A Predictive Cooperative Collision Avoidance for Multi-Robot Systems Using Control Barrier Function

TL;DR

This paper introduces a predictive control barrier function approach for multi-robot collision avoidance, enhancing safety guarantees by considering future states and effectively managing deadlocks with minimal detours.

Contribution

It develops a predictive safety matrix and a deadlock escape strategy integrated into trajectory tracking, advancing safety and efficiency in multi-robot systems.

Findings

Robustness to measurement uncertainty

Immunity to oscillations

Effective deadlock avoidance without large detours

Abstract

Control barrier function (CBF)-based methods provide the minimum modification necessary to formally guarantee safety in the context of quadratic programming, and strict safety guarantee for safety critical systems. However, most CBF-related derivatives myopically focus on present safety at each time step, a reasoning over a look-ahead horizon is exactly missing. In this paper, a predictive safety matrix is constructed. We then consolidate the safety condition based on the smallest eigenvalue of the proposed safety matrix. A predefined deconfliction strategy of motion paths is embedded into the trajectory tracking module to manage deadlock conflicts, which computes the deadlock escape velocity with the minimum attitude angle. Comparison results show that the introduction of the predictive term is robust for measurement uncertainty and is immune to oscillations. The proposed deadlock avoidance method avoids a large detour, without obvious stagnation.