Decentralized Robust Collision-Avoidance for Cooperative Multirobot Systems: A Gaussian Process-Based Control Barrier Function Approach

Data-based machine learning methods have been successfully applied to control system design in recent years. However, safety during the learning and control process is difficult to guarantee due to the inherent uncertainties. In this work, we propose a barrier-function-based robust cooperative collision-avoidance control framework for heterogeneous multirobot systems with model learning. First, a new control barrier function (CBF) design is proposed for cooperative collision-avoidance, which leads to less conservativeness of the feasible control actions. Then, decentralized robust CBF conditions are derived, which incorporate the estimation of the individual model uncertainty using Gaussian process models such that each robot can guarantee safety with a high probability. Finally, a quadratic programming problem is formulated to obtain a controller that is minimally invasive to the nominal controller and satisfies the CBF and velocity constraints simultaneously. The decentralized implementation of the robust collision-avoidance control strategy is explicitly shown and proven. Two simulation examples are given to demonstrate the effectiveness of the proposed control framework.

Automated summary

Data-based machine learning methods have been used in control system design, but safety is a challenge due to uncertainties. This study proposes a barrier-function-based robust cooperative collision-avoidance control framework for heterogeneous multirobot systems. A new control barrier function design is proposed for less conservative feasible control actions. Decentralized robust conditions are derived, incorporating individual model uncertainty estimation to ensure safety. The proposed control framework is demonstrated effective through simulation examples.