Benchmarking Multi-Robot Coordination in Realistic, Unstructured Human-Shared Environments

Coordinating a fleet of robots in unstructured, human-shared environments is challenging. Human behavior is hard to predict, and its uncertainty impacts the performance of the robotic fleet. Various multi-robot planning and coordination algorithms have been proposed, including Multi-Agent Path Finding (MAPF) methods to precedence-based algorithms. However, it is still unclear how human presence impacts different coordination strategies in both simulated environments and the real world. With the goal of studying and further improving multi-robot planning capabilities in those settings, we propose a method to develop and benchmark different multi-robot coordination algorithms in realistic, unstructured and human-shared environments. To this end, we introduce a multi-robot benchmark framework that is based on state-of-the-art open-source navigation and simulation frameworks and can use different types of robots, environments and human motion models. We show a possible application of the benchmark framework with two different environments and three centralized coordination methods (two MAPF algorithms and a loosely-coupled coordination method based on precedence constraints). We evaluate each environment for different human densities to investigate its impact on each coordination method. We also present preliminary results that show how informing each coordination method about human presence can help the coordination method to find faster paths for the robots.