Voronoi-GRU-Based Multi-Robot Collaborative Exploration in Unknown Environments
In modern society, the autonomous exploration of unknown environments has attracted extensive attention due to its broad applications, such as in search and rescue operations, planetary exploration, and environmental monitoring. This paper proposes a novel collaborative exploration strategy for mult...
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MDPI AG
2025-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/6/3313 |
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| author | Yang Lei Jian Hou Peixin Ma Mingze Ma |
| author_facet | Yang Lei Jian Hou Peixin Ma Mingze Ma |
| author_sort | Yang Lei |
| collection | DOAJ |
| description | In modern society, the autonomous exploration of unknown environments has attracted extensive attention due to its broad applications, such as in search and rescue operations, planetary exploration, and environmental monitoring. This paper proposes a novel collaborative exploration strategy for multiple mobile robots, aiming to quickly realize the exploration of entire unknown environments. Specifically, we investigate a hierarchical control architecture, comprising an upper decision-making layer and a lower planning and mapping layer. In the upper layer, the next frontier point for each robot is determined using Voronoi partitioning and the Multi-Agent Twin Delayed Deep Deterministic policy gradient (MATD3) deep reinforcement learning algorithm in a centralized training and decentralized execution framework. In the lower layer, navigation planning is achieved using A* and Timed Elastic Band (TEB) algorithms, while an improved Cartographer algorithm is used to construct a joint map for the multi-robot system. In addition, the improved Robot Operating System (ROS) and Gazebo simulation environments speed up simulation times, further alleviating the slow training of high-precision simulation engines. Finally, the simulation results demonstrate the superiority of the proposed strategy, which achieves over 90% exploration coverage in unknown environments with a significantly reduced exploration time. Compared to MATD3, Multi-Agent Proximal Policy Optimization (MAPPO), Rapidly-Exploring Random Tree (RRT), and Cost-based methods, our strategy reduces time consumption by 41.1%, 47.0%, 63.9%, and 74.9%, respectively. |
| format | Article |
| id | doaj-art-049c857eabaa4dd598f4101cd326c3c0 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-049c857eabaa4dd598f4101cd326c3c02025-08-20T02:42:41ZengMDPI AGApplied Sciences2076-34172025-03-01156331310.3390/app15063313Voronoi-GRU-Based Multi-Robot Collaborative Exploration in Unknown EnvironmentsYang Lei0Jian Hou1Peixin Ma2Mingze Ma3School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaSchool of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaBeijing Zhongke Huiling Robot Technology Co., Ltd., Beijing 100192, ChinaSchool of Information Engineering, Wenzhou Business College, Wenzhou 325035, ChinaIn modern society, the autonomous exploration of unknown environments has attracted extensive attention due to its broad applications, such as in search and rescue operations, planetary exploration, and environmental monitoring. This paper proposes a novel collaborative exploration strategy for multiple mobile robots, aiming to quickly realize the exploration of entire unknown environments. Specifically, we investigate a hierarchical control architecture, comprising an upper decision-making layer and a lower planning and mapping layer. In the upper layer, the next frontier point for each robot is determined using Voronoi partitioning and the Multi-Agent Twin Delayed Deep Deterministic policy gradient (MATD3) deep reinforcement learning algorithm in a centralized training and decentralized execution framework. In the lower layer, navigation planning is achieved using A* and Timed Elastic Band (TEB) algorithms, while an improved Cartographer algorithm is used to construct a joint map for the multi-robot system. In addition, the improved Robot Operating System (ROS) and Gazebo simulation environments speed up simulation times, further alleviating the slow training of high-precision simulation engines. Finally, the simulation results demonstrate the superiority of the proposed strategy, which achieves over 90% exploration coverage in unknown environments with a significantly reduced exploration time. Compared to MATD3, Multi-Agent Proximal Policy Optimization (MAPPO), Rapidly-Exploring Random Tree (RRT), and Cost-based methods, our strategy reduces time consumption by 41.1%, 47.0%, 63.9%, and 74.9%, respectively.https://www.mdpi.com/2076-3417/15/6/3313multiple mobile robotscollaborative explorationreinforcement learningvoronoi partitioningMATD3 |
| spellingShingle | Yang Lei Jian Hou Peixin Ma Mingze Ma Voronoi-GRU-Based Multi-Robot Collaborative Exploration in Unknown Environments Applied Sciences multiple mobile robots collaborative exploration reinforcement learning voronoi partitioning MATD3 |
| title | Voronoi-GRU-Based Multi-Robot Collaborative Exploration in Unknown Environments |
| title_full | Voronoi-GRU-Based Multi-Robot Collaborative Exploration in Unknown Environments |
| title_fullStr | Voronoi-GRU-Based Multi-Robot Collaborative Exploration in Unknown Environments |
| title_full_unstemmed | Voronoi-GRU-Based Multi-Robot Collaborative Exploration in Unknown Environments |
| title_short | Voronoi-GRU-Based Multi-Robot Collaborative Exploration in Unknown Environments |
| title_sort | voronoi gru based multi robot collaborative exploration in unknown environments |
| topic | multiple mobile robots collaborative exploration reinforcement learning voronoi partitioning MATD3 |
| url | https://www.mdpi.com/2076-3417/15/6/3313 |
| work_keys_str_mv | AT yanglei voronoigrubasedmultirobotcollaborativeexplorationinunknownenvironments AT jianhou voronoigrubasedmultirobotcollaborativeexplorationinunknownenvironments AT peixinma voronoigrubasedmultirobotcollaborativeexplorationinunknownenvironments AT mingzema voronoigrubasedmultirobotcollaborativeexplorationinunknownenvironments |