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Filters
Learning a Controller Fusion Network by Online Trajectory Filtering for Vision-based UAV Racing
[article]
2019
arXiv
pre-print
Preserved Fulltext
The network learns a robust controller with online trajectory filtering, which suppresses noisy trajectories and imperfections of individual controllers. ...
The result is a network that is able to learn a good fusion of filtered trajectories from different controllers leading to significant improvements in overall performance. ...
Acknowledgments This work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research. ...
arXiv:1904.08801v1
fatcat:ikybmj57j5grrlswxxp3h3p5ia
Learning a Controller Fusion Network by Online Trajectory Filtering for Vision-Based UAV Racing
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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The network learns a robust controller with online trajectory filtering, which suppresses noisy trajectories and imperfections of individual controllers. ...
The result is a network that is able to learn a good fusion of filtered trajectories from different controllers leading to significant improvements in overall performance. ...
Acknowledgments This work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research. ...
doi:10.1109/cvprw.2019.00083
dblp:conf/cvpr/MullerLCSMG19
fatcat:n7w3v7y5qbfqbfrly4z7etnjsa
Table of Contents
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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of Objects by UAVs 563 Hasan Saribas (missing), Bedirhan Uzun (missing), Burak Benligiray (missing), Onur Eker (missing), and Hakan Cevikalp (missing) Learning a Controller Fusion Network by Online Trajectory ...
Filtering for Vision-Based UAV Racing 573 Matthias Müller (missing), Guohao Li (missing), Vincent Casser (missing), Neil Smith (missing), Dominik L. ...
Learning Raw Image Denoising With Bayer Pattern Unification and Bayer Preserving Augmentation 2070 Jiaming Liu (missing) , Chi-Hao Wu (missing) , Yuzhi Wang (missing) , Qin Xu (missing), Yuqian Zhou ...
doi:10.1109/cvprw.2019.00004
fatcat:h7xpqwyrofdxniqtxbodn66mpy
A simple vision-based navigation and control strategy for autonomous drone racing
[article]
2021
arXiv
pre-print
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Based on the API provided by the manufacturer, we have created a Python application that enables the communication with the drone over WiFi, realises drone positioning based on visual feedback, and generates ...
In this paper, we present a control system that allows a drone to fly autonomously through a series of gates marked with ArUco tags. A simple and low-cost DJI Tello EDU quad-rotor platform was used. ...
ArUco markers), use more advanced control strategies and methods (data fusion, trajectory optimisation, approaches based on reinforcement learning), consider reconfigurable devices (FPGA, Zynq SoC) as ...
arXiv:2104.09815v1
fatcat:jdrjpryofjgtdeqculkyancg2q
2019 Index IEEE Robotics and Automation Letters Vol. 4
2019
IEEE Robotics and Automation Letters
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., +, LRA April 2019 830-837 Vision-Based Online Learning Kinematic Control for Soft Robots Using Local Gaussian Process Regression. ...
., +, LRA July 2019 2691-2698 Vision-Based Online Learning Kinematic Control for Soft Robots Using Local Gaussian Process Regression. ...
Permanent magnets Adaptive Dynamic Control for Magnetically Actuated Medical Robots. ...
doi:10.1109/lra.2019.2955867
fatcat:ckastwefh5chhamsravandtnx4
2020 Index IEEE Robotics and Automation Letters Vol. 5
2020
IEEE Robotics and Automation Letters
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., +, LRA April 2020 2427-2434 Knowledge Transfer Between Different UAVs for Trajectory Tracking. Gaussian Process Online Learning With a Sparse Data Stream. ...
., +, LRA April 2020 1891-1898 Synthesis of a Time-Varying Communication Network by Robot Teams With Gated Recurrent Fusion to Learn Driving Behavior from Temporal Multimodal Data. ...
doi:10.1109/lra.2020.3032821
fatcat:qrnouccm7jb47ipq6w3erf3cja
Image Generation for Efficient Neural Network Training in Autonomous Drone Racing
[article]
2020
arXiv
pre-print
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In autonomous drone racing, one must accomplish this task by flying fully autonomously in an unknown environment by relying only on computer vision methods for detecting the target gates. ...
Convolutional neural networks offer impressive advances in computer vision but require an immense amount of data to learn. ...
The latter is fed into the extended Kalman filter (EKF) running on the Intel Aero Flight Controller with a Dronecode PX4 autopilot to obtain more accurate velocity information through sensor fusion with ...
arXiv:2008.02596v1
fatcat:nqpulbvu5fhvfdzjqxdszu3er4
2020 Index IEEE Transactions on Vehicular Technology Vol. 69
2020
IEEE Transactions on Vehicular Technology
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Joint Design of Platoon Communication and Control Based on LTE-V2V; 15893-15907 Hong, C.S., see Nguyen, M.N.H., TVT May 2020 5618-5633 Hong, C.S., see Chen, D., TVT May 2020 5634-5646 Hong, C.S., ...
see Le, T.H.T., TVT Dec. 2020 15162-15176 Hong, D., Lee, S., Cho, Y.H., Baek, D., Kim, J., and Chang, N Guo, H., Liu, J., and Zhang, Y., Toward Swarm Coordination: Topol-ogy-Aware Inter-UAV Routing ...
Bera, A., +, TVT
June 2020 6680-6687
Rendezvous: Opportunistic Data Delivery to Mobile Users by UAVs
Through Target Trajectory Prediction. ...
doi:10.1109/tvt.2021.3055470
fatcat:536l4pgnufhixneoa3a3dibdma
From ERL to MBZIRC: Development of An Aerial-Ground Robotic Team for Search and Rescue
[chapter]
2021
Search and Rescue Robotics [Working Title]
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Throughout the chapter, we highlight the evolution of the robotic system based on the experience gained in the ERL competition. ...
We focus on the implementation of hardware and software modules that enable the deployment of aerial-ground robotic teams in unstructured environments for joint missions. ...
for control and trajectory execution of UAVs in obstacle-rich environments. ...
doi:10.5772/intechopen.99210
fatcat:kbq5opjy2jhepiauunjmqxqgja
Table of Contents
2020
IEEE Robotics and Automation Letters
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Barfoot 1429 A Probabilistic Model-Based Online Learning Optimal Control Algorithm for Soft Pneumatic Actuators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Degani 2403 Musculoskeletal AutoEncoder: A Unified Online Acquisition Method of Intersensory Networks for State Estimation, Control, and Simulation of Musculoskeletal Humanoids . . . . . . . . . . . ...
doi:10.1109/lra.2020.2987582
fatcat:3qafzip5xrg5jliyngq4xxvjha
Autonomous Aerial Delivery Vehicles, a Survey of Techniques on how Aerial Package Delivery is Achieved
[article]
2022
arXiv
pre-print
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Other Versions
Furthermore, improved control schemes and vehicle dynamics are better able to model the payload and improved perception algorithms to detect key features within the unmanned aerial vehicle's (UAV) environment ...
This has been enabled by technological advancements in aerial manipulators and novel grippers with enhanced force to weight ratios. ...
The second category is online SLAM, which estimates the current pose of the vehicle based on the last sensor data typically using filter based approaches. ...
arXiv:2110.02429v2
fatcat:pi2di7z63zfhvolyeuxvdjfb3u
Graph-Based Horizon Line Detection for UAV Navigation
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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We then determine the sky-component by cascade filtering and extract the horizon line based on the boundaries of the sky-component. ...
To address these problems, we propose a graph-based horizon line detection technique that is composed of graph-based image segmentation, connected domain cascade filtering, horizon line extraction, and ...
of 4 UAVs for the Simulation Docking Race and in a formation of 7 UAVs for the Speed Crossing Race. ...
doi:10.1109/jstars.2021.3126586
fatcat:v5pnp5fyurdi5obkxokpxz6sgm
Protect Your Sky: A Survey of Counter Unmanned Aerial Vehicle Systems
2020
IEEE Access
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The last part is devoted to a survey of the CUS market with relevant challenges and future visions. ...
The CUS, also known as a counterdrone system, protects personal, commercial, public, and military facilities and areas from uncontrollable and belligerent UAVs by neutralizing or destroying them. ...
ACKNOWLEDGMENT The authors wish to express their deep appreciation for the support rendered by the members of the Next-Generation Unmanned Vehicle Wireless Communication Laboratory (including the Mobile ...
doi:10.1109/access.2020.3023473
fatcat:boi4ct6lgvdndp4nhkm2hdthwe
Application Specific Drone Simulators: Recent Advances and Challenges
2019
Simulation modelling practice and theory
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2019 pre-print arXiv:1902.00616v1 fatcat:h75bxbx5dvc55di6wihuq35pae
Security and viability concerns in drone-based applications are growing at an alarming rate. Besides, UAV networks (UAVNets) are distinctive from other ad-hoc networks. ...
This is achievable by creating a simulator that includes these aspects. ...
AirSim AirSim is an O/S drone simulator designed by Microsoft's Aerial Informatics and Robotics (AIR), primarily, to introduce it as a useful tool for AI research focusing on deep learning, computer vision ...
doi:10.1016/j.simpat.2019.01.004
fatcat:oy4rssrl5fagtixx5wrr747apm
Decentralized Communication-Aware Motion Planning in Mobile Networks: An Information-Gain Approach
2009
Journal of Intelligent and Robotic Systems
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More specifically, we show how each node can predict the information gained through its communications, by online learning of link quality measures such as received Signal to Noise Ratio (SNR) and correlation ...
We finally show that highly correlated deep fades, on the other hand, can degrade the performance drastically for a long period of time. ...
We then propose a probabilistic decision-making and control framework that integrates both communication and sensing objectives based on online learning of link qualities. ...
doi:10.1007/s10846-009-9335-9
fatcat:fb626fpclvd2fm6bi6zhfe5n3q
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