Champagnie, Kale; Chen, Boli; Arvin, Farshad; Hu, Junyan; (2024) Online Multi-Robot Coverage Path Planning in Dynamic Environments Through Pheromone-Based Reinforcement Learning. In: 2024 IEEE 20th International Conference on Automation Science and Engineering. IEEE (In press).

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Abstract

Two promising approaches to coverage path planning are reward-based and pheromone-based methods. Rewardbased methods allow heuristics to be learned automatically, often yielding a superior performance over hand-crafted rules. On the other hand, pheromone-based methods consistently demonstrate superior generalization and adaptation abilities when placed in unfamiliar 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 antlike 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 basic necessities such as memory and explicit communication. Finally, we show that GEM can be executed asynchronously in constant-time, enabling it to scale arbitrarily.

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