Online Multi-Robot Coverage Path Planning in Dynamic Environments Through Pheromone-Based Reinforcement Learning

Champagnie, Kale; Chen, Boli; Arvin, Farshad; Hu, Junyan

Abstract

Two promising approaches to coverage path planning are reward-based and pheromone-based methods. Reward-based methods allow heuristics to be learned automatically, often yielding a superior performance to hand-crafted rules. On the other hand, pheromone-based methods leverage stimgergy to achieve superior generalization and adaptation in unknown or nonstationary environments. To obtain the best of both worlds, we introduce Greedy Entropy Maximization (GEM), a hybrid approach that aims to maximize the entropy of a pheromone deposited by a swarm of homogeneous ant-like agents. We begin by establishing a sharp upper-bound on achievable entropy and show that this corresponds to optimal dynamic coverage path planning. Next, we demonstrate that GEM closely approaches this upper-bound despite depriving agents of typical necessities such as memory and explicit communication. Finally, we show that GEM can be executed asynchronously in constant-time through distillation into a shallow neural network, making our approach highly scalable.

Citation

Champagnie, K., Chen, B., Arvin, F., & Hu, J. (2024, August). Online Multi-Robot Coverage Path Planning in Dynamic Environments Through Pheromone-Based Reinforcement Learning. Presented at 2024 IEEE International Conference on Automation Science and Engineering (CASE), Bari, Italy

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2024 IEEE International Conference on Automation Science and Engineering (CASE)
• Start Date: Aug 28, 2024
• End Date: Aug 31, 2024
• Acceptance Date: Jun 3, 2024
• Online Publication Date: Oct 23, 2024
• Publication Date: Oct 23, 2024
• Deposit Date: Aug 9, 2024
• Publicly Available Date: Oct 23, 2024
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Pages: 1000-1005
• Series ISSN: 2161-8070
• Book Title: 2024 IEEE International Conference on Automation Science and Engineering (CASE)
• DOI: https://doi.org/10.1109/CASE59546.2024.10711550
• Public URL: https://durham-repository.worktribe.com/output/2745380