Robust and Safe Task-Driven Planning and Navigation for Heterogeneous Multi-Robot Teams with Uncertain Dynamics

Task and motion planning (TAMP) can enhance intelligent multi-robot coordination. TAMP becomes signifi-cantly more complicated in obstacle-cluttered environments and in the presence of robot dynamic uncertainties. We propose a control framework that solves the motion-planning problem for multi-robot teams with uncertain dynamics, addressing a key component of the TAMP pipeline. The principal part of the proposed algorithm constitutes a decentralized feedback control policy for tracking of reference paths taken by the robots while avoiding collision and adapting in real time to the underlying dynamic uncertainties. The proposed framework further leverages sampling-based motion planners to free the robots from local-minimum configurations. Extensive experimental results in complex, realistic environments illustrate the superior efficiency of the proposed approach, in terms of planning time and number of encountered local minima, with respect to state-of-the-art baseline methods.

I. INTRODUCTION

Modern robotics applications require multiple heterogeneous robots to safely execute complex tasks in obstacle-cluttered environments and over long horizons. The robots must be able to reason about high-level tasks and derive and track successfully the respective low-level paths. The complexity of solving such high-dimensional continuous problems explodes with more robots and goals. At the same time, robots evolve subject to dynamics that often suffer from uncertainties and unknown exogenous disturbances that need to be addressed by the underlying algorithms. Task and Motion Planning (TAMP) [1] has become a popular paradigm as it interleaves both high-level reasoning and low-level motion planning to find feasible paths that reach the high-level goal efficiently, while feedback control is often used to accommodate the robot dynamics [2], [3].