Filters








331 Hits in 5.4 sec

Learning Vision-based Cohesive Flight in Drone Swarms [article]

Fabian Schilling, Julien Lecoeur, Fabrizio Schiano, Dario Floreano
2018 arXiv   pre-print
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

Stephane Ross, Narek Melik-Barkhudarov, Kumar Shaurya Shankar, Andreas Wendel, Debadeepta Dey, J. Andrew Bagnell, Martial Hebert
2013 2013 IEEE International Conference on Robotics and Automation  
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]

Stephane Ross, Narek Melik-Barkhudarov, Kumar Shaurya Shankar, Andreas Wendel, Debadeepta Dey, J. Andrew Bagnell, Martial Hebert
2012 arXiv   pre-print
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

Mario Coppola, Kimberly N. McGuire, Christophe De Wagter, Guido C. H. E. de Croon
2020 Frontiers in Robotics and AI  
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  
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]

Jinfan Zhou, Jiyu Cheng, Lin Zhang, Wei Zhang
2021 arXiv   pre-print
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)  
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

Sebastian von Mammen, Sven Tomforde, Jörg Hähner
2016 Frontiers in Robotics and AI  
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

Mithra Sivakumar, Naga Malleswari TYJ
2021 Journal of Aerospace Technology and Management  
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

Yali Zhang, Haoxin Tian, Xinrong Huang, Chenyang Ma, Linlin Wang, Hanchao Liu, Yubin Lan
2021 Applied Sciences  
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

Ahmad Taher Azar, Anis Koubaa, Nada Ali Mohamed, Habiba A. Ibrahim, Zahra Fathy Ibrahim, Muhammad Kazim, Adel Ammar, Bilel Benjdira, Alaa M. Khamis, Ibrahim A. Hameed, Gabriella Casalino
2021 Electronics  
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]

Amanda Prorok, Jan Blumenkamp, Qingbiao Li, Ryan Kortvelesy, Zhe Liu, Ethan Stump
2021 arXiv   pre-print
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]

Dany Naser Addin, Benoit Ozell
2021 arXiv   pre-print
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

Paula Fraga-Lamas, Lucía Ramos, Víctor Mondéjar-Guerra, Tiago M. Fernández-Caramés
2019 Remote Sensing  
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  
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
« Previous Showing results 1 — 15 out of 331 results