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

05/22/2023
by   Yiwei Lyu, et al.
0

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.

READ FULL TEXT

page 1

page 7

research
06/17/2022

Responsibility-associated Multi-agent Collision Avoidance with Social Preferences

This paper introduces a novel social preference-aware decentralized safe...
research
05/30/2023

Distributed Hierarchical Distribution Control for Very-Large-Scale Clustered Multi-Agent Systems

As the scale and complexity of multi-agent robotic systems are subject t...
research
01/14/2021

Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificates

We study the multi-agent safe control problem where agents should avoid ...
research
09/19/2023

Learning Adaptive Safety for Multi-Agent Systems

Ensuring safety in dynamic multi-agent systems is challenging due to lim...
research
06/23/2020

Online Multi-agent Reinforcement Learning for Decentralized Inverter-based Volt-VAR Control

The distributed Volt/Var control (VVC) methods have been widely studied ...
research
02/22/2022

Event-Triggered Tracking Control of Networked Multi-Agent Systems

This paper studies the tracking control problem of networked multi-agent...
research
02/27/2023

Safe Multi-agent Learning via Trapping Regions

One of the main challenges of multi-agent learning lies in establishing ...

Please sign up or login with your details

Forgot password? Click here to reset