Conference Paper/Proceeding/Abstract 101 views 1 download
Predator-Prey Q-Learning Based Collaborative Coverage Path Planning for Swarm Robotics
Michael Watson,
Hans Ren,
Farshad Arvin,
Junyan Hu
Towards Autonomous Robotic Systems
Swansea University Author: Hans Ren
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Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy (rights retention).
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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 |
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2024
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URI: | https://cronfa.swan.ac.uk/Record/cronfa66908 |
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v2 66908 2024-06-28 Predator-Prey Q-Learning Based Collaborative Coverage Path Planning for Swarm Robotics 9e043b899a2b786672a28ed4f864ffcc Hans Ren Hans Ren true false 2024-06-28 MACS 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 stan- dard Q-learning based CPP that often fall into a local optimum and may be in-efficient in large-scale environments, two methods of improve- ment 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 simulat- ing 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. Conference Paper/Proceeding/Abstract Towards Autonomous Robotic Systems 28 6 2024 2024-06-28 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University 2024-08-08T13:59:13.1037536 2024-06-28T19:24:58.2731592 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Michael Watson 1 Hans Ren 2 Farshad Arvin 3 Junyan Hu 4 66908__30779__04553fc7c7c942afa64a946d8eeb66d8.pdf Michael_TAROS.pdf 2024-06-28T19:28:46.8381822 Output 642046 application/pdf Accepted Manuscript true 2024-07-28T00:00:00.0000000 Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy (rights retention). true eng https://creativecommons.org/licenses/by/4.0/deed.en |
title |
Predator-Prey Q-Learning Based Collaborative Coverage Path Planning for Swarm Robotics |
spellingShingle |
Predator-Prey Q-Learning Based Collaborative Coverage Path Planning for Swarm Robotics Hans Ren |
title_short |
Predator-Prey Q-Learning Based Collaborative Coverage Path Planning for Swarm Robotics |
title_full |
Predator-Prey Q-Learning Based Collaborative Coverage Path Planning for Swarm Robotics |
title_fullStr |
Predator-Prey Q-Learning Based Collaborative Coverage Path Planning for Swarm Robotics |
title_full_unstemmed |
Predator-Prey Q-Learning Based Collaborative Coverage Path Planning for Swarm Robotics |
title_sort |
Predator-Prey Q-Learning Based Collaborative Coverage Path Planning for Swarm Robotics |
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9e043b899a2b786672a28ed4f864ffcc |
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9e043b899a2b786672a28ed4f864ffcc_***_Hans Ren |
author |
Hans Ren |
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Michael Watson Hans Ren Farshad Arvin Junyan Hu |
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Towards Autonomous Robotic Systems |
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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 stan- dard Q-learning based CPP that often fall into a local optimum and may be in-efficient in large-scale environments, two methods of improve- ment 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 simulat- ing 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. |
published_date |
2024-06-28T13:59:12Z |
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1806824323971809280 |
score |
11.021648 |