Human-robot collaborative topological exploration for search and rescue applications

Abstract

We address the coordination between humans and robots in tasks that involve exploration and reconnaissance with applications to search and rescue. Specifically, we consider the problem of humans and robots cooperatively searching an indoor environment in a distributed manner where we assume that each robot is equipped with sensors that are able to locate targets of interest. Rather than have humans issue explicit commands to and guide robots, we allow humans to make decisions on their own and let the robots adapt to decisions taken by the human. The main contribution of this paper is a framework in which the robots in the team respond and adapt to the behavior of the human agents in the task of exploring and clearing an indoor environment. The central idea is the assignment of robots to homotopy classes that are complementary to the classes being pursued by human agents. By the virtue of the sparse topological representation of the agent trajectories, our algorithm lends itself naturally to a distributed implementation. The framework has three advantages: it (a) ensures that robots and humans pursue different homotopy classes; (b) requires very little communication between the humans and the robots; and (c) allows robots to adapt to human movement without having to model complex human decision-making behaviors.We demonstrate the effectiveness of the proposed algorithm through a distributed implementation on a ROS (Robot Operating System) platform.