Reinforcement Learning Driven Multi-Robot Exploration via Explicit Communication and Density-Based Frontier Search
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912171493949440 |
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| author | Calzolari, Gabriele Sumathy, Vidya Kanellakis, Christoforos Nikolakopoulos, George |
| author_facet | Calzolari, Gabriele Sumathy, Vidya Kanellakis, Christoforos Nikolakopoulos, George |
| contents | Collaborative multi-agent exploration of unknown environments is crucial for search and rescue operations. Effective real-world deployment must address challenges such as limited inter-agent communication and static and dynamic obstacles. This paper introduces a novel decentralized collaborative framework based on Reinforcement Learning to enhance multi-agent exploration in unknown environments. Our approach enables agents to decide their next action using an agent-centered field-of-view occupancy grid, and features extracted from $\text{A}^*$ algorithm-based trajectories to frontiers in the reconstructed global map. Furthermore, we propose a constrained communication scheme that enables agents to share their environmental knowledge efficiently, minimizing exploration redundancy. The decentralized nature of our framework ensures that each agent operates autonomously, while contributing to a collective exploration mission. Extensive simulations in Gymnasium and real-world experiments demonstrate the robustness and effectiveness of our system, while all the results highlight the benefits of combining autonomous exploration with inter-agent map sharing, advancing the development of scalable and resilient robotic exploration systems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_20049 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning Driven Multi-Robot Exploration via Explicit Communication and Density-Based Frontier Search Calzolari, Gabriele Sumathy, Vidya Kanellakis, Christoforos Nikolakopoulos, George Robotics I.2.9 Collaborative multi-agent exploration of unknown environments is crucial for search and rescue operations. Effective real-world deployment must address challenges such as limited inter-agent communication and static and dynamic obstacles. This paper introduces a novel decentralized collaborative framework based on Reinforcement Learning to enhance multi-agent exploration in unknown environments. Our approach enables agents to decide their next action using an agent-centered field-of-view occupancy grid, and features extracted from $\text{A}^*$ algorithm-based trajectories to frontiers in the reconstructed global map. Furthermore, we propose a constrained communication scheme that enables agents to share their environmental knowledge efficiently, minimizing exploration redundancy. The decentralized nature of our framework ensures that each agent operates autonomously, while contributing to a collective exploration mission. Extensive simulations in Gymnasium and real-world experiments demonstrate the robustness and effectiveness of our system, while all the results highlight the benefits of combining autonomous exploration with inter-agent map sharing, advancing the development of scalable and resilient robotic exploration systems. |
| title | Reinforcement Learning Driven Multi-Robot Exploration via Explicit Communication and Density-Based Frontier Search |
| topic | Robotics I.2.9 |
| url | https://arxiv.org/abs/2412.20049 |