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Predator-Prey Q-Learning Based Collaborative Coverage Path Planning for Swarm Robotics
Towards Autonomous Robotic Systems: 25th Annual Conference, TAROS 2024, London, UK, August 21–23, 2024, Proceedings, Part II, Pages: 320 - 332
Swansea University Author: Hanchi Ren
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DOI (Published version): 10.1007/978-3-031-72062-8_28
Abstract
Coverage Path Planning (CPP) is an effective approach to let intelligent robots cover an area by finding feasible paths through the environment. In this paper, we focus on using reinforcement learning to learn about a given environment and find the most efficient path that explores all target points...
| Published in: | Towards Autonomous Robotic Systems: 25th Annual Conference, TAROS 2024, London, UK, August 21–23, 2024, Proceedings, Part II |
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| ISBN: | 9783031720611 9783031720628 |
| ISSN: | 0302-9743 1611-3349 |
| Published: |
Cham
Springer
2025
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa66908 |
| Abstract: |
Coverage Path Planning (CPP) is an effective approach to let intelligent robots cover an area by finding feasible paths through the environment. In this paper, we focus on using reinforcement learning to learn about a given environment and find the most efficient path that explores all target points. To overcome the limitations caused by standard Q-learning based CPP that often fall into a local optimum and may be in-efficient in large-scale environments, two methods of improvement are considered, i.e., the use of a robot swarm working towards the same goal and the augmenting of the Q-learning algorithm to include a predator-prey based reward system. Existing predator-prey based reward systems provide rewards the further away an agent is from its predator, the paper adapts this concept to work within a robot swarm by simulating each agent of the swarm as both predator and prey. Simulation case studies and comparisons with the standard Q-learning show that the proposed method has a superior coverage performance in complicated environments. |
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| Item Description: |
Lecture Notes in Computer Science (LNAI, volume 15052) |
| Keywords: |
Coverage Path Planning; Reinforcement Learning; Swarm Robotics |
| College: |
Faculty of Science and Engineering |
| Funders: |
This work was supported by EU H2020-FET-OPEN RoboRoyale project [grant number 964492]. |
| Start Page: |
320 |
| End Page: |
332 |