Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning
Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning
复制标题
基于voronoi的多机器人在未知环境下的深度强化学习自主探索
DOI：
10.1109/tvt.2020.3034800
复制
发表时间：
2020-12-01
影响因子：
6.8
通讯作者：
Arvin, Farshad
中科院分区：
文献类型：
--
作者：
Hu, Junyan;Niu, Hanlin;Arvin, Farshad
自主探索是多车辆系统的一个重要应用，其中一组网络机器人协调合作地探索未知环境。这项技术已经赢得了显着的研究兴趣，由于其实用性在搜索和救援，故障检测和监测，定位和地图，在本文中，提出了一种新的合作探索策略，多个移动的机器人，减少了整体任务完成时间和能源成本相比，传统的方法。为了有效地导航网络化机器人在协作任务，分层控制结构的设计，其中包括一个高层决策层和一个低层次的目标跟踪层。建议的合作探索方法开发使用动态Voronoi分区，最大限度地减少重复的探索区域，通过分配不同的目标位置，个别机器人。针对未知环境中的突发障碍物，提出了一种基于深度强化学习的集成避碰算法，使控制策略能够从人体演示数据中学习，从而提高学习速度和性能。最后，仿真和实验结果证明了该方案的有效性。
Autonomous exploration is an important application of multi-vehicle systems, where a team of networked robots are coordinated to explore an unknown environment collaboratively. This technique has earned significant research interest due to its usefulness in search and rescue, fault detection and monitoring, localization and mapping, etc. In this paper, a novel cooperative exploration strategy is proposed for multiple mobile robots, which reduces the overall task completion time and energy costs compared to conventional methods. To efficiently navigate the networked robots during the collaborative tasks, a hierarchical control architecture is designed which contains a high-level decision making layer and a low-level target tracking layer. The proposed cooperative exploration approach is developed using dynamic Voronoi partitions, which minimizes duplicated exploration areas by assigning different target locations to individual robots. To deal with sudden obstacles in the unknown environment, an integrated deep reinforcement learning based collision avoidance algorithm is then proposed, which enables the control policy to learn from human demonstration data and thus improve the learning speed and performance. Finally, simulation and experimental results are provided to demonstrate the effectiveness of the proposed scheme.