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This paper proposes KC-TSS: K-Clustered-Traveling Salesman Based Search, afailure resilient path planning algorithm for heterogeneous robot teamsperforming target search in human environments. We separate the sample pathgeneration problem into Heterogeneous Clustering and multiple TravelingSalesman Problems. This allows us to provide high-quality candidate paths (i.e.minimal backtracking, overlap) to an Information-Theoretic utility function foreach agent. First, we generate waypoint candidates from map knowledge and atarget prediction model. All of these candidates are clustered according to thenumber of agents and their ability to cover space, or coverage competency. Eachagent solves a Traveling Salesman Problem (TSP) instance over their assignedcluster and then candidates are fed to a utility function for path selection.We perform extensive Gazebo simulations and preliminary deployment of realrobots in indoor search and simulated rescue scenarios with static targets. Wecompare our proposed method against a state-of-the-art algorithm and show thatours is able to outperform it in mission time. Our method provides resiliencein the event of single or multi teammate failure by recomputing global teamplans online.

KC-TSS: An Algorithm for Heterogeneous Robot Teams Performing Resilient Target Search