Department of Electrical Engineering, Advanced Institute of Manufacturing with High-Tech Innovation, National Chung Cheng University, Chiayi 621, Taiwan.
Department of Electrical Engineering, National Chung Cheng University, Chiayi 621, Taiwan.
Sensors (Basel). 2021 May 26;21(11):3709. doi: 10.3390/s21113709.
In mobile robotics research, the exploration of unknown environments has always been an important topic due to its practical uses in consumer and military applications. One specific interest of recent investigation is the field of complete coverage and path planning (CCPP) techniques for mobile robot navigation. In this paper, we present a collaborative CCPP algorithms for single robot and multi-robot systems. The incremental coverage from the robot movement is maximized by evaluating a new cost function. A goal selection function is then designed to facilitate the collaborative exploration for a multi-robot system. By considering the local gains from the individual robots as well as the global gain by the goal selection, the proposed method is able to optimize the overall coverage efficiency. In the experiments, our CCPP algorithms are carried out on various unknown and complex environment maps. The simulation results and performance evaluation demonstrate the effectiveness of the proposed collaborative CCPP technique.
在移动机器人研究中，由于其在消费和军事应用中的实际用途，探索未知环境一直是一个重要的课题。最近的研究特别关注移动机器人导航的全覆盖和路径规划（CCPP）技术领域。在本文中，我们提出了一种用于单机器人和多机器人系统的协作 CCPP 算法。通过评估新的成本函数，最大化了机器人运动的增量覆盖范围。然后设计了一个目标选择函数，以方便多机器人系统的协作探索。通过考虑单个机器人的局部收益以及目标选择的全局收益，所提出的方法能够优化整体覆盖效率。在实验中，我们的 CCPP 算法在各种未知和复杂的环境地图上进行了测试。仿真结果和性能评估证明了所提出的协作 CCPP 技术的有效性。
