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Learning Vision-based Cohesive Flight in Drone Swarms
[article]
2018
arXiv
pre-print
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This paper presents a data-driven approach to learning vision-based collective behavior from a simple flocking algorithm. ...
We simulate a swarm of quadrotor drones and formulate the controller as a regression problem in which we generate 3D velocity commands directly from raw camera images. ...
This research was supported by the Swiss National Science Foundation (SNF) with grant number 200021 155907 and the Swiss National Center of Competence Research (NCCR). ...
arXiv:1809.00543v1
fatcat:lkizzqursnejndc3muovz6riri
Learning monocular reactive UAV control in cluttered natural environments
2013
2013 IEEE International Conference on Robotics and Automation
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Given a small set of human pilot demonstrations, we use recent state-of-theart imitation learning techniques to train a controller that can avoid trees by adapting the MAVs heading. ...
We demonstrate the performance of our system in a more controlled environment indoors, and in real natural forest environments outdoors. ...
ACKNOWLEDGMENT This work has been supported by ONR MURI grant N00014-09-1-1051 "Provably-Stable Vision-Based Control of High-Speed Flight through Forests and Urban Environments". A. ...
doi:10.1109/icra.2013.6630809
dblp:conf/icra/RossMSWDBH13
fatcat:ggioq3gsojdzdd7kf5e63vqel4
Learning Monocular Reactive UAV Control in Cluttered Natural Environments
[article]
2012
arXiv
pre-print
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Given a small set of human pilot demonstrations, we use recent state-of-the-art imitation learning techniques to train a controller that can avoid trees by adapting the MAVs heading. ...
We demonstrate the performance of our system in a more controlled environment indoors, and in real natural forest environments outdoors. ...
ACKNOWLEDGMENT This work has been supported by ONR through BIRD MURI. A. Wendel acknowledges the support of the Austrian Marshallplan Foundation during his research visit at CMU. ...
arXiv:1211.1690v1
fatcat:dab5fekrgfdxzl2xezx5inpoza
A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints
2020
Frontiers in Robotics and AI
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Vision-based systems, for instance, force MAVs to fly within the field of view of their camera. ...
Range or communication-based solutions, alternatively, provide omni-directional relative localization, yet can be victim to unobservable conditions under certain flight behaviors, such as parallel flight ...
Using Visual Odometry (VO), a robot integrates vision-based measurements during flight in order to estimate its motion. ...
doi:10.3389/frobt.2020.00018
pmid:33501187
pmcid:PMC7806031
fatcat:p3zp5y3r65cn7nilxzu6gedxaq
Mathematical modeling for flocking flight of autonomous multi-UAV system, including environmental factors
2020
KSII Transactions on Internet and Information Systems
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The main contribution of this work is a mathematical model for stable group flight even in adverse weather conditions (e.g., heavy wind, rain, etc.) by adding Gaussian noise. ...
One is based on a simple biological imitation from statistical physical modeling, which mimics animal group behavior; the other is an algorithm for cooperatively tracking an object, which aligns the velocities ...
In this paper, the grouping and flight direction of drones is determined by a linear equation based on the Lyapunov-based method. ...
doi:10.3837/tiis.2020.02.007
fatcat:pkwvafv5nzahzi3dkppwsv47jy
A General Auxiliary Controller for Multi-agent Flocking
[article]
2021
arXiv
pre-print
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We demonstrate the efficacy of the auxiliary controller by applying it to several existing algorithms including learning-based and non-learning-based methods. ...
We aim to improve the performance of multi-agent flocking behavior by quantifying the structural significance of each agent. ...
“Learning vision-based flight in
IEEE Transactions on Robotics 22.2 (2006), pp. 402– drone swarms by imitation”. ...
arXiv:2112.06023v1
fatcat:o7s4b6evc5ggnbfh4j2737kdzy
Final Program
2020
2020 International Conference on Unmanned Aircraft Systems (ICUAS)
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Our conference is "truly international" as evidenced by the submitted papers and registered participants. ...
The three-day conference is preceded by a one-day Workshops/Tutorials program, which is composed of four (4) Tutorials. ...
due to their short flight time. ...
doi:10.1109/icuas48674.2020.9214039
fatcat:7jr6chhfija47kgtwoxqmfmmoe
An Organic Computing Approach to Self-Organizing Robot Ensembles
2016
Frontiers in Robotics and AI
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Relying on an extended Learning Classifier System (XCS) in combination with adequate simulation techniques, this basic system design empowers robot individuals to improve their individual and collaborative ...
performances, e.g., by means of adapting to changing goals and conditions. ...
Yet, it has been shown that observer/controllerdriven robots can increase their learning speed imitating each other (Jungmann et al., 2011) . ...
doi:10.3389/frobt.2016.00067
fatcat:phanw5tjgnfcpnuswdo4g7kih4
A Literature Survey of Unmanned Aerial Vehicle Usage for Civil Applications
2021
Journal of Aerospace Technology and Management
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Unmanned aerial vehicles aid in the detection of weeds, crop management, and the identification of plant diseases, among other issues, paving the path for researchers to create drone applications in the ...
Unmanned vehicles/systems (UVs/USs) technology has exploded in recent years. Unmanned vehicles are operated in the air, on the ground, or on/in the water. ...
DJI Phantom 4 RTK drone; Zero V-Cptr Falcon. Exploring techniques and developments in automated by the deep learning techniques applied in the cloud. ...
doi:10.1590/jatm.v13.1233
fatcat:btwhe2e3sjh3zccmfwretpwxiu
Research Progress and Prospects of Agricultural Aero-Bionic Technology in China
2021
Applied Sciences
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It is produced by the mutual penetration and integration of life science and engineering science. ...
Bionic technology has received more and more attention in recent years, and breakthroughs have been made in the fields of biomedicine and health, military, brain science and brain-like navigation, and ...
Han [71] proposed a grid cell construction model based on differential Hebbian learning. ...
doi:10.3390/app112110435
fatcat:66bjym3hszbtdla7ikw3lodony
Drone Deep Reinforcement Learning: A Review
2021
Electronics
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In this paper, we described the state of the art of one subset of these algorithms: the deep reinforcement learning (DRL) techniques. ...
We noted that most of these DRL methods were designed to ensure stable and smooth UAV navigation by training computer-simulated environments. ...
The data in the research is trained through a novel developed algorithm that uses DRL and imitation learning [41] . ...
doi:10.3390/electronics10090999
doaj:57ededb7d1a0445eaf34975cb6625c1f
fatcat:kya3fbblszd27i4exlybnji4ni
The Holy Grail of Multi-Robot Planning: Learning to Generate Online-Scalable Solutions from Offline-Optimal Experts
[article]
2021
arXiv
pre-print
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The use of learning-based methods in multi-robot planning holds great promise as it enables us to offload the online computational burden of expensive, yet optimal solvers, to an offline learning procedure ...
Simply put, the idea is to train a policy to copy an optimal pattern generated by a small-scale system, and then transfer that policy to much larger systems, in the hope that the learned strategy scales ...
An example of this is a swarm of drones flying closely to each other and turbulence affecting the motions of other drones in the vicinity. ...
arXiv:2107.12254v1
fatcat:tvmepyeftjeorivf332xgkky4m
Design and Test of an adaptive augmented reality interface to manage systems to assist critical missions
[article]
2021
arXiv
pre-print
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We present a user interface (UI) based on augmented reality (AR) with head-mounted display (HMD) for improving situational awareness during critical operation and improve human efficiency on operations ...
We established experiments where people had been put in a stressful situation and are asked to resolve a complex mission using a headset and a computer. ...
Acknowledgments This work was supported by Mitacs [grant number IT10647] and Humanitas Solutions. ...
arXiv:2103.14160v1
fatcat:d226zigpt5gtlbnrlx5pf3ssnm
A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance
2019
Remote Sensing
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Advances in Unmanned Aerial Vehicles (UAVs), also known as drones, offer unprecedented opportunities to boost a wide array of large-scale Internet of Things (IoT) applications. ...
In this regard, Deep Learning (DL) techniques have arisen as a promising alternative for improving real-time obstacle detection and collision avoidance for highly autonomous UAVs. ...
The learning process returns the policy that best imitates the action of the experts according to the given examples [49, 50, 52, 55] . ...
doi:10.3390/rs11182144
fatcat:54xs26xnvzf7rfa5b64tuzkz44
Table of Contents
2021
IEEE Robotics and Automation Letters
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Johnson 2946 Vision-Based Drone Flocking in Outdoor Environments . . . . . . . . . . . . . . . . . . . . . F. Schilling, F. Schiano, and D. ...
Berenson 1447 Tracking and Relative Localization of Drone Swarms With a Vision-Based Headset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
doi:10.1109/lra.2021.3072707
fatcat:qyphyzqxfrgg7dxdol4qamrdqu
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