Complete coverage planning with clustering method for autonomous mobile robots

Abstract

SummaryComplete coverage planning (CCP) is a task to cover the entire area on the map, according to the job description of the autonomous mobile robot. The most widely used method for CCP in the literature is the grid‐based coverage method. In this method, the problem is processing the partially filled cell as completely filled, which reduces the coverage performance. The ability to use the clustering method, which will be created by considering the characteristics of the environment, was determined as a research question to solve this problem. In this direction, it is aimed to use K‐means++ algorithm, which is a widely used clustering algorithm and segmentation technique. In this context, an offline K‐means++ complete coverage planning (Km++CCP) method, in which the navigable area on the map of the indoor where a mobile robot will navigate is clustered using the K‐means++ algorithm and the centroids can be used as waypoints, is proposed. To test the proposed method, 2 simulations and 36 real‐world experiments were conducted. The indoor coverage ratio of Km++CCP was calculated higher than the grid‐based method in all experiments.

Conventional, Heuristic and Learning-Based Robot Motion Planning: Reviewing Frameworks of Current Practical Significance

“…However, its memory usage may be significantly high, due to the fact that the map resolution is not adjusted with respect to the actual environment's complexity, including the shapes and positions of the obstacles. Moreover, it is prone to nonoptimality, due to possible inefficient organization of the cells [201]. Other variants of grid-based algorithms include quadtree [202], Circle Grid Trajectory Cell (CGTC) [203], A* [203,204], Anytime Repairing A* (ARA*) [205,206], and D* [207].…”