Version 1
: Received: 13 December 2020 / Approved: 14 December 2020 / Online: 14 December 2020 (08:26:16 CET)
How to cite:
Sundar, J.; Yesudasan, S.; Chacko, S. A Cooperative Multi-Agent Based Path-Planning and Optimization Strategy for Dynamic Environment. Preprints2020, 2020120307. https://doi.org/10.20944/preprints202012.0307.v1
Sundar, J.; Yesudasan, S.; Chacko, S. A Cooperative Multi-Agent Based Path-Planning and Optimization Strategy for Dynamic Environment. Preprints 2020, 2020120307. https://doi.org/10.20944/preprints202012.0307.v1
Sundar, J.; Yesudasan, S.; Chacko, S. A Cooperative Multi-Agent Based Path-Planning and Optimization Strategy for Dynamic Environment. Preprints2020, 2020120307. https://doi.org/10.20944/preprints202012.0307.v1
APA Style
Sundar, J., Yesudasan, S., & Chacko, S. (2020). A Cooperative Multi-Agent Based Path-Planning and Optimization Strategy for Dynamic Environment. Preprints. https://doi.org/10.20944/preprints202012.0307.v1
Chicago/Turabian Style
Sundar, J., Sumith Yesudasan and Sibi Chacko. 2020 "A Cooperative Multi-Agent Based Path-Planning and Optimization Strategy for Dynamic Environment" Preprints. https://doi.org/10.20944/preprints202012.0307.v1
Abstract
This investigation explores a novel path-planning and optimization strategy for multiple cooperative robotic agents, applied in a fully observable and dynamically changing obstacle field. Current dynamic path planning strategies employ static algorithms operating over incremental time-steps. We propose a cooperative multi-agent (CMA) based algorithm, based on natural flocking of animals, using vector operations. It is preferred over more common graph search algorithms like A* as it can be easily applied for dynamic environments. CMA algorithm executes obstacle avoidance using static potential fields around obstacles, that scale based on relative motion. Optimization strategies including interpolation and Bezier curves are applied to the algorithm. To validate effectiveness, CMA algorithm is compared with A* using static obstacles due to lack of equivalent algorithms for dynamic environments. CMA performed comparably to A* with difference ranging from -0.2% to 1.3%. CMA algorithm is applied experimentally to achieve comparable performance, with an error range of -0.5% to 5.2%. These errors are attributed to the limitations of the Kinect V1 sensor used for obstacle detection. The algorithm was finally implemented in a 3D simulated space, indicating that it is possible to apply with drones. This algorithm shows promise for application in warehouse and inventory automation, especially when the workspace is observable.
Keywords
CMA; Path Planning; Dynamic Environment; Multi Agent; Autonomous Navigation
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
