Chen Qihong, Wang Rui, Lyu Ming, Zhang Jie
School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China.
Sensors (Basel). 2024 Aug 6;24(16):5083. doi: 10.3390/s24165083.
A map of the environment is the basis for the robot's navigation. Multi-robot collaborative autonomous exploration allows for rapidly constructing maps of unknown environments, essential for application areas such as search and rescue missions. Traditional autonomous exploration methods are inefficient due to the repetitive exploration problem. For this reason, we propose a multi-robot autonomous exploration method based on the Transformer model. Our multi-agent deep reinforcement learning method includes a multi-agent learning method to effectively improve exploration efficiency. We conducted experiments comparing our proposed method with existing methods in a simulation environment, and the experimental results showed that our proposed method had a good performance and a specific generalization ability.
环境地图是机器人导航的基础。多机器人协作自主探索能够快速构建未知环境的地图，这对于搜索和救援任务等应用领域至关重要。由于存在重复探索问题，传统的自主探索方法效率低下。因此，我们提出了一种基于Transformer模型的多机器人自主探索方法。我们的多智能体深度强化学习方法包括一种多智能体学习方法，以有效提高探索效率。我们在模拟环境中进行了实验，将我们提出的方法与现有方法进行比较，实验结果表明我们提出的方法具有良好的性能和特定的泛化能力。
