Champagnie, Kale;
Chen, Boli;
Arvin, Farshad;
Hu, Junyan;
(2024)
Online Multi-Robot Coverage Path Planning in Dynamic Environments Through Pheromone-Based Reinforcement Learning.
In:
Proceedings of the 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE).
(pp. pp. 1000-1005).
Institute of Electrical and Electronics Engineers (IEEE): Bari, Italy.
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Online Multi-Robot Coverage Path Planning in Dynamic Environments Through Pheromone-Based Reinforcement Learning |
| Event: | 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE) |
| Location: | Bari, Italy |
| Dates: | 28th August - 1st September 2024 |
| ISBN-13: | 979-8-3503-5851-3 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/CASE59546.2024.10711550 |
| Publisher version: | https://doi.org/10.1109/CASE59546.2024.10711550 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Computer aided software engineering, Automation, Neural networks, Reinforcement learning, Path planning, Entropy |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10193055 |
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