2024
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Journal Articles
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Tom van Dijk; Christophe De Wagter; Guido C.H.E. de Croon Visual Route-following for Tiny Autonomous Robots (Journal Article) In: Science Robotics, vol. 9, no. 92, pp. eadk0310, 2024, ISSN: 2470-9476. @article{sr_vr,
title = {Visual Route-following for Tiny Autonomous Robots},
author = {Tom van Dijk and Christophe De Wagter and Guido C.H.E. de Croon},
url = {https://www.science.org/doi/abs/10.1126/scirobotics.adk0310},
doi = {10.1126/scirobotics.adk0310},
issn = {2470-9476},
year = {2024},
date = {2024-07-17},
urldate = {2024-07-17},
journal = {Science Robotics},
volume = {9},
number = {92},
pages = {eadk0310},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Dario Izzo; Emmanuel Blazquez; Robin Ferede; Sebastien Origer; Christophe De Wagter; Guido C. H. E. de Croon Optimality principles in spacecraft neural guidance and control (Journal Article) In: Science Robotics, vol. 9, iss. 91, no. 91, pp. eadi6421, 2024, ISSN: 2470-9476. @article{izzo_sr,
title = {Optimality principles in spacecraft neural guidance and control},
author = {Dario Izzo and Emmanuel Blazquez and Robin Ferede and Sebastien Origer and Christophe De Wagter and Guido C. H. E. de Croon},
doi = {10.1126/scirobotics.adi6421},
issn = {2470-9476},
year = {2024},
date = {2024-06-19},
journal = {Science Robotics},
volume = {9},
number = {91},
issue = {91},
pages = {eadi6421},
abstract = {This Review discusses the main results obtained in training end-to-end neural architectures for guidance and control of interplanetary transfers, planetary landings, and close-proximity operations, highlighting the successful learning of optimality principles by the underlying neural models. Spacecraft and drones aimed at exploring our solar system are designed to operate in conditions where the smart use of onboard resources is vital to the success or failure of the mission. Sensorimotor actions are thus often derived from high-level, quantifiable, optimality principles assigned to each task, using consolidated tools in optimal control theory. The planned actions are derived on the ground and transferred on board, where controllers have the task of tracking the uploaded guidance profile. Here, we review recent trends based on the use of end-to-end networks, called guidance and control networks (G&CNets), which allow spacecraft to depart from such an architecture and to embrace the onboard computation of optimal actions. In this way, the sensor information is transformed in real time into optimal plans, thus increasing mission autonomy and robustness. We then analyze drone racing as an ideal gym environment to test these architectures on real robotic platforms and thus increase confidence in their use in future space exploration missions. Drone racing not only shares with spacecraft missions both limited onboard computational capabilities and similar control structures induced from the optimality principle sought but also entails different levels of uncertainties and unmodeled effects and a very different dynamical timescale. Onboard optimal guidance and control could be entirely neural based in future space missions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This Review discusses the main results obtained in training end-to-end neural architectures for guidance and control of interplanetary transfers, planetary landings, and close-proximity operations, highlighting the successful learning of optimality principles by the underlying neural models. Spacecraft and drones aimed at exploring our solar system are designed to operate in conditions where the smart use of onboard resources is vital to the success or failure of the mission. Sensorimotor actions are thus often derived from high-level, quantifiable, optimality principles assigned to each task, using consolidated tools in optimal control theory. The planned actions are derived on the ground and transferred on board, where controllers have the task of tracking the uploaded guidance profile. Here, we review recent trends based on the use of end-to-end networks, called guidance and control networks (G&CNets), which allow spacecraft to depart from such an architecture and to embrace the onboard computation of optimal actions. In this way, the sensor information is transformed in real time into optimal plans, thus increasing mission autonomy and robustness. We then analyze drone racing as an ideal gym environment to test these architectures on real robotic platforms and thus increase confidence in their use in future space exploration missions. Drone racing not only shares with spacecraft missions both limited onboard computational capabilities and similar control structures induced from the optimality principle sought but also entails different levels of uncertainties and unmodeled effects and a very different dynamical timescale. Onboard optimal guidance and control could be entirely neural based in future space missions. |
Liming Zheng; Salua Hamaza ALBERO: Agile Landing on Branches for Environmental Robotics Operations (Journal Article) In: IEEE Robotics and Automation Letters, vol. 9, no. 3, pp. 2845–2852, 2024, 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{4f72ee48af594442976869c1133ef133,
title = {ALBERO: Agile Landing on Branches for Environmental Robotics Operations},
author = {Liming Zheng and Salua Hamaza},
url = {https://research.tudelft.nl/en/publications/albero-agile-landing-on-branches-for-environmental-robotics-opera},
doi = {10.1109/LRA.2024.3349914},
issn = {2377-3766},
year = {2024},
date = {2024-01-01},
journal = {IEEE Robotics and Automation Letters},
volume = {9},
number = {3},
pages = {2845–2852},
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}
}
|
F. Paredes-Vallés; J. J. Hagenaars; J. Dupeyroux; S. Stroobants; Y. Xu; G. C. H. E. Croon Fully neuromorphic vision and control for autonomous drone flight (Journal Article) In: Science Robotics, vol. 9, no. 90, pp. eadi0591, 2024. @article{<LineBreak>doi:10.1126/scirobotics.adi0591,
title = {Fully neuromorphic vision and control for autonomous drone flight},
author = {F. Paredes-Vallés and J. J. Hagenaars and J. Dupeyroux and S. Stroobants and Y. Xu and G. C. H. E. Croon},
url = {https://www.science.org/doi/abs/10.1126/scirobotics.adi0591},
doi = {10.1126/scirobotics.adi0591},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Science Robotics},
volume = {9},
number = {90},
pages = {eadi0591},
abstract = {Biological sensing and processing is asynchronous and sparse, leading to low-latency and energy-efficient perception and action. In robotics, neuromorphic hardware for event-based vision and spiking neural networks promises to exhibit similar characteristics. However, robotic implementations have been limited to basic tasks with low-dimensional sensory inputs and motor actions because of the restricted network size in current embedded neuromorphic processors and the difficulties of training spiking neural networks. Here, we present a fully neuromorphic vision-to-control pipeline for controlling a flying drone. Specifically, we trained a spiking neural network that accepts raw event-based camera data and outputs low-level control actions for performing autonomous vision-based flight. The vision part of the network, consisting of five layers and 28,800 neurons, maps incoming raw events to ego-motion estimates and was trained with self-supervised learning on real event data. The control part consists of a single decoding layer and was learned with an evolutionary algorithm in a drone simulator. Robotic experiments show a successful sim-to-real transfer of the fully learned neuromorphic pipeline. The drone could accurately control its ego-motion, allowing for hovering, landing, and maneuvering sideways—even while yawing at the same time. The neuromorphic pipeline runs on board on Intel’s Loihi neuromorphic processor with an execution frequency of 200 hertz, consuming 0.94 watt of idle power and a mere additional 7 to 12 milliwatts when running the network. These results illustrate the potential of neuromorphic sensing and processing for enabling insect-sized intelligent robots. A fully neuromorphic vision-to-control pipeline enables fast and energy-efficient ego-motion control of a flying drone. Despite the ability of visual processing enabled by artificial neural networks, the associated hardware and large power consumption limit deployment on small flying drones. Neuromorphic hardware offers a promising alternative, but the accompanying spiking neural networks are difficult to train, and the current hardware only supports a limited number of neurons. Paredes-Vallés et al. now present a neuromorphic pipeline to control drone flight. They trained a five-layer spiking neural network to process the raw inputs from an event camera. The network first estimated ego-motion and subsequently determined low-level control commands. Real-world experiments demonstrated that the drone could control its ego-motion to land, hover, and maneuver sideways, with minimal power consumption. —Melisa Yashinski},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Biological sensing and processing is asynchronous and sparse, leading to low-latency and energy-efficient perception and action. In robotics, neuromorphic hardware for event-based vision and spiking neural networks promises to exhibit similar characteristics. However, robotic implementations have been limited to basic tasks with low-dimensional sensory inputs and motor actions because of the restricted network size in current embedded neuromorphic processors and the difficulties of training spiking neural networks. Here, we present a fully neuromorphic vision-to-control pipeline for controlling a flying drone. Specifically, we trained a spiking neural network that accepts raw event-based camera data and outputs low-level control actions for performing autonomous vision-based flight. The vision part of the network, consisting of five layers and 28,800 neurons, maps incoming raw events to ego-motion estimates and was trained with self-supervised learning on real event data. The control part consists of a single decoding layer and was learned with an evolutionary algorithm in a drone simulator. Robotic experiments show a successful sim-to-real transfer of the fully learned neuromorphic pipeline. The drone could accurately control its ego-motion, allowing for hovering, landing, and maneuvering sideways—even while yawing at the same time. The neuromorphic pipeline runs on board on Intel’s Loihi neuromorphic processor with an execution frequency of 200 hertz, consuming 0.94 watt of idle power and a mere additional 7 to 12 milliwatts when running the network. These results illustrate the potential of neuromorphic sensing and processing for enabling insect-sized intelligent robots. A fully neuromorphic vision-to-control pipeline enables fast and energy-efficient ego-motion control of a flying drone. Despite the ability of visual processing enabled by artificial neural networks, the associated hardware and large power consumption limit deployment on small flying drones. Neuromorphic hardware offers a promising alternative, but the accompanying spiking neural networks are difficult to train, and the current hardware only supports a limited number of neurons. Paredes-Vallés et al. now present a neuromorphic pipeline to control drone flight. They trained a five-layer spiking neural network to process the raw inputs from an event camera. The network first estimated ego-motion and subsequently determined low-level control commands. Real-world experiments demonstrated that the drone could control its ego-motion to land, hover, and maneuver sideways, with minimal power consumption. —Melisa Yashinski |
Robin Ferede; Guido Croon; Christophe De Wagter; Dario Izzo End-to-end neural network based optimal quadcopter control (Journal Article) In: Robotics and Autonomous Systems, vol. 172, 2024, ISSN: 0921-8890, (Funding Information: This work was supported by the European Space Agency.This research was co-funded under the Discovery programme of, and funded by, the European Space Agency. Funding Information: This work was supported by the European Space Agency . Publisher Copyright: © 2023 The Authors). @article{16169a19bf6b46818ecc18a9f2bd5e0f,
title = {End-to-end neural network based optimal quadcopter control},
author = {Robin Ferede and Guido Croon and Christophe De Wagter and Dario Izzo},
url = {https://research.tudelft.nl/en/publications/end-to-end-neural-network-based-optimal-quadcopter-control},
doi = {10.1016/j.robot.2023.104588},
issn = {0921-8890},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Robotics and Autonomous Systems},
volume = {172},
publisher = {Elsevier},
note = {Funding Information: This work was supported by the European Space Agency.This research was co-funded under the Discovery programme of, and funded by, the European Space Agency. Funding Information: This work was supported by the European Space Agency . Publisher Copyright: © 2023 The Authors},
keywords = {},
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tppubtype = {article}
}
|
Proceedings Articles
|
S. Wang; M. Hoed; S. Hamaza A Low-cost Fabrication Approach to Embody Flexible and Lightweight Strain Sensing on Flapping Wings (Proceedings Article) In: IEEE ICRA 2024 - Workshop on Bioinspired, Soft, and Other Novel Design Paradigms for Aerial Robotics, IEEE, United States, 2024, (2024 IEEE International Conference on<br/>Robotics and Automation, ICRA 2024 ; Conference date: 13-05-2024 Through 17-05-2024). @inproceedings{cdeff09dcd0048b7b12f16a07f6d2751,
title = {A Low-cost Fabrication Approach to Embody Flexible and Lightweight Strain Sensing on Flapping Wings},
author = {S. Wang and M. Hoed and S. Hamaza},
url = {https://research.tudelft.nl/en/publications/a-low-cost-fabrication-approach-to-embody-flexible-and-lightweigh},
year = {2024},
date = {2024-01-01},
booktitle = {IEEE ICRA 2024 - Workshop on Bioinspired, Soft, and Other Novel Design Paradigms for Aerial Robotics},
publisher = {IEEE},
address = {United States},
note = {2024 IEEE International Conference on<br/>Robotics and Automation, ICRA 2024 ; Conference date: 13-05-2024 Through 17-05-2024},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Masters Theses
|
Noah Stam Adaptive dynamic incremental nonlinear control allocation: An actuator fault-tolerant control solution for high-performance aircraft (Masters Thesis) TU Delft Aerospace Engineering, 2024, (de Visser, C.C. (mentor); Smeur, E.J.J. (graduation committee); Mooij, E. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:bd671c3b-afc3-4215-a724-dd69512f4715,
title = {Adaptive dynamic incremental nonlinear control allocation: An actuator fault-tolerant control solution for high-performance aircraft},
author = {Noah Stam},
url = {http://resolver.tudelft.nl/uuid:bd671c3b-afc3-4215-a724-dd69512f4715},
year = {2024},
date = {2024-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Neglecting actuator dynamics in nonlinear control and control allocation can lead to performance degradation, especially when considering fast dynamic systems. This thesis provides a novel method to account for actuator dynamics in the control allocation solution, dynamic incremental nonlinear control allocation, or D-INCA. The incremental approach allows for the implementation of a first order discrete-time actuator dynamics model in the quadratic programming (QP) solver. This model is used to find the optimal command inputs in addition to the desired physical actuator deflections, hereby compensating for actuator dynamics delays. Whereas, the baseline incremental nonlinear control allocation (INCA) approach requires pseudo-control hedging of the outer loop reference to increase closed loop stability margins under actuator dynamics delays. To its advantage, D-INCA does not require feedback of higher order output derivatives than INCA and can be used with nonlinear non-control affine systems. Furthermore, with adaptive D-INCA, or AD-INCA, an actuator dynamics parameter estimator is introduced to adapt the actuator model online, minimizing actuator tracking errors after actuator failures. The proposed methods are applied to a fighter aircraft model with an over-actuated innovative control effectors suite and results are compared to the baseline INCA controller.},
note = {de Visser, C.C. (mentor); Smeur, E.J.J. (graduation committee); Mooij, E. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Neglecting actuator dynamics in nonlinear control and control allocation can lead to performance degradation, especially when considering fast dynamic systems. This thesis provides a novel method to account for actuator dynamics in the control allocation solution, dynamic incremental nonlinear control allocation, or D-INCA. The incremental approach allows for the implementation of a first order discrete-time actuator dynamics model in the quadratic programming (QP) solver. This model is used to find the optimal command inputs in addition to the desired physical actuator deflections, hereby compensating for actuator dynamics delays. Whereas, the baseline incremental nonlinear control allocation (INCA) approach requires pseudo-control hedging of the outer loop reference to increase closed loop stability margins under actuator dynamics delays. To its advantage, D-INCA does not require feedback of higher order output derivatives than INCA and can be used with nonlinear non-control affine systems. Furthermore, with adaptive D-INCA, or AD-INCA, an actuator dynamics parameter estimator is introduced to adapt the actuator model online, minimizing actuator tracking errors after actuator failures. The proposed methods are applied to a fighter aircraft model with an over-actuated innovative control effectors suite and results are compared to the baseline INCA controller. |
Francesco Branca Optical Flow Determination using Neuromorphic Hardware with Integrate & Fire Neurons (Masters Thesis) TU Delft Aerospace Engineering, 2024, (de Croon, G.C.H.E. (mentor); Hagenaars, J.J. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:b4f643b2-a64f-4fb4-a18f-5012364f7b0f,
title = {Optical Flow Determination using Neuromorphic Hardware with Integrate & Fire Neurons},
author = {Francesco Branca},
url = {http://resolver.tudelft.nl/uuid:b4f643b2-a64f-4fb4-a18f-5012364f7b0f},
year = {2024},
date = {2024-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Spiking neural networks implemented for sensing and control of robots have the potential to achieve lower latency and power consumption by processing information sparsely and asynchronously. They have been used on neuromorphic devices to estimate optical flow for micro air vehicles navigation, however robotic implementations have been limited to hardware setups with sensing and processing as separate systems. This article investigates a new approach for training a spiking neural network for optical flow to be deployed on the speck2e device from Synsense. The method takes into account the restrictions of the speck2e in terms of network architecture, neuron model, and number of synaptic operations and it involves training a recurrent neural network with ReLU activation functions, which is subsequently converted into a spiking network. A system of weight rescaling is applied after conversion, to ensure optimal information flow between the layers. Our study shows that it is possible to estimate optical flow with Integrate-and-Fire neurons. However, currently, the optical flow estimation performance is still hampered by the number of synaptic operations. As a result, the network presented in this work is able to estimate optical flow in a range of [-4, 1] pixel/s.},
note = {de Croon, G.C.H.E. (mentor); Hagenaars, J.J. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Spiking neural networks implemented for sensing and control of robots have the potential to achieve lower latency and power consumption by processing information sparsely and asynchronously. They have been used on neuromorphic devices to estimate optical flow for micro air vehicles navigation, however robotic implementations have been limited to hardware setups with sensing and processing as separate systems. This article investigates a new approach for training a spiking neural network for optical flow to be deployed on the speck2e device from Synsense. The method takes into account the restrictions of the speck2e in terms of network architecture, neuron model, and number of synaptic operations and it involves training a recurrent neural network with ReLU activation functions, which is subsequently converted into a spiking network. A system of weight rescaling is applied after conversion, to ensure optimal information flow between the layers. Our study shows that it is possible to estimate optical flow with Integrate-and-Fire neurons. However, currently, the optical flow estimation performance is still hampered by the number of synaptic operations. As a result, the network presented in this work is able to estimate optical flow in a range of [-4, 1] pixel/s. |
Lyana Usa Novel Neuromorphic Hardware Inspired by the Olfactory Pathway Model of the Drosophila: Leveraging bio-plausible computational primitives in digital circuits for spatio-temporal processing (Masters Thesis) TU Delft Electrical Engineering, Mathematics and Computer Science, 2024, (Frenkel, C. (mentor); Makinwa, K.A.A. (graduation committee); de Croon, G.C.H.E. (graduation committee); Nawrot, M. P. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:6a74fb80-425c-4366-8110-fecfb4a1a5fc,
title = {Novel Neuromorphic Hardware Inspired by the Olfactory Pathway Model of the \textit{Drosophila}: Leveraging bio-plausible computational primitives in digital circuits for spatio-temporal processing},
author = {Lyana Usa},
url = {http://resolver.tudelft.nl/uuid:6a74fb80-425c-4366-8110-fecfb4a1a5fc},
year = {2024},
date = {2024-01-01},
school = {TU Delft Electrical Engineering, Mathematics and Computer Science},
abstract = {Olfactory learning in \textit{Drosophila }larvae exemplifies efficient neural processing in a small-scale network with minimal power consumption. This system enables larvae to anticipate important outcomes based on new and familiar odor stimuli, a process crucial for survival and adaptation. Central to this learning mechanism is the olfactory pathway model, which embodies the principles of synaptic plasticity and associative learning through prediction error coding mediated by specific neuromodulating neurons in the mushroom body, like dopaminergic neurons. There is a pressing need to develop novel computational frameworks that capture the spatio-temporal processes while remaining compatible with the constraints of small-scale neural networks. These frameworks should draw inspiration from the biophysical properties of neurons within the olfactory pathway model, enabling accurate emulation of neural dynamics and efficient learning processes using spiking neural networks. This thesis proposes a framework based on a phenomenological conductance-based leaky integrate-and-fire (COBALIF) neuron model, inspired by the olfactory pathway model of \textit{Drosophila} larvae. By first prototyping the spiking neural network in Intel's Lava Python-based framework, we validated the design on a neuron and system level for a neuromorphic hardware implementation. This was the foundation of a programmable, neuromorphic FPGA architecture capable of adaptive optimization, employed on a Zynq 7000 SoC FPGA. By implementing this architecture in a single-precision floating-point format, we model the real-time neural dynamics of the COBALIF neuron in one-tenth of a millisecond precision. Moreover, our FPGA implementation serves as a feasible prototype for deploying such biologically inspired neurons and their spatio-temporal dependencies in digital design, paving the way for scaling up to small-scale networks.},
note = {Frenkel, C. (mentor); Makinwa, K.A.A. (graduation committee); de Croon, G.C.H.E. (graduation committee); Nawrot, M. P. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Olfactory learning in Drosophila larvae exemplifies efficient neural processing in a small-scale network with minimal power consumption. This system enables larvae to anticipate important outcomes based on new and familiar odor stimuli, a process crucial for survival and adaptation. Central to this learning mechanism is the olfactory pathway model, which embodies the principles of synaptic plasticity and associative learning through prediction error coding mediated by specific neuromodulating neurons in the mushroom body, like dopaminergic neurons. There is a pressing need to develop novel computational frameworks that capture the spatio-temporal processes while remaining compatible with the constraints of small-scale neural networks. These frameworks should draw inspiration from the biophysical properties of neurons within the olfactory pathway model, enabling accurate emulation of neural dynamics and efficient learning processes using spiking neural networks. This thesis proposes a framework based on a phenomenological conductance-based leaky integrate-and-fire (COBALIF) neuron model, inspired by the olfactory pathway model of Drosophila larvae. By first prototyping the spiking neural network in Intel's Lava Python-based framework, we validated the design on a neuron and system level for a neuromorphic hardware implementation. This was the foundation of a programmable, neuromorphic FPGA architecture capable of adaptive optimization, employed on a Zynq 7000 SoC FPGA. By implementing this architecture in a single-precision floating-point format, we model the real-time neural dynamics of the COBALIF neuron in one-tenth of a millisecond precision. Moreover, our FPGA implementation serves as a feasible prototype for deploying such biologically inspired neurons and their spatio-temporal dependencies in digital design, paving the way for scaling up to small-scale networks. |
Luke Waal Towards a Robust Wireless Real-Time Ecological Monitoring System (Masters Thesis) TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Aerospace Engineering, 2024, (Hamaza, S. (mentor); Rajan, R.T. (mentor); Smeur, E.J.J. (graduation committee); Hendriks, R.C. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:491a7744-dc49-405c-ba38-5198b3e839a8,
title = {Towards a Robust Wireless Real-Time Ecological Monitoring System},
author = {Luke Waal},
url = {http://resolver.tudelft.nl/uuid:491a7744-dc49-405c-ba38-5198b3e839a8},
year = {2024},
date = {2024-01-01},
school = {TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Aerospace Engineering},
abstract = {Climate change poses a serious threat to ecosystems and increases the need for accurate and rigorous monitoring of ecosystems. Current monitoring solutions are often bulky, expensive, and lack critical functionalities such as on-board inference capabilities, robust wireless connections, and a diverse sensor suite. Ecological monitoring projects often suffer from inefficiencies caused by the large time delays between collecting data and analyzing said data, as well as having to spend large amounts of time in the field setting up the sensors manually. This thesis addresses many of these issues by designing a sensor with an extensive sensor suite, robust wireless capabilities and an on-board audio classifier able to perform real-time inference. Furthermore, attention is paid to making the system extendable in the future and allow for potentially integrating the sensors with a drone delivery- and retrieval system. The system tests performed indicate that the system has great potential given more time to tweak some of its identified shortcomings.},
note = {Hamaza, S. (mentor); Rajan, R.T. (mentor); Smeur, E.J.J. (graduation committee); Hendriks, R.C. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Climate change poses a serious threat to ecosystems and increases the need for accurate and rigorous monitoring of ecosystems. Current monitoring solutions are often bulky, expensive, and lack critical functionalities such as on-board inference capabilities, robust wireless connections, and a diverse sensor suite. Ecological monitoring projects often suffer from inefficiencies caused by the large time delays between collecting data and analyzing said data, as well as having to spend large amounts of time in the field setting up the sensors manually. This thesis addresses many of these issues by designing a sensor with an extensive sensor suite, robust wireless capabilities and an on-board audio classifier able to perform real-time inference. Furthermore, attention is paid to making the system extendable in the future and allow for potentially integrating the sensors with a drone delivery- and retrieval system. The system tests performed indicate that the system has great potential given more time to tweak some of its identified shortcomings. |
Noah Wechtler Implications of Propeller-Wing Interactions on the Control of Aerodynamic-Surface-Free Tilt-Rotor Quad-Planes (Masters Thesis) TU Delft Aerospace Engineering, 2024, (Smeur, E.J.J. (mentor); Mancinelli, A. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:3193131c-6b68-46a2-afe1-964a044dd6f9,
title = {Implications of Propeller-Wing Interactions on the Control of Aerodynamic-Surface-Free Tilt-Rotor Quad-Planes},
author = {Noah Wechtler},
url = {http://resolver.tudelft.nl/uuid:3193131c-6b68-46a2-afe1-964a044dd6f9},
year = {2024},
date = {2024-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Quad-planes are a type of vehicle which combine the hovering capability of quadcopters and the forward flight efficiency of winged aircraft. Flight tests conducted on a dual-axis tilting-rotor quad-plane, designed to fly without aerodynamic surfaces, observed that the quad-plane suffered from insufficient roll authority during fast, forward flight. Subsequent wind tunnel testing confirmed a two- to fourfold reduction in roll moment generation from propellers mounted in front of the wing at similar levels of tilt as their rear counterparts, caused by propeller-wing interactions. To address the mismatch in actuator effectiveness shown by the wind tunnel experiment, the effect of the propeller-wing interactions was incorporated into the aero-propulsive model by means of a global polynomial, the structure of which was found using multivariate orthogonal function modelling. New flight tests demonstrated that, by including the propeller-wing interactions in the control allocation, the vehicle is capable of tracking a figure 8 maneuver without aerodynamic surfaces, and without compromising tracking performance.},
note = {Smeur, E.J.J. (mentor); Mancinelli, A. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Quad-planes are a type of vehicle which combine the hovering capability of quadcopters and the forward flight efficiency of winged aircraft. Flight tests conducted on a dual-axis tilting-rotor quad-plane, designed to fly without aerodynamic surfaces, observed that the quad-plane suffered from insufficient roll authority during fast, forward flight. Subsequent wind tunnel testing confirmed a two- to fourfold reduction in roll moment generation from propellers mounted in front of the wing at similar levels of tilt as their rear counterparts, caused by propeller-wing interactions. To address the mismatch in actuator effectiveness shown by the wind tunnel experiment, the effect of the propeller-wing interactions was incorporated into the aero-propulsive model by means of a global polynomial, the structure of which was found using multivariate orthogonal function modelling. New flight tests demonstrated that, by including the propeller-wing interactions in the control allocation, the vehicle is capable of tracking a figure 8 maneuver without aerodynamic surfaces, and without compromising tracking performance. |
Aleksandar Shokolarov Self-Supervised Learning of Event-Based Optical Flow via Deep Equilibrium Models (Masters Thesis) TU Delft Aerospace Engineering, 2024, (de Croon, G.C.H.E. (graduation committee); Wu, Y. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:eb522c6b-1b1d-4988-8a7a-e2846dc697c5,
title = {Self-Supervised Learning of Event-Based Optical Flow via Deep Equilibrium Models},
author = {Aleksandar Shokolarov},
url = {http://resolver.tudelft.nl/uuid:eb522c6b-1b1d-4988-8a7a-e2846dc697c5},
year = {2024},
date = {2024-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {The estimation of optical flow, which determines the movement of objects in a visual scene, is a crucial problem in computer vision. It is essential for applications such as autonomous navigation, where precise motion estimation is critical for performance and safety.<br/><br/>Frame-based cameras capture sequences of still images at regular intervals, from which optical flow is traditionally extracted using optimization-based or learning-based methods. Recently, event-based cameras, which detect changes in pixel brightness asynchronously, have gained traction due to their high temporal resolution and robustness to motion blur, and many algorithms have been developed to estimate optical flow from this data. IDNet is a learning-based approach that achieves state-of-the-art performance. However, IDNet and similar models face two major challenges: they require labeled ground-truth data for training, which is scarce and difficult to collect, and they rely on recurrent neural networks (RNNs) with a fixed number of refinement iterations. This fixed iteration scheme does not adapt to scene complexity, limiting accuracy for complex flows and increasing computational effort for simpler patterns.<br/><br/>The aim of this project is to explore, implement, and evaluate potential methods to address these two mentioned limitations and enhance the capabilities of models like IDNet.<br/><br/>To remove the need for ground-truth data, a self-supervised learning paradigm was implemented by introducing a novel contrast maximization loss that assesses the blur present when accumulating raw events for a certain time interval and compensating it with the predicted flow. To assess the effectiveness of this method, models were trained on the benchmark MVSEC dataset, showing improved results over previous methods with up to 15% on some sequences and an 8% improvement on average. Based on these experiments and results, further research directions were proposed.<br/><br/>As for the problem of the current fixed iteration scheme, Deep Equilibrium Models were found to provide a promising pathway to solving it. These novel models reformulate their iterative structure into a root-finding problem and utilize traditional solvers to find a solution based on some tolerance, providing a trade-off between speed and accuracy. Moreover, they allow for direct differentiation through the network using only their final estimate, compared to previous methods that keep track of their state through all iterations, leading to an O(1) memory consumption. Implementing these and some additional ideas, the trained DEQ IDNet model reached competitive performance on the DSEC dataset while consuming 15% less memory. Yet, further work is needed to close the gap and achieve state-of-the-art performance.},
note = {de Croon, G.C.H.E. (graduation committee); Wu, Y. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
The estimation of optical flow, which determines the movement of objects in a visual scene, is a crucial problem in computer vision. It is essential for applications such as autonomous navigation, where precise motion estimation is critical for performance and safety.<br/><br/>Frame-based cameras capture sequences of still images at regular intervals, from which optical flow is traditionally extracted using optimization-based or learning-based methods. Recently, event-based cameras, which detect changes in pixel brightness asynchronously, have gained traction due to their high temporal resolution and robustness to motion blur, and many algorithms have been developed to estimate optical flow from this data. IDNet is a learning-based approach that achieves state-of-the-art performance. However, IDNet and similar models face two major challenges: they require labeled ground-truth data for training, which is scarce and difficult to collect, and they rely on recurrent neural networks (RNNs) with a fixed number of refinement iterations. This fixed iteration scheme does not adapt to scene complexity, limiting accuracy for complex flows and increasing computational effort for simpler patterns.<br/><br/>The aim of this project is to explore, implement, and evaluate potential methods to address these two mentioned limitations and enhance the capabilities of models like IDNet.<br/><br/>To remove the need for ground-truth data, a self-supervised learning paradigm was implemented by introducing a novel contrast maximization loss that assesses the blur present when accumulating raw events for a certain time interval and compensating it with the predicted flow. To assess the effectiveness of this method, models were trained on the benchmark MVSEC dataset, showing improved results over previous methods with up to 15% on some sequences and an 8% improvement on average. Based on these experiments and results, further research directions were proposed.<br/><br/>As for the problem of the current fixed iteration scheme, Deep Equilibrium Models were found to provide a promising pathway to solving it. These novel models reformulate their iterative structure into a root-finding problem and utilize traditional solvers to find a solution based on some tolerance, providing a trade-off between speed and accuracy. Moreover, they allow for direct differentiation through the network using only their final estimate, compared to previous methods that keep track of their state through all iterations, leading to an O(1) memory consumption. Implementing these and some additional ideas, the trained DEQ IDNet model reached competitive performance on the DSEC dataset while consuming 15% less memory. Yet, further work is needed to close the gap and achieve state-of-the-art performance. |
Sander Hazelaar Adaptive Visual Servoing Control for Quadrotors: A Bio-inspired Strategy Using Active Vision (Masters Thesis) TU Delft Aerospace Engineering, 2024, (de Croon, G.C.H.E. (mentor); Yedutenko, M. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:9ab2b4ba-8f91-4891-8190-4a96f77c471e,
title = {Adaptive Visual Servoing Control for Quadrotors: A Bio-inspired Strategy Using Active Vision},
author = {Sander Hazelaar},
url = {http://resolver.tudelft.nl/uuid:9ab2b4ba-8f91-4891-8190-4a96f77c471e},
year = {2024},
date = {2024-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {New insights into the landing behavior of bumblebees show an adaptive strategy where the optical flow expansion of the landing target is step-wise regulated. In this article, the potential benefits of this approach are studied by replicating the landing experiment with a quadrotor. To this end, an open-loop switching method is developed, enabling fast steps in divergence. An adaptive control law is used to deal with non-linear system dynamics, where the control gain is scheduled based on the control effectiveness of the actuator inputs during the steps. It is demonstrated that the quadrotor can reliably land on the target from varying initial positions, and the switching strategy shows a slight reduction in landing time compared to a constant divergence strategy with the same average divergence over distance. This strategy also reduces the maximum velocity during the landing.},
note = {de Croon, G.C.H.E. (mentor); Yedutenko, M. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
New insights into the landing behavior of bumblebees show an adaptive strategy where the optical flow expansion of the landing target is step-wise regulated. In this article, the potential benefits of this approach are studied by replicating the landing experiment with a quadrotor. To this end, an open-loop switching method is developed, enabling fast steps in divergence. An adaptive control law is used to deal with non-linear system dynamics, where the control gain is scheduled based on the control effectiveness of the actuator inputs during the steps. It is demonstrated that the quadrotor can reliably land on the target from varying initial positions, and the switching strategy shows a slight reduction in landing time compared to a constant divergence strategy with the same average divergence over distance. This strategy also reduces the maximum velocity during the landing. |
Alexander Bom Design of an inherently fully dynamically balanced aerial manipulator with omnidirectional workspace (Masters Thesis) TU Delft Mechanical, Maritime and Materials Engineering, 2024, (van der Wijk, V. (mentor); Hamaza, S. (mentor); Herder, J.L. (graduation committee); Goosen, J.F.L. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:9a295d44-1e95-4911-a4a2-4a96c498fe79,
title = {Design of an inherently fully dynamically balanced aerial manipulator with omnidirectional workspace},
author = {Alexander Bom},
url = {http://resolver.tudelft.nl/uuid:9a295d44-1e95-4911-a4a2-4a96c498fe79},
year = {2024},
date = {2024-01-01},
school = {TU Delft Mechanical, Maritime and Materials Engineering},
abstract = {Drones are increasingly used nowadays, primarily for visual inspection tasks facilitated by onboard cameras. The field of aerial manipulation tries to expand the capabilities of drones by attaching a manipulator, enabling physical interaction. Unfortunately, the usability of aerial manipulators is hindered by disturbances resulting from the movements of the manipulator. These disturbances, including reaction forces and a shifting centre of mass, not only affect manipulation accuracy but also pose safety risks by potentially destabilizing the drone. In this thesis, a design is presented that addresses this challenge by leveraging the theory of dynamic balance. <br/>A new design approach of making a manipulator fly, instead of the common approach of mounting a manipulator arm to a drone was used. This new approach avoids interference with the drone's components, allowing to focus on the design of the manipulator arm. Furthermore, it made it possible to create a manipulator which can manipulate above, to the side and underneath itself. This makes the presented manipulator arm more versatile than common aerial manipulators whose workspace is mostly located only above or below the drone. The kinematics, workspace and balance conditions of the manipulator arm are presented. Furthermore, the design's workspace is optimised while the mass of the manipulator is minimized in a bilevel optimisation. Finally, the design is validated both by simulation and measurements performed with the built prototype.<br/>The design presented is the first inherently fully dynamically balanced manipulator with omnidirectional workspace which can be used for aerial manipulation.<br},
note = {van der Wijk, V. (mentor); Hamaza, S. (mentor); Herder, J.L. (graduation committee); Goosen, J.F.L. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Drones are increasingly used nowadays, primarily for visual inspection tasks facilitated by onboard cameras. The field of aerial manipulation tries to expand the capabilities of drones by attaching a manipulator, enabling physical interaction. Unfortunately, the usability of aerial manipulators is hindered by disturbances resulting from the movements of the manipulator. These disturbances, including reaction forces and a shifting centre of mass, not only affect manipulation accuracy but also pose safety risks by potentially destabilizing the drone. In this thesis, a design is presented that addresses this challenge by leveraging the theory of dynamic balance. <br/>A new design approach of making a manipulator fly, instead of the common approach of mounting a manipulator arm to a drone was used. This new approach avoids interference with the drone's components, allowing to focus on the design of the manipulator arm. Furthermore, it made it possible to create a manipulator which can manipulate above, to the side and underneath itself. This makes the presented manipulator arm more versatile than common aerial manipulators whose workspace is mostly located only above or below the drone. The kinematics, workspace and balance conditions of the manipulator arm are presented. Furthermore, the design's workspace is optimised while the mass of the manipulator is minimized in a bilevel optimisation. Finally, the design is validated both by simulation and measurements performed with the built prototype.<br/>The design presented is the first inherently fully dynamically balanced manipulator with omnidirectional workspace which can be used for aerial manipulation.<br |
Andreas Zwanenburg A lightweight quadrotor autonomy system: To navigate in densely cluttered forest environments (Masters Thesis) TU Delft Mechanical, Maritime and Materials Engineering, 2024, (Wisse, M. (mentor); Hamaza, S. (graduation committee); Benders, D. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:a164abc6-0103-4fa0-b7ac-c15bce2bce64,
title = {A lightweight quadrotor autonomy system: To navigate in densely cluttered forest environments},
author = {Andreas Zwanenburg},
url = {http://resolver.tudelft.nl/uuid:a164abc6-0103-4fa0-b7ac-c15bce2bce64},
year = {2024},
date = {2024-01-01},
school = {TU Delft Mechanical, Maritime and Materials Engineering},
abstract = {These days, people see more and more applications for drones, including monitoring rainforests to protect plant and animal species. However, drones face challenges when navigating through the dense and cluttered vegetation of the forest. These environments necessitate advanced autonomous detection and navigation to make the drone traverse robustly and fly safely. In addition, the forest brings extra challenges, such as blocked signals for GPS localisation, remote control, and remote supervising.<br/><br/>In this thesis project, a drone is designed, built, and programmed to navigate autonomously in the rainforest with complete onboard computing and no GPS localisation. This 500-gram drone is being extensively tested and optimized in real forest conditions, and a dataset is being created from its autonomous flights to simulate various configurations of the path-planning algorithm. The results of these simulations on this dataset are then used for thorough research on how the algorithm can downscale to smaller systems and how this affects performance.<br/><br/>By using the results of this research on downscaling, a 100-gram drone is built and programmed to fly in forest conditions with complete onboard computation. Challenging on this small-size drone is the use of low-quality lightweight sensors and processor. The processor only weighs 10 grams, and the depth camera weighs 8 grams. Unique on this small drone is the 3D path planning fully computed onboard and the implementation of a new type of depth camera.},
note = {Wisse, M. (mentor); Hamaza, S. (graduation committee); Benders, D. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
These days, people see more and more applications for drones, including monitoring rainforests to protect plant and animal species. However, drones face challenges when navigating through the dense and cluttered vegetation of the forest. These environments necessitate advanced autonomous detection and navigation to make the drone traverse robustly and fly safely. In addition, the forest brings extra challenges, such as blocked signals for GPS localisation, remote control, and remote supervising.<br/><br/>In this thesis project, a drone is designed, built, and programmed to navigate autonomously in the rainforest with complete onboard computing and no GPS localisation. This 500-gram drone is being extensively tested and optimized in real forest conditions, and a dataset is being created from its autonomous flights to simulate various configurations of the path-planning algorithm. The results of these simulations on this dataset are then used for thorough research on how the algorithm can downscale to smaller systems and how this affects performance.<br/><br/>By using the results of this research on downscaling, a 100-gram drone is built and programmed to fly in forest conditions with complete onboard computation. Challenging on this small-size drone is the use of low-quality lightweight sensors and processor. The processor only weighs 10 grams, and the depth camera weighs 8 grams. Unique on this small drone is the 3D path planning fully computed onboard and the implementation of a new type of depth camera. |
Miscellaneous
|
Michiel Firlefyn; Jesse Hagenaars; Guido de Croon Direct learning of home vector direction for insect-inspired robot navigation (Miscellaneous) 2024. @misc{firlefyn2024,
title = {Direct learning of home vector direction for insect-inspired robot navigation},
author = {Michiel Firlefyn and Jesse Hagenaars and Guido de Croon},
url = {https://arxiv.org/abs/2405.03827},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Changrui Liu; Sven U. Pfeiffer; Guido C. H. E. Croon Cooperative Relative Localization in MAV Swarms with Ultra-wideband Ranging (Miscellaneous) 2024. @misc{2405.18234,
title = {Cooperative Relative Localization in MAV Swarms with Ultra-wideband Ranging},
author = {Changrui Liu and Sven U. Pfeiffer and Guido C. H. E. Croon},
url = {https://arxiv.org/abs/2405.18234},
year = {2024},
date = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Matthew Yedutenko; Federico Paredes-Valles; Lyes Khacef; Guido C. H. E. De Croon TDE-3: An improved prior for optical flow computation in spiking neural networks (Miscellaneous) 2024. @misc{2402.11662,
title = {TDE-3: An improved prior for optical flow computation in spiking neural networks},
author = {Matthew Yedutenko and Federico Paredes-Valles and Lyes Khacef and Guido C. H. E. De Croon},
url = {https://arxiv.org/abs/2402.11662},
year = {2024},
date = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Hang Yu; Christophe De Wagter; Guido C. H. E Croon MAVRL: Learn to Fly in Cluttered Environments with Varying Speed (Miscellaneous) 2024. @misc{2402.08381,
title = {MAVRL: Learn to Fly in Cluttered Environments with Varying Speed},
author = {Hang Yu and Christophe De Wagter and Guido C. H. E Croon},
url = {https://arxiv.org/abs/2402.08381},
year = {2024},
date = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Marija Popovic; Joshua Ott; Julius Rückin; Mykel J. Kochendorfer Robotic Learning for Adaptive Informative Path Planning (Miscellaneous) 2024. @misc{2404.06940,
title = {Robotic Learning for Adaptive Informative Path Planning},
author = {Marija Popovic and Joshua Ott and Julius Rückin and Mykel J. Kochendorfer},
url = {https://arxiv.org/abs/2404.06940},
year = {2024},
date = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Sicong Pan; Liren Jin; Xuying Huang; Cyrill Stachniss; Marija Popović; Maren Bennewitz Exploiting Priors from 3D Diffusion Models for RGB-Based One-Shot View Planning (Miscellaneous) 2024. @misc{2403.16803,
title = {Exploiting Priors from 3D Diffusion Models for RGB-Based One-Shot View Planning},
author = {Sicong Pan and Liren Jin and Xuying Huang and Cyrill Stachniss and Marija Popović and Maren Bennewitz},
url = {https://arxiv.org/abs/2403.16803},
year = {2024},
date = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Liren Jin; Haofei Kuang; Yue Pan; Cyrill Stachniss; Marija Popović STAIR: Semantic-Targeted Active Implicit Reconstruction (Miscellaneous) 2024. @misc{2403.11233,
title = {STAIR: Semantic-Targeted Active Implicit Reconstruction},
author = {Liren Jin and Haofei Kuang and Yue Pan and Cyrill Stachniss and Marija Popović},
url = {https://arxiv.org/abs/2403.11233},
year = {2024},
date = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Apoorva Vashisth; Julius Rückin; Federico Magistri; Cyrill Stachniss; Marija Popović Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative Path Planning (Miscellaneous) 2024. @misc{2402.04894,
title = {Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative Path Planning},
author = {Apoorva Vashisth and Julius Rückin and Federico Magistri and Cyrill Stachniss and Marija Popović},
url = {https://arxiv.org/abs/2402.04894},
year = {2024},
date = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Online
|
Till Blaha; Ewoud J. J. Smeur; Bart D. W. Remes Flight Data of A Quadrotor Launched in the Air while Learning its own Flight Model and Controller (Online) 2024, visited: 05.09.2024. @online{blaha_flight_data_2024,
title = {Flight Data of A Quadrotor Launched in the Air while Learning its own Flight Model and Controller},
author = {Till Blaha and Ewoud J. J. Smeur and Bart D. W. Remes},
url = {https://data.4tu.nl/datasets/0530be90-cc6c-4029-9774-670657882906},
doi = {10.4121/0530be90-cc6c-4029-9774-670657882906},
year = {2024},
date = {2024-09-05},
urldate = {2024-09-05},
howpublished = {4TU.ResearchData},
keywords = {},
pubstate = {published},
tppubtype = {online}
}
|
Unpublished
|
T. M. Blaha; E. J. J. Smeur; B. D. W. Remes
Control of Unknown Quadrotors from a Single Throw (Unpublished) arXiv:2406.11723, 2024. @unpublished{blaha_control_2024,
title = {Control of Unknown Quadrotors from a Single Throw},
author = {T. M. Blaha and E. J. J. Smeur and B. D. W. Remes
},
doi = {10.48550/arXiv.2406.11723},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
howpublished = {arXiv:2406.11723},
keywords = {},
pubstate = {published},
tppubtype = {unpublished}
}
|
T. M. Blaha; E. J. J. Smeur; B. D. W. Remes; C. C. de Visser Flying a Quadrotor with Unknown Actuators and Sensor Configuration (Unpublished) 2024. @unpublished{blaha_flying_2024,
title = {Flying a Quadrotor with Unknown Actuators and Sensor Configuration},
author = {T. M. Blaha and E. J. J. Smeur and B. D. W. Remes and C. C. de Visser},
doi = {10.48550/arXiv.2409.01080},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Proceedings - International Micro Air Vehicle Conference, IMAV 2024},
publisher = {IMAV},
address = {Bristol},
keywords = {},
pubstate = {published},
tppubtype = {unpublished}
}
|
2023
|
Journal Articles
|
G.C.H.E. de Croon Drone-racing champions outpaced by AI (Journal Article) In: Nature, vol. 620, pp. 952-954, 2023, ISBN: 0028-0836. @article{drone_racing_nature_news,
title = {Drone-racing champions outpaced by AI},
author = {G.C.H.E. de Croon},
url = {https://www.nature.com/articles/d41586-023-02506-8},
doi = {10.1038/d41586-023-02506-8},
isbn = {0028-0836},
year = {2023},
date = {2023-08-30},
urldate = {2023-08-30},
journal = {Nature},
volume = {620},
pages = {952-954},
abstract = {An autonomous drone has competed against human drone-racing champions — and won. The victory can be attributed to savvy engineering and a type of artificial intelligence that learns mostly through trial and error.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
An autonomous drone has competed against human drone-racing champions — and won. The victory can be attributed to savvy engineering and a type of artificial intelligence that learns mostly through trial and error. |
Guido C. H. E. Croon Drone-racing champions outpaced by AI (Journal Article) In: Nature, vol. 620, no. 7976, pp. 952–954, 2023, ISSN: 0028-0836, (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{e9309de882e8426685a02436a0e40190,
title = {Drone-racing champions outpaced by AI},
author = {Guido C. H. E. Croon},
url = {https://research.tudelft.nl/en/publications/drone-racing-champions-outpaced-by-ai},
doi = {10.1038/d41586-023-02506-8},
issn = {0028-0836},
year = {2023},
date = {2023-01-01},
journal = {Nature},
volume = {620},
number = {7976},
pages = {952–954},
publisher = {Nature},
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}
}
|
Cheng Liu; Yingfu Xu; Erik Jan Kampen; Guido Croon Nano Quadcopter Obstacle Avoidance with a Lightweight Monocular Depth Network (Journal Article) In: IFAC-PapersOnLine, vol. 56, no. 2, pp. 9312–9317, 2023, ISSN: 2405-8963, (22nd IFAC World Congress ; Conference date: 09-07-2023 Through 14-07-2023). @article{8939194787f34d19846218a767667b53,
title = {Nano Quadcopter Obstacle Avoidance with a Lightweight Monocular Depth Network},
author = {Cheng Liu and Yingfu Xu and Erik Jan Kampen and Guido Croon},
url = {https://research.tudelft.nl/en/publications/nano-quadcopter-obstacle-avoidance-with-a-lightweight-monocular-d},
doi = {10.1016/j.ifacol.2023.10.217},
issn = {2405-8963},
year = {2023},
date = {2023-01-01},
journal = {IFAC-PapersOnLine},
volume = {56},
number = {2},
pages = {9312–9317},
publisher = {Elsevier},
note = {22nd IFAC World Congress ; Conference date: 09-07-2023 Through 14-07-2023},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Andrew J. King; Steven J. Portugal; Daniel Strömbom; Richard P. Mann; José A. Carrillo; Dante Kalise; Guido Croon; Heather Barnett; Paul Scerri; More Authors Biologically inspired herding of animal groups by robots (Journal Article) In: Methods in Ecology and Evolution, vol. 14, no. 2, pp. 478–486, 2023, ISSN: 2041-210X. @article{ee1fa5db82ce49cca721188a0dd6c194,
title = {Biologically inspired herding of animal groups by robots},
author = {Andrew J. King and Steven J. Portugal and Daniel Strömbom and Richard P. Mann and José A. Carrillo and Dante Kalise and Guido Croon and Heather Barnett and Paul Scerri and More Authors},
url = {https://research.tudelft.nl/en/publications/biologically-inspired-herding-of-animal-groups-by-robots},
doi = {10.1111/2041-210X.14049},
issn = {2041-210X},
year = {2023},
date = {2023-01-01},
journal = {Methods in Ecology and Evolution},
volume = {14},
number = {2},
pages = {478–486},
publisher = {John Wiley & Sons},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Rasmus Steffensen; Agnes Steinert; E. J. J. Smeur Nonlinear Dynamic Inversion with Actuator Dynamics: An Incremental Control Perspective (Journal Article) In: Journal of Guidance, Control, and Dynamics: devoted to the technology of dynamics and control, vol. 46, no. 4, pp. 709–717, 2023, ISSN: 0731-5090. @article{5a4287a80ab94c0f880c345ca5000c2d,
title = {Nonlinear Dynamic Inversion with Actuator Dynamics: An Incremental Control Perspective},
author = {Rasmus Steffensen and Agnes Steinert and E. J. J. Smeur},
url = {https://research.tudelft.nl/en/publications/nonlinear-dynamic-inversion-with-actuator-dynamics-an-incremental},
doi = {10.2514/1.G007079},
issn = {0731-5090},
year = {2023},
date = {2023-01-01},
journal = {Journal of Guidance, Control, and Dynamics: devoted to the technology of dynamics and control},
volume = {46},
number = {4},
pages = {709–717},
publisher = {American Institute of Aeronautics and Astronautics Inc. (AIAA)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
A. Altena; J. J. Beers; C. C. Visser Loss-of-Control Prediction of a Quadcopter Using Recurrent Neural Networks (Journal Article) In: Journal of Aerospace Information Systems (online), vol. 20, no. 10, pp. 648–659, 2023, ISSN: 2327-3097, (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{3051e4a556844d69a3a5bf1109298635,
title = {Loss-of-Control Prediction of a Quadcopter Using Recurrent Neural Networks},
author = {A. Altena and J. J. Beers and C. C. Visser},
url = {https://research.tudelft.nl/en/publications/loss-of-control-prediction-of-a-quadcopter-using-recurrent-neural},
doi = {10.2514/1.I011231},
issn = {2327-3097},
year = {2023},
date = {2023-01-01},
journal = {Journal of Aerospace Information Systems (online)},
volume = {20},
number = {10},
pages = {648–659},
publisher = {American Institute of Aeronautics and Astronautics Inc. (AIAA)},
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}
}
|
Alessandro Mancinelli; Bart D. W. Remes; Guido C. H. E. De Croon; Ewoud J. J. Smeur Real-Time Nonlinear Control Allocation Framework for Vehicles with Highly Nonlinear Effectors Subject to Saturation (Journal Article) In: Journal of Intelligent and Robotic Systems: Theory and Applications, vol. 108, no. 4, 2023, ISSN: 0921-0296. @article{a3ef568eb7134c79b73d83a48fc18145,
title = {Real-Time Nonlinear Control Allocation Framework for Vehicles with Highly Nonlinear Effectors Subject to Saturation},
author = {Alessandro Mancinelli and Bart D. W. Remes and Guido C. H. E. De Croon and Ewoud J. J. Smeur},
url = {https://research.tudelft.nl/en/publications/real-time-nonlinear-control-allocation-framework-for-vehicles-wit},
doi = {10.1007/s10846-023-01865-8},
issn = {0921-0296},
year = {2023},
date = {2023-01-01},
journal = {Journal of Intelligent and Robotic Systems: Theory and Applications},
volume = {108},
number = {4},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
T. M. Blaha; E. J. J. Smeur; B. D. W. Remes A Survey of Optimal Control Allocation for Aerial Vehicle Control (Journal Article) In: Actuators, vol. 12, no. 7, 2023, ISSN: 2076-0825. @article{3ac44b4fe76a4c5388e3a007df9df587,
title = {A Survey of Optimal Control Allocation for Aerial Vehicle Control},
author = {T. M. Blaha and E. J. J. Smeur and B. D. W. Remes},
url = {https://research.tudelft.nl/en/publications/a-survey-of-optimal-control-allocation-for-aerial-vehicle-control},
doi = {10.3390/act12070282},
issn = {2076-0825},
year = {2023},
date = {2023-01-01},
journal = {Actuators},
volume = {12},
number = {7},
publisher = {MDPI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Christina Harvey; G. C. H. E. Croon; Graham K. Taylor; Richard J. Bomphrey Lessons from natural flight for aviation: then, now and tomorrow (Journal Article) In: The Journal of Experimental Biology, vol. 226, no. 1, 2023, ISSN: 0022-0949. @article{225489f0eb0b4c599f97b3ce63aac365,
title = {Lessons from natural flight for aviation: then, now and tomorrow},
author = {Christina Harvey and G. C. H. E. Croon and Graham K. Taylor and Richard J. Bomphrey},
url = {https://research.tudelft.nl/en/publications/lessons-from-natural-flight-for-aviation-then-now-and-tomorrow},
doi = {10.1242/jeb.245409},
issn = {0022-0949},
year = {2023},
date = {2023-01-01},
journal = {The Journal of Experimental Biology},
volume = {226},
number = {1},
publisher = {Company of Biologists Ltd},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
M. K. Makaveev; M. Snellen; E. J. J. Smeur Microphones as Airspeed Sensors for Unmanned Aerial Vehicles (Journal Article) In: Sensors, vol. 23, no. 5, 2023, ISSN: 1424-8220. @article{626768d7b4ef47a9bb9810e449320f2e,
title = {Microphones as Airspeed Sensors for Unmanned Aerial Vehicles},
author = {M. K. Makaveev and M. Snellen and E. J. J. Smeur},
url = {https://research.tudelft.nl/en/publications/microphones-as-airspeed-sensors-for-unmanned-aerial-vehicles},
doi = {10.3390/s23052463},
issn = {1424-8220},
year = {2023},
date = {2023-01-01},
journal = {Sensors},
volume = {23},
number = {5},
publisher = {MDPI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
G. C. H. E. Croon; W. Hönig; G. Theraulaz; G. Vásárhelyi Cross-disciplinary approaches for designing intelligent swarms of drones (Journal Article) In: Swarm Intelligence, vol. 17, no. 1-2, pp. 1–4, 2023, ISSN: 1935-3812, (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{22345640e6c24a69b8cf85eedc8c3cad,
title = {Cross-disciplinary approaches for designing intelligent swarms of drones},
author = {G. C. H. E. Croon and W. Hönig and G. Theraulaz and G. Vásárhelyi},
url = {https://research.tudelft.nl/en/publications/cross-disciplinary-approaches-for-designing-intelligent-swarms-of},
doi = {10.1007/s11721-023-00223-6},
issn = {1935-3812},
year = {2023},
date = {2023-01-01},
journal = {Swarm Intelligence},
volume = {17},
number = {1-2},
pages = {1–4},
publisher = {Springer},
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}
}
|
Jane Pauline Ramirez; Salua Hamaza Multimodal Locomotion: Next Generation Aerial–Terrestrial Mobile Robotics (Journal Article) In: Advanced Intelligent Systems, 2023, ISSN: 2640-4567, (Funding Information: This research is funded by the Delft University of Technology. Publisher Copyright: © 2023 The Authors. Advanced Intelligent Systems published by Wiley-VCH GmbH.). @article{9fb07c9259344be2a566ab9911723fc3,
title = {Multimodal Locomotion: Next Generation Aerial–Terrestrial Mobile Robotics},
author = {Jane Pauline Ramirez and Salua Hamaza},
url = {https://research.tudelft.nl/en/publications/multimodal-locomotion-next-generation-aerialterrestrial-mobile-ro},
doi = {10.1002/aisy.202300327},
issn = {2640-4567},
year = {2023},
date = {2023-01-01},
journal = {Advanced Intelligent Systems},
publisher = {Wiley},
note = {Funding Information: This research is funded by the Delft University of Technology. Publisher Copyright: © 2023 The Authors. Advanced Intelligent Systems published by Wiley-VCH GmbH.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Barbara Dix; Meng Li; Esther Roosenbrand; Colby Francoeur; Steven S. Brown; Jessica B. Gilman; Thomas F. Hanisco; J. Pepijn Veefkind; Pieternel F. Levelt; More Authors Sources of Formaldehyde in U.S. Oil and Gas Production Regions (Journal Article) In: ACS Earth and Space Chemistry, vol. 7, no. 12, pp. 2444–2457, 2023, ISSN: 2472-3452, (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{c7d3fcef0a4a4c27b0df42dc4f7e6037,
title = {Sources of Formaldehyde in U.S. Oil and Gas Production Regions},
author = {Barbara Dix and Meng Li and Esther Roosenbrand and Colby Francoeur and Steven S. Brown and Jessica B. Gilman and Thomas F. Hanisco and J. Pepijn Veefkind and Pieternel F. Levelt and More Authors},
url = {https://research.tudelft.nl/en/publications/sources-of-formaldehyde-in-us-oil-and-gas-production-regions},
doi = {10.1021/acsearthspacechem.3c00203},
issn = {2472-3452},
year = {2023},
date = {2023-01-01},
journal = {ACS Earth and Space Chemistry},
volume = {7},
number = {12},
pages = {2444–2457},
publisher = {American Chemical Society (ACS)},
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}
}
|
S. Schröter; E. J. J. Smeur; B. D. W. Remes Design and Joint Control of a Conjoined Biplane and Quadrotor (Journal Article) In: Unmanned Systems, 2023, ISSN: 2301-3850. @article{25b011785cc942d1a7f9ec798d99b656,
title = {Design and Joint Control of a Conjoined Biplane and Quadrotor},
author = {S. Schröter and E. J. J. Smeur and B. D. W. Remes},
url = {https://research.tudelft.nl/en/publications/design-and-joint-control-of-a-conjoined-biplane-and-quadrotor-2},
doi = {10.1142/S2301385024430039},
issn = {2301-3850},
year = {2023},
date = {2023-01-01},
journal = {Unmanned Systems},
publisher = {World Scientific Publishing},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
E. J. J. Smeur; G. C. H. E. Croon; Q. Chu Corrigendum to “Cascaded incremental nonlinear dynamic inversion control for MAV disturbance rejection”[Journal of Control Engineering Practice 73 (2018) 79–90, (S0967066118300030), (10.1016/j.conengprac.2018.01.003)] (Journal Article) In: Control Engineering Practice, vol. 141, 2023, ISSN: 0967-0661. @article{49b721f7bfbb45c59d9a4cf9e938fe4c,
title = {Corrigendum to “Cascaded incremental nonlinear dynamic inversion control for MAV disturbance rejection”[Journal of Control Engineering Practice 73 (2018) 79–90, (S0967066118300030), (10.1016/j.conengprac.2018.01.003)]},
author = {E. J. J. Smeur and G. C. H. E. Croon and Q. Chu},
url = {https://research.tudelft.nl/en/publications/corrigendum-to-cascaded-incremental-nonlinear-dynamic-inversion-c},
doi = {10.1016/j.conengprac.2022.105093},
issn = {0967-0661},
year = {2023},
date = {2023-01-01},
journal = {Control Engineering Practice},
volume = {141},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Books
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Stephen Pringle; Zoe G. Davies; Mark A. Goddard; Martin Dallimer; Emma Hart; Léni E. Le Goff; Simon J. Langdale; Sara-Adela Abad; S. Hamaza; More Authors Robotics and Autonomous Systems for Environmental Sustainability: Monitoring Terrestrial Biodiversity (Book) EPSRC UK-RAS Network, 2023. @book{808e66196aee41a6953d254d0c537f8f,
title = {Robotics and Autonomous Systems for Environmental Sustainability: Monitoring Terrestrial Biodiversity},
author = {Stephen Pringle and Zoe G. Davies and Mark A. Goddard and Martin Dallimer and Emma Hart and Léni E. Le Goff and Simon J. Langdale and Sara-Adela Abad and S. Hamaza and More Authors},
url = {https://research.tudelft.nl/en/publications/robotics-and-autonomous-systems-for-environmental-sustainability-},
doi = {10.31256/WP2023.4},
year = {2023},
date = {2023-01-01},
publisher = {EPSRC UK-RAS Network},
series = {UK-RAS White paper Series on Robotics and Autonomous Systems (RAS)},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
|
Conferences
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J. P. R Ramirez; A. Bredenbeck; S. Hamaza Bioinspired Compliant Limbs For Robust Landing Of Free-Flying Robots (Conference) 17th Symposium on Advanced Space Technologies in Robotics and Automation, ESA/ESTEC, 2023, (17th Symposium on Advanced Space Technologies in Robotics and Automation , ASTRA 2023 ; Conference date: 18-10-2023 Through 20-10-2023). @conference{9579553c9c0940e582ce18bdc4c3bac4,
title = {Bioinspired Compliant Limbs For Robust Landing Of Free-Flying Robots},
author = {J. P. R Ramirez and A. Bredenbeck and S. Hamaza},
url = {https://research.tudelft.nl/en/publications/bioinspired-compliant-limbs-for-robust-landing-of-free-flying-rob},
year = {2023},
date = {2023-01-01},
booktitle = {17th Symposium on Advanced Space Technologies in Robotics and Automation},
publisher = {ESA/ESTEC},
note = {17th Symposium on Advanced Space Technologies in Robotics and Automation , ASTRA 2023 ; Conference date: 18-10-2023 Through 20-10-2023},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
Proceedings Articles
|
Federico Paredes-Vallés; Kirk Y. W. Scheper; Christope De Wagter; Guido C. H. E. De Croon Taming Contrast Maximization for Learning Sequential, Low-latency, Event-based Optical Flow (Proceedings Article) In: Proceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023, pp. 9661–9671, IEEE, United States, 2023, (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. ; 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023 ; Conference date: 02-10-2023 Through 06-10-2023). @inproceedings{fbeb5c823153469baa4a8cf32bb00099,
title = {Taming Contrast Maximization for Learning Sequential, Low-latency, Event-based Optical Flow},
author = {Federico Paredes-Vallés and Kirk Y. W. Scheper and Christope De Wagter and Guido C. H. E. De Croon},
url = {https://research.tudelft.nl/en/publications/taming-contrast-maximization-for-learning-sequential-low-latency-},
doi = {10.1109/ICCV51070.2023.00889},
year = {2023},
date = {2023-01-01},
booktitle = {Proceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023},
pages = {9661–9671},
publisher = {IEEE},
address = {United States},
series = {Proceedings of the IEEE International Conference on Computer Vision},
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. ; 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023 ; Conference date: 02-10-2023 Through 06-10-2023},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
W. J. E. Völker; Y. Li; E. Kampen Twin-Delayed Deep Deterministic Policy Gradient for altitude control of a flying-wing aircraft with an uncertain aerodynamic model (Proceedings Article) In: AIAA SciTech Forum 2023, 2023, (AIAA SCITECH 2023 Forum ; Conference date: 23-01-2023 Through 27-01-2023). @inproceedings{9986a064454849c984e3794c73888ea5,
title = {Twin-Delayed Deep Deterministic Policy Gradient for altitude control of a flying-wing aircraft with an uncertain aerodynamic model},
author = {W. J. E. Völker and Y. Li and E. Kampen},
url = {https://research.tudelft.nl/en/publications/twin-delayed-deep-deterministic-policy-gradient-for-altitude-cont},
doi = {10.2514/6.2023-2678},
year = {2023},
date = {2023-01-01},
booktitle = {AIAA SciTech Forum 2023},
note = {AIAA SCITECH 2023 Forum ; Conference date: 23-01-2023 Through 27-01-2023},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Kartik Suryavanshi; S. Hamaza; V. Wijk; J. L. Herder ADAPT: A 3 Degrees of Freedom Reconfigurable Force Balanced Parallel Manipulator for Aerial Applications (Proceedings Article) In: Proceedings IEEE International Conference on Robotics and Automation, ICRA 2023, pp. 11936–11942, IEEE, United States, 2023, ISBN: 979-8-3503-2365-8, (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.; ICRA 2023: International Conference on Robotics and Automation ; Conference date: 29-05-2023 Through 02-06-2023). @inproceedings{7ad2db50f9d14b86aa3f42b3170cbb89,
title = {ADAPT: A 3 Degrees of Freedom Reconfigurable Force Balanced Parallel Manipulator for Aerial Applications},
author = {Kartik Suryavanshi and S. Hamaza and V. Wijk and J. L. Herder},
url = {https://research.tudelft.nl/en/publications/adapt-a-3-degrees-of-freedom-reconfigurable-force-balanced-parall},
doi = {10.1109/ICRA48891.2023.10160451},
isbn = {979-8-3503-2365-8},
year = {2023},
date = {2023-01-01},
booktitle = {Proceedings IEEE International Conference on Robotics and Automation, ICRA 2023},
pages = {11936–11942},
publisher = {IEEE},
address = {United States},
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.; ICRA 2023: International Conference on Robotics and Automation ; Conference date: 29-05-2023 Through 02-06-2023},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Anique Altena; Salil Luesutthiviboon; Guido Croon; Mirjam Snellen; Mark Voskuijl Comparison of acoustic localisation techniques for drone position estimation using real-world experimental data (Proceedings Article) In: Carletti, Eleonora (Ed.): Proceedings of the 29th International Congress on Sound and Vibration, ICSV 2023, Society of Acoustics, 2023, (29th International Congress on Sound and Vibration, ICSV 2023 : The annual congress of the International Institute of Acoustics and Vibrations (IIAV), ICSV 2023 ; Conference date: 09-07-2023 Through 13-07-2023). @inproceedings{5bee61754c1041e0bf5c60bb9d9c6640,
title = {Comparison of acoustic localisation techniques for drone position estimation using real-world experimental data},
author = {Anique Altena and Salil Luesutthiviboon and Guido Croon and Mirjam Snellen and Mark Voskuijl},
editor = {Eleonora Carletti},
url = {https://research.tudelft.nl/en/publications/comparison-of-acoustic-localisation-techniques-for-drone-position},
year = {2023},
date = {2023-01-01},
booktitle = {Proceedings of the 29th International Congress on Sound and Vibration, ICSV 2023},
publisher = {Society of Acoustics},
series = {Proceedings of the International Congress on Sound and Vibration},
note = {29th International Congress on Sound and Vibration, ICSV 2023 : The annual congress of the International Institute of Acoustics and Vibrations (IIAV), ICSV 2023 ; Conference date: 09-07-2023 Through 13-07-2023},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Cheng Liu; Erik Jan Van Kampen; Guido C. H. E. De Croon Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learning (Proceedings Article) In: Proceedings - ICRA 2023, pp. 7198–7204, Institute of Electrical and Electronics Engineers (IEEE), United States, 2023, (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. ; 2023 IEEE International Conference on Robotics and Automation, ICRA 2023 ; Conference date: 29-05-2023 Through 02-06-2023). @inproceedings{f53b6aea479944049cbfdcf6d5a85242,
title = {Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learning},
author = {Cheng Liu and Erik Jan Van Kampen and Guido C. H. E. De Croon},
url = {https://research.tudelft.nl/en/publications/adaptive-risk-tendency-nano-drone-navigation-in-cluttered-environ},
doi = {10.1109/ICRA48891.2023.10160324},
year = {2023},
date = {2023-01-01},
booktitle = {Proceedings - ICRA 2023},
pages = {7198–7204},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
address = {United States},
series = {Proceedings - IEEE International Conference on Robotics and Automation},
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. ; 2023 IEEE International Conference on Robotics and Automation, ICRA 2023 ; Conference date: 29-05-2023 Through 02-06-2023},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Rik J. Bouwmeester; Federico Paredes-Valles; Guido C. H. E. De Croon NanoFlowNet: Real-time Dense Optical Flow on a Nano Quadcopter (Proceedings Article) In: Proceedings - ICRA 2023, pp. 1996–2003, Institute of Electrical and Electronics Engineers (IEEE), United States, 2023, (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. ; 2023 IEEE International Conference on Robotics and Automation, ICRA 2023 ; Conference date: 29-05-2023 Through 02-06-2023). @inproceedings{334f5d9a68de4ae9951aa8fd40c78215,
title = {NanoFlowNet: Real-time Dense Optical Flow on a Nano Quadcopter},
author = {Rik J. Bouwmeester and Federico Paredes-Valles and Guido C. H. E. De Croon},
url = {https://research.tudelft.nl/en/publications/nanoflownet-real-time-dense-optical-flow-on-a-nano-quadcopter},
doi = {10.1109/ICRA48891.2023.10161258},
year = {2023},
date = {2023-01-01},
booktitle = {Proceedings - ICRA 2023},
pages = {1996–2003},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
address = {United States},
series = {Proceedings - IEEE International Conference on Robotics and Automation},
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. ; 2023 IEEE International Conference on Robotics and Automation, ICRA 2023 ; Conference date: 29-05-2023 Through 02-06-2023},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Tom Suys; Sunyou Hwang; Guido C. H. E. De Croon; Bart D. W. Remes Autonomous Control for Orographic Soaring of Fixed-Wing UAVs (Proceedings Article) In: Proceedings - ICRA 2023, pp. 5338–5344, Institute of Electrical and Electronics Engineers (IEEE), United States, 2023, (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. ; 2023 IEEE International Conference on Robotics and Automation, ICRA 2023 ; Conference date: 29-05-2023 Through 02-06-2023). @inproceedings{415500ce291541629a377ea124756701,
title = {Autonomous Control for Orographic Soaring of Fixed-Wing UAVs},
author = {Tom Suys and Sunyou Hwang and Guido C. H. E. De Croon and Bart D. W. Remes},
url = {https://research.tudelft.nl/en/publications/autonomous-control-for-orographic-soaring-of-fixed-wing-uavs},
doi = {10.1109/ICRA48891.2023.10161578},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Proceedings - ICRA 2023},
pages = {5338–5344},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
address = {United States},
series = {Proceedings - IEEE International Conference on Robotics and Automation},
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. ; 2023 IEEE International Conference on Robotics and Automation, ICRA 2023 ; Conference date: 29-05-2023 Through 02-06-2023},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|