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Title	M-Set: Multi-Drone Swarm Intelligence Experimentation With Collision Avoidance Realism
ID_Doc	35845
Authors	Qin C.; Robins A.; Lillywhite-Roake C.; Pearce A.; Mehta H.; James S.; Wong T.H.; Pournaras E.
Year	2024
Published	Proceedings - Conference on Local Computer Networks, LCN
DOI	http://dx.doi.org/10.1109/LCN60385.2024.10639825
Abstract	Distributed sensing by cooperative drone swarms is crucial for several Smart City applications, such as traffic monitoring and disaster response. Using an indoor lab with inexpensive drones, a testbed supports complex and ambitious studies on these systems while maintaining low cost, rigor, and external validity. This paper introduces the Multi-drone Sensing Experimentation Testbed (M-SET), a novel platform designed to prototype, develop, test, and evaluate distributed sensing with swarm intelligence. M-SET addresses the limitations of existing testbeds that fail to emulate collisions, thus lacking realism in outdoor environments. By integrating a collision avoidance method based on a potential field algorithm, M-SET ensures collision-free navigation and sensing, further optimized via a multi-agent collective learning algorithm. Extensive evaluation demonstrates accurate energy consumption estimation and a low risk of collisions, providing a robust proof-of-concept. New insights show that M-SET has significant potential to support ambitious research with minimal cost, simplicity, and high sensing quality. © 2024 IEEE.
Author Keywords	collision avoidance; distributed sensing; drones; smart city; swarm intelligence; testbed

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