Multiregion Inspection by Combining Clustered Traveling Salesman Tours With Sampling-Based Motion Planning

Abstract

This paper develops an efficient approach to generate a collision-free and dynamically feasible trajectory that enables a robotic vehicle to inspect the entire workspace or a subset consisting of one or several regions. The approach makes it possible to specify constraints on the order in which the regions should be inspected by using colors to ensure that regions with the same color are inspected as a group. A key aspect is the transformation of the multiregion inspection into a clustered traveling salesman problem (CTSP). This is achieved by generating several inspection points on the medial axis of each region to increase the visibility and by grouping the inspection points into clusters. We also develop a fast branch-and-bound CTSP solver to find low-cost clustered tours. These tours guide sampling-based motion planning, as it expands a motion tree in search for a collision-free and dynamically feasible trajectory to carry out the multiregion inspection. The approach is evaluated in simulation using a snake-like robot model to carry out inspection tasks in complex environments. Comparisons to related work show significant speedups.