Risk-aware Safe Control for Decentralized Multi-agent Systems via Dynamic Responsibility Allocation
This addresses safety and efficiency challenges in decentralized multi-agent systems, such as autonomous vehicles, but is incremental as it builds on existing Control Barrier Function methods.
The paper tackles the problem of decentralized multi-agent control by introducing a risk-aware framework that dynamically allocates responsibility shares among agents to avoid collisions without direct communication, achieving improved collective safety in examples like autonomous driving ramp merging and position-swapping games.
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.