M-SET: Multi-Drone Swarm Intelligence Experimentation with Collision Avoidance Realism
<|reference_start|>M-SET: Multi-Drone Swarm Intelligence Experimentation with Collision Avoidance Realism: 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.<|reference_end|>
@article{qin2024m-set:,
    title={M-SET: Multi-Drone Swarm Intelligence Experimentation with Collision
  Avoidance Realism},
    author={Chuhao Qin, Alexander Robins, Callum Lillywhite-Roake, Adam Pearce,
  Hritik Mehta, Scott James, Tsz Ho Wong, and Evangelos Pournaras},
    journal={arXiv preprint arXiv:2406.10916},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10916},
    primaryClass={cs.RO cs.DC}
}