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

TL;DR

This paper introduces KC-TSS, a resilient path planning algorithm for heterogeneous robot teams that improves target search efficiency and robustness through clustering and TSP solutions, validated in simulations and real robot tests.

Contribution

The paper presents KC-TSS, a novel failure-resilient path planning algorithm that combines clustering and TSP for heterogeneous robot teams in target search tasks.

Findings

Outperforms state-of-the-art algorithms in mission time

Provides resilience against team member failures through online plan recomputation

Effective in indoor search and simulated rescue scenarios

Abstract

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