Multi-Robot Segregation Using Finite-Time MPC with Chernoff Bound-Based Asynchronous Motion Smoothing

Abstract

For multi-robot systems operating in dynamic environments, collision-free segregation into a desired set of groups in finite time is an essential task requirement in many applications. This work presents a control framework for such systems, utilizing Finite-time Model Predictive Control. The objective is to guide the robots toward a segregated formation while adhering to leader-follower dynamics and effectively avoiding collisions. To ensure finite-time convergence, the concept of a control invariant set is incorporated. Furthermore, the paper derives an upper bound on the required time steps for the robots to achieve the segregated formation. In order to maintain a smooth motion profile in the face of external state perturbations, this work proposes a data-driven Chernoff bound-based triggering method that enables Asynchronous Motion Smoothing for the robots. To validate the effectiveness of the proposed control framework, both simulations and hardware experiments are conducted, focusing on the segregation of five robots into two distinct groups.