Lightweight Decentralized Neural Network-Based Strategies for Multi-Robot Patrolling
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914047845203968 |
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| author | Ward, James C. McConville, Ryan Hunt, Edmund R. |
| author_facet | Ward, James C. McConville, Ryan Hunt, Edmund R. |
| contents | The problem of decentralized multi-robot patrol has previously been approached primarily with hand-designed strategies for minimization of 'idlenes' over the vertices of a graph-structured environment. Here we present two lightweight neural network-based strategies to tackle this problem, and show that they significantly outperform existing strategies in both idleness minimization and against an intelligent intruder model, as well as presenting an examination of robustness to communication failure. Our results also indicate important considerations for future strategy design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_11916 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Lightweight Decentralized Neural Network-Based Strategies for Multi-Robot Patrolling Ward, James C. McConville, Ryan Hunt, Edmund R. Robotics The problem of decentralized multi-robot patrol has previously been approached primarily with hand-designed strategies for minimization of 'idlenes' over the vertices of a graph-structured environment. Here we present two lightweight neural network-based strategies to tackle this problem, and show that they significantly outperform existing strategies in both idleness minimization and against an intelligent intruder model, as well as presenting an examination of robustness to communication failure. Our results also indicate important considerations for future strategy design. |
| title | Lightweight Decentralized Neural Network-Based Strategies for Multi-Robot Patrolling |
| topic | Robotics |
| url | https://arxiv.org/abs/2412.11916 |