Multi-Robot Adversarial Resilience using Control Barrier Functions

Matthew Cavorsi (Harvard University), Beatrice Capelli (University of Modena and Reggio Emilia), Lorenzo Sabattini (University of Modena and Reggio Emilia), Stephanie Gil (Harvard University)
	Paper #053

Abstract

In this paper we present a control barrier function-based (CBF) resilience controller that provides resilience in a multi-robot network to adversaries. Previous approaches provide resilience by virtue of specific linear combinations of multiple control constraints. These combinations can be difficult to find and are sensitive to the addition of new constraints. Unlike previous approaches, the proposed CBF provides network resilience and is easily amenable to multiple other control constraints, such as collision and obstacle avoidance. The inclusion of such constraints is essential in order to implement a resilience controller on realistic robot platforms. We demonstrate the viability of the CBF-based resilience controller on real robotic systems through case studies on a multi-robot flocking problem in cluttered environments with the presence of adversarial robots.