Risk-aware Safe Control for Decentralized Multi-agent Systems via Dynamic Responsibility Allocation

• URL: http://arxiv.org/abs/2305.13467v1
• Date: Mon, 22 May 2023 20:21:49 GMT
• Title: Risk-aware Safe Control for Decentralized Multi-agent Systems via Dynamic Responsibility Allocation
• Authors: Yiwei Lyu, Wenhao Luo and John M. Dolan
• Abstract summary: We present a risk-aware decentralized control framework that provides guidance on how much responsibility share an individual agent should take to avoid collisions with others. We propose a novel Control Barrier Function (CBF)-inspired risk measurement to characterize the aggregate risk agents face from potential collisions under motion uncertainty. We are able to leverage the flexibility of robots with lower risk to improve the motion flexibility for those with higher risk, thus achieving improved collective safety.
• Score: 36.52509571098292
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Decentralized control schemes are increasingly favored in various domains that involve multi-agent systems due to the need for computational efficiency as well as general applicability to large-scale systems. However, in the absence of an explicit global coordinator, it is hard for distributed agents to determine how to efficiently interact with others. In this paper, we present a risk-aware decentralized control framework that provides guidance on how much relative responsibility share (a percentage) an individual agent should take to avoid collisions with others while moving efficiently without direct communications. We propose a novel Control Barrier Function (CBF)-inspired risk measurement to characterize the aggregate risk agents face from potential collisions under motion uncertainty. We use this measurement to allocate responsibility shares among agents dynamically and develop risk-aware decentralized safe controllers. In this way, we are able to leverage the flexibility of robots with lower risk to improve the motion flexibility for those with higher risk, thus achieving improved collective safety. We demonstrate the validity and efficiency of our proposed approach through two examples: ramp merging in autonomous driving and a multi-agent position-swapping game.

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