Abstract
In the evolving era of social robots, managing a swarm of autonomous agents to perform particular tasks has become essential for numerous industries. The task becomes more challenging for large-scale swarms and complex environments, which have not been fully explored yet. Therefore, this research introduces a methodology incorporating multiple coordinated robotic shepherds to effectively guide large-scale agent swarms in obstacle-laden terrains. The proposed framework commences with deploying an unsupervised machine-learning algorithm to categorise the swarm into clusters. Then, a shepherding algorithm with coordinated robotic shepherds drives the sub-swarms towards the goal. Also, a path planner based on an evolutionary algorithm is proposed to help robotic shepherds move in a way that minimises the dispersion of each sub-swarm and avoids potential hazards and obstructions. The proposed approach is tested on different scenarios, with the results showing a success rate of 100% in guiding swarms with sizes up to 3000 agents.
This research is supported by UNSW Rector’s start-up grant (No. PS48058) and the U.S. Office of Naval Research-Global (ONR-G).
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Elsayed, S., Mabrok, M. (2024). Large-Scale Swarm Control in Cluttered Environments. In: Ali, A.A., et al. Social Robotics. ICSR 2023. Lecture Notes in Computer Science(), vol 14453 . Springer, Singapore. https://doi.org/10.1007/978-981-99-8715-3_32
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DOI: https://doi.org/10.1007/978-981-99-8715-3_32
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