![]() ![]() Swarms of UAVs have recently achieved growing popularity in both industrial and research fields due to the development of UAV technology and reasonably priced models, namely drones or quadcopters ( Bürkle, Segor & Kollmann, 2011). The analytical results of behavioral rules’ impact also validate the proposed weighting mechanism's effectiveness leading to improved performance. The simulation results show that our proposed scheme has better performance than the conventional Reynolds-based ones in terms of the flock compactness and the reduction in the number of crashed swarm members due to collisions. This paper proposes a novel control scheme for a swarm of Unmanned Aerial Vehicles (UAVs) that also employs the original Reynolds rules but adopts an adaptive weight allocation mechanism based on the current context than being fixed at the beginning. Although optimal values of the weighting coefficients giving good performance can be found through many experiments for each particular scenario, the overall performance could not be guaranteed due to unexpected conditions not covered in experiments. PeerJ Computer Science 7: e388 Ĭooperative navigation for fleets of robots conventionally adopts algorithms based on Reynolds's flocking rules, which usually use a weighted sum of vectors for calculating the velocity from behavioral velocity vectors with corresponding fixed weights. An adaptive weighting mechanism for Reynolds rules-based flocking control scheme. ![]() ![]() Cite this article Hoang DNM, Tran DM, Tran T, Pham H. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. Licence This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. 2 Vietnam National University of Ho Chi Minh City (VNU-HCM), Ho Chi Minh, Vietnam DOI 10.7717/peerj-cs.388 Published Accepted Received Academic Editor Tawfik Al-Hadhrami Subject Areas Adaptive and Self-Organizing Systems, Algorithms and Analysis of Algorithms, Autonomous Systems, Embedded Computing, Robotics Keywords Swarm behavior, Reynolds rules, Flocking control, Adaptive algorithm Copyright © 2021 Hoang et al. ![]()
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