In this paper we present a control barrier functionbased (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.

Multi-Robot Adversarial Resilience using Control Barrier Functions / Cavorsi, Mcavorsi@G. Harvard. Edu M.; Capelli, B.; Sabattini, L.; Gil, Sgil@Seas. Harvard. Edu S.. - (2022). (Intervento presentato al convegno 18th Robotics: Science and Systems, RSS 2022 tenutosi a usa nel 2022) [10.15607/RSS.2022.XVIII.053].

Multi-Robot Adversarial Resilience using Control Barrier Functions

Capelli B.;Sabattini L.;
2022

Abstract

In this paper we present a control barrier functionbased (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.
2022
18th Robotics: Science and Systems, RSS 2022
usa
2022
Cavorsi, Mcavorsi@G. Harvard. Edu M.; Capelli, B.; Sabattini, L.; Gil, Sgil@Seas. Harvard. Edu S.
Multi-Robot Adversarial Resilience using Control Barrier Functions / Cavorsi, Mcavorsi@G. Harvard. Edu M.; Capelli, B.; Sabattini, L.; Gil, Sgil@Seas. Harvard. Edu S.. - (2022). (Intervento presentato al convegno 18th Robotics: Science and Systems, RSS 2022 tenutosi a usa nel 2022) [10.15607/RSS.2022.XVIII.053].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1329231
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