Optimal Partitioning of Non-Convex Environments for Minimum Turn Coverage Planning
This addresses the challenge of efficient robot navigation in indoor environments for applications like cleaning or inspection, though it is incremental as it builds on existing coverage planning methods.
The paper tackles the problem of planning optimal coverage paths for indoor robots by minimizing turns, which degrade performance, and proposes a method that computes the optimal number of axis-parallel coverage lines in polynomial time using a linear program, achieving improvements in path efficiency compared to state-of-the-art approaches.
In this paper, we tackle the problem of planning an optimal coverage path for a robot operating indoors. Many existing approaches attempt to discourage turns in the path by covering the environment along the least number of coverage lines, i.e., straight-line paths. This is because turning not only slows down the robot but also negatively affects the quality of coverage, e.g., tools like cameras and cleaning attachments commonly have poor performance around turns. The problem of minimizing coverage lines however is typically solved using heuristics that do not guarantee optimality. In this work, we propose a turn-minimizing coverage planning method that computes the optimal number of axis-parallel (horizontal/vertical) coverage lines for the environment in polynomial time. We do this by formulating a linear program (LP) that optimally partitions the environment into axis-parallel ranks (non-intersecting rectangles of width equal to the tool width). We then generate coverage paths for a set of real-world indoor environments and compare the results with state-of-the-art coverage approaches.