Responsibility-associated Multi-agent Collision Avoidance with Social Preferences

TL;DR

This paper presents a decentralized control framework for multi-agent collision avoidance that incorporates social preferences and responsibility sharing, enabling safe and semi-cooperative interactions among heterogeneous agents.

Contribution

It introduces Responsibility-associated Social Value Orientation (R-SVO) and Local Pairwise Responsibility Weights to model responsibility sharing in asymmetric multi-agent collision avoidance.

Findings

Framework achieves formal safety guarantees.

Effective in diverse multi-agent navigation scenarios.

Enhances cooperation with responsibility-aware social preferences.

Abstract

This paper introduces a novel social preference-aware decentralized safe control framework to address the responsibility allocation problem in multi-agent collision avoidance. Considering that agents do not necessarily cooperate in symmetric ways, this paper focuses on semi-cooperative behavior among heterogeneous agents with varying cooperation levels. Drawing upon the idea of Social Value Orientation (SVO) for quantifying the individual selfishness, we propose a novel concept of Responsibility-associated Social Value Orientation (R-SVO) to express the intended relative social implications between pairwise agents. This is used to redefine each agent's social preferences or personalities in terms of corresponding responsibility shares in contributing to the coordination scenario, such as semi-cooperative collision avoidance where all agents interact in an asymmetric way. By incorporating such relative social implications through proposed Local Pairwise Responsibility Weights, we develop a Responsibility-associated Control Barrier Function-based safe control framework for individual agents, and multi-agent collision avoidance is achieved with formally provable safety guarantees. Simulations are provided to demonstrate the effectiveness and efficiency of the proposed framework in several multi-agent navigation tasks, such as a position-swapping game, a self-driving car highway ramp merging scenario, and a circular position swapping game.