-Supervised Monocular Distance Learning on a Lightweight Micro Air Vehicle},

author = {K. Lamers},

url = {http://resolver.tudelft.nl/uuid:55f9ab7a-2651-4a90-93a0-a8c9ddc7c6a9},

year  = {2016},

date = {2016-01-01},

school = {Delft University of Technology},

abstract = {This thesis presents all the work performed in developing a novel method for estimating distances on a flapping wing micro air vehicle using a monocular camera. These distance estimates are useful for providing a way to avoid collisions while flying indoors. The proposed method is based on a self-supervised learning algorithm that uses a short range impact detector to learn camera based long range distance estimates. The first part of this thesis contains an extended version of the paper on this topic as was submitted to the 2016 International Conference on Intelligent Robots and Systems (IROS). The second part contains the preliminary thesis that was preparatory to the final work and gives an in-depth overview of the state-of-the-art of different aspects of the problem as found in literature.},

note = {Hoekstra, J.M. (mentor); De Croon, G.C.H.E. (mentor); Tijmons, S. (mentor); Guo, J. (mentor)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{Fonville2016,

title = {The Exploring DelFly: How to increase the indoor explored area of the DelFly Explorer by means of computationally efficient routing decisions?},

author = {C R Fonville},

url = {http://resolver.tudelft.nl/uuid:8efab9c5-e78b-40ff-ab37-a563366d22f9},

year  = {2016},

date = {2016-01-01},

address = {Delft, NL},

school = {Delft University of Technology},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:424ead9b-50be-4e80-94a9-d041a1418dd3,

title = {The influence of a bottom camera in indoor ground-segmentation based obstacle avoiding performance for MAVs},

author = {M. W. M. Kuijpers},

url = {http://resolver.tudelft.nl/uuid:424ead9b-50be-4e80-94a9-d041a1418dd3},

year  = {2016},

date = {2016-01-01},

school = {Delft University of Technology},

note = {de Wagter, C. (mentor); de Croon, G.C.H.E. (mentor)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{Lamers2016b,

title = {Self-Supervised Monocular Distance Learning on a Lightweight Micro Air Vehicle},

author = {K Lamers},

url = {http://resolver.tudelft.nl/uuid:55f9ab7a-2651-4a90-93a0-a8c9ddc7c6a9},

year  = {2016},

date = {2016-01-01},

address = {Delft, NL},

school = {Delft University of Technology},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:3704b044-b9bf-454a-8678-0d140bd1d308,

title = {Actuator Saturation Handling using Weighted Optimal Control Allocation Applied to an INDI Controlled Quadcopter},

author = {D. C. Höppener},

url = {http://resolver.tudelft.nl/uuid:3704b044-b9bf-454a-8678-0d140bd1d308},

year  = {2016},

date = {2016-01-01},

school = {Delft University of Technology},

abstract = {Incremental Nonlinear Dynamic Inversion provides a high performance attitude controller for multi-rotor Micro Aerial Vehicles by providing very good disturbance rejection capabilities. Flights conducted with a quadcopter revealed undesired pitch and rolling motions which occurred simultaneously with actuator saturation for instantaneous yaw angle reference tracking commands. Constrained control allocation methods can increase the system's performance by providing an effective strategy to prioritize control objectives, and redistribute control effort accordingly. Weighted Least Squares control allocation makes the constrained control allocation problem a quadratic optimization problem. An iterative solver based on the computationally efficient active-set algorithm finds the optimal control distribution for a weighted control objective. In this paper the Weighted Least Squares control allocator is used to overcome two challenges 1) increase performance by applying prioritization between control objectives and redistribute control effort accordingly, accounting for the actuator limits 2) enable flight when flying with severely compromised actuator(s). Real-world flight experiments are performed and show a significant increase in performance for high load yaw maneuvers, and enabled a quadcopter to perform controlled flight with a severely compromised actuator},

note = {de Wagter, C. (mentor)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:382dec56-7789-40df-af28-f2e61de99fad,

title = {Attitude control- and stabilisation moment generation of the DelFly using Wing Tension Modulation},

author = {R. M. J. Janssen},

url = {http://resolver.tudelft.nl/uuid:382dec56-7789-40df-af28-f2e61de99fad},

year  = {2016},

date = {2016-01-01},

school = {Delft University of Technology},

note = {Karasek, M. (mentor)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:58a4c285-e3b6-4bf0-b885-2908077e9b02,

title = {Tracking and Following a Moving Person Onboard a Small Pocket Drone},

author = {T. Duro},

url = {http://resolver.tudelft.nl/uuid:58a4c285-e3b6-4bf0-b885-2908077e9b02},

year  = {2016},

date = {2016-01-01},

school = {Delft University of Technology},

abstract = {This paper presents a vision based strategy, designed to work fully onboard a small pocket drone, for autonomously tracking and following a person. Flying a drone is not an easy task, usually requiring a trained pilot, with the presented system it is possible to use a drone for filming or taking pictures from previously inaccessible places without the need for a person controlling the aircraft. Such framework is comprised by two main components, a tracker and a control system. The tracker has the function of estimating the position of the person that is being followed, while the control system gets the drone near that person. Limited by payload weight, power consumption and processing power the system results in a delicate balance between these constraints. The main contributions of this paper are the comparison between two state-of-the-art visual trackers running on paparazzi, Struck and KCF, as well as the control system that uses the tracker’s output location to perform the person following task. Then a new tracker is developed to be as computationally light as possible so that it can run onboard a small pocket drone, based on HOG feature extraction, it uses logistic regression to train a detector on the appearance of a person.},

note = {De Croon, G. (mentor); De Wagter, C (mentor); Meertens, R. (mentor)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:4b4c4e4b-5e45-4166-bd2c-f35a1e495c6a,

title = {Learning Depth from Single Monocular Images Using Stereo Supervisory Input},

author = {J. Paquim},

url = {http://resolver.tudelft.nl/uuid:4b4c4e4b-5e45-4166-bd2c-f35a1e495c6a},

year  = {2016},

date = {2016-01-01},

school = {Delft University of Technology},

abstract = {Stereo vision systems are often employed in robotics as a means for obstacle avoidance and navigation. These systems have inherent depth-sensing limitations, with significant problems in occluded and untextured regions, leading to sparse depth maps. We propose using a monocular depth estimation algorithm to tackle these problems, in a Self-Supervised Learning (SSL) framework. The algorithm learns online from the sparse depth map generated by a stereo vision system, producing a dense depth map. The algorithm is designed to be computationally efficient, for implementation onboard resource-constrained mobile robots and unmanned aerial vehicles. Within that context, it can be used to provide both reliability against a stereo camera failure, as well as more accurate depth perception, by filling in missing depth information, in occluded and low texture regions. This in turn allows the use of more efficient sparse stereo vision algorithms. We test the algorithm offline on a new, high resolution, stereo dataset, of scenes shot in indoor environments, and processed using both sparse and dense stereo matching algorithms. It is shown that the algorithm’s performance doesn’t deteriorate, and in fact sometimes improves, when learning only from sparse, high confidence regions rather than from the computationally expensive, dense, occlusion-filled and highly post-processed dense depth maps. This makes the approach very promising for self- supervised learning on autonomous robots.},

note = {de Croon, G.C.H.E. (mentor)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:e5604e9a-c241-4236-83dd-5fc823e7e284,

title = {Mission Planning for Sensor Network Deployment using a Fleet of Drones},

author = {P. Goyal},

url = {http://resolver.tudelft.nl/uuid:e5604e9a-c241-4236-83dd-5fc823e7e284},

year  = {2016},

date = {2016-01-01},

school = {Delft University of Technology},

abstract = {Various methods for route planning of on-road vehicles to serve transportation requests have been developed in the literature in order to reduce transportation and operational costs. The applicability and thus development of these methods is primarily motivated by the field of application. This article deals with the mission planning for a fleet of drones to deploy sensors in a network. In particular, they are conceived to complete the task of delivering geophones in the seismic surveys. Unlike conventional on-road vehicles used for delivery purposes, every drone in the fleet is constrained to make a frequent return trip back to the depot to pick-up a new payload and restore its battery. A centralized planner is proposed in this article due to this constraint. The problem of planning is decomposed into two phases: route formation and route scheduling. The first phase is handled using the extensive formulation of Multi-Trip Vehicle Routing Problem (MTVRP) aiming at minimizing the overall journey time. A heuristic method is also proposed for this phase which provides near-optimal solutions in a computationally efficient manner. The second phase of the planning algorithm deals with the unaddressed problem of depot congestion arising due to the frequent visits of each drone to the depot. This problem is expressed in the form of a Mixed-Integer Linear Program (MILP) that can be solved using available software. This phase is computationally intensive and comparatively slow which restricts the usage of this mission planner in the re-planning phase to the cases involving longer journeys with limited number of routes. The results from a flight-test are also presented in order to demonstrate the mission planner.},

note = {Hoekstra, J.M. (mentor); Blacquiere, G. (mentor); de Croon, G.C.H.E. (mentor); Smeur, E.J.J. (mentor)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:e714d5b9-236d-4509-bc80-79035c0f4725,

title = {Quadrotor Thrust Vectoring Control with Time Optimal Trajectory Planning in Constant Wind Fields},

author = {J. P. Rocha Silva},

url = {http://resolver.tudelft.nl/uuid:e714d5b9-236d-4509-bc80-79035c0f4725},

year  = {2016},

date = {2016-01-01},

school = {Delft University of Technology},

abstract = {This work proposes a control strategy to follow time optimal trajectories planned to visit a given set of waypoints in windy conditions. The aerodynamic effects of quadrotors are investigated, with emphasis on blade flapping, induced and parasitic drag. An extended method to identify all the aerodynamic coefficients is developed, and their influence on the performance is analyzed. A computationally efficient three steps approach is suggested to optimize the trajectory, by minimizing aerodynamic drag and jerk while still guaranteeing near optimal results. The derived smooth trajectory is compared with standard discrete point to point followed by low-pass filtering trajectories, showing energetic improvements in thrust and reductions in Euler angles aggressiveness. By exploiting the non-linear aerodynamic effects and using a priori trajectory information, a thrust vectoring controller is designed and compared with a standard PID controller, showing an increase in performance by reducing the tracking delay and extending the flight envelope.},

note = {de Croon, G.C.H.E. (mentor)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:8145032a-9b1c-48c1-bf46-6f9d7405e5ef,

title = {Relative Localization for Collision Avoidance in Micro Air Vehicle Teams},

author = {M. Coppola},

url = {http://resolver.tudelft.nl/uuid:8145032a-9b1c-48c1-bf46-6f9d7405e5ef},

year  = {2016},

date = {2016-01-01},

school = {Delft University of Technology},

abstract = {A current limitation of using Micro Air Vehicles in teams is the high risk of collisions between members. Knowledge of relative location is needed in order to perform evasive maneuvers from such collisions. We propose an on-board Bluetooth-based relative localization scheme. Bluetooth is a light-weight and energy efficient communication technology that is readily available on even the smallest Micro Air Vehicle units. In this work, it is exploited for communication between team members to exchange on-board states (velocity, height, and orientation), and the strength of the communication signal is used to infer relative range. The data is fused on-board by each Micro Air Vehicle to obtain a relative estimate of the location and motion of all other team members. Furthermore, a collision avoidance controller is proposed based on collision cones. It is designed to deal with the expected performance of the localization scheme by adapting the collision cones during flight and enforcing a clock-wise evasion maneuver. The system was tested with a team of AR-Drones 2.0 flying in a 4m×4m arena. The task requested the AR-Drones to repeatedly fly from wall to wall whilst passing through the center of the arena, hence making collisions highly likely. The system showed promising results. When using two AR-Drones and off-board velocity/orientation estimates, the drones are able to fly around the arena and avoid each other for the entire flight time as permitted by the battery. With three AR-Drones under the same conditions, flight time to collision was 3 minutes. With two AR-Drones flying with on-board velocity estimation, the time to collision was approximately 3 minutes due to the disturbances in velocity estimates. Simulation results show that significantly better results can be expected with smaller units.},

note = {De Croon, G. (mentor)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:9eba1543-6f55-4708-9d68-09446a95d6d4,

title = {Attitude Control of a Small Helicopter UAV using Incremental Nonlinear Dynamic Inversion},

author = {B. J. M. M. Slinger},

url = {http://resolver.tudelft.nl/uuid:9eba1543-6f55-4708-9d68-09446a95d6d4},

year  = {2016},

date = {2016-01-01},

school = {Delft University of Technology},

abstract = {This paper presents an attitude controller for a small helicopter Unmanned Aerial Vehicle (UAV) based on Incremental Nonlinear Dynamic Inversion (INDI). INDI is a sensor-based control method which responds quickly to the commanded input, but also to disturbances. While previous implementations of INDI used a control effectiveness matrix describing effects on rotational accelerations, the implementation presented in this paper uses rotational rates. This is possible with small hingeless-rotor helicopters since the rotational rates are achieved almost immediately, but also the transient is taken into account. By doing so, the matrix contains only constants and the control structure is much simpler. The proposed controller is implemented on a small helicopter which weighs less than 50 grams. The performance of the controller is demonstrated with step responses on roll and heading angles. Also disturbance rejection capabilities are demonstrated. Finally, the controller is deliberately configured incorrectly with wrong control effectiveness and actuator model parameters. A theoretical derivation is provided to predict the effect of incorrect parameters. With experiments, it is demonstrated that the helicopter can be stabilized over a wide range of incorrect values. It is concluded that the demonstrated controller is a suitable choice for small autonomous helicopters.},

note = {Remes, B.D.W. (mentor)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:f72c5d11-d959-4778-831d-2abe07945398,

title = {Visual Homing for Micro Aerial Vehicles using Scene Familiarity},

author = {G. J. J. Van Dalen},

url = {http://resolver.tudelft.nl/uuid:f72c5d11-d959-4778-831d-2abe07945398},

year  = {2016},

date = {2016-01-01},

school = {Delft University of Technology},

abstract = {Autonomous navigation is a major challenge in the development of MAVs. When an algorithm has to be efficient, insect intelligence can be a source of inspiration. An elementary navigation task is homing, which means autonomously returning to the initial location. A promising approach makes use of visual familiarity of a route to determine reference headings during homing. In this thesis an existing biological proof of concept based on desert ants is transferred to MAVs. Vision-in-the-loop experiments in different environments are performed, to investigate the viability of scene familiarity for visual navigation. Trained images are used to determine which control actions to take during homing. To determine familiarity, either a database of stored images is kept or an artificial neural network is used. Different image representations are compared in multiple simulated environments. The use of textons for determining familiarity gives the best performance, but HSV color histograms also perform well and are very efficient. It is concluded that to make this method competitive with other visual navigation approaches, route familiarity should be combined with other methods to improve robustness.},

note = {De Croon, G.C.H.E. (mentor)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:9ad6db51-5d2b-4680-b250-72b03ccc5fbb,

title = {Performance Evaluation in Obstacle Avoidance},

author = {C. W. M. Nous},

url = {http://resolver.tudelft.nl/uuid:9ad6db51-5d2b-4680-b250-72b03ccc5fbb},

year  = {2016},

date = {2016-01-01},

school = {Delft University of Technology},

abstract = {No quantitative procedure currently exists to evaluate the obstacle avoidance capabilities of robotic applications. Such an evaluation method is needed for comparing different methods, but also to determine the operational limits of autonomous systems. This work proposes an evaluation framework which can find such limits. The framework comprises two sets of tests: detection tests and avoidance tests. For each set, environment and performance metrics need to be defined. For detection tests such metrics are well known. For avoidance tests however such metrics are not readily available. Therefore a new set of metrics is proposed. The framework is applied to a UAV that uses stereo vision to detect obstacles and three different algorithms to calculate the avoidance manoeuvre.},

note = {De Croon, G.C.H.E. (mentor)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:f6008da9-d688-4b9f-9880-8d7c3b51a777,

title = {Reinforcement Learning Policy Approximation by Behavior Trees: using Genetic Algoritms},

author = {Y. S. Janssen},

url = {http://resolver.tudelft.nl/uuid:f6008da9-d688-4b9f-9880-8d7c3b51a777},

year  = {2016},

date = {2016-01-01},

school = {Delft University of Technology},

note = {Scheper, K.Y.W. (mentor)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}