SPARCAS: A Decentralized, Truthful Multi-Agent Collision-free Path Finding Mechanism

TL;DR

This paper introduces SPARCAS, a decentralized, truthful mechanism for multi-robot collision avoidance that scales well and incentivizes honest information sharing, ensuring deadlock-free, efficient paths in competitive environments.

Contribution

The paper presents SPARCAS, a novel decentralized mechanism that guarantees truthful reporting and efficient collision-free paths for competitive robots, unlike existing centralized or collaborative approaches.

Findings

SPARCAS ensures collision-free, deadlock-free robot movement.

The mechanism scales efficiently with many robots.

It maintains near-optimal path quality despite decentralization.

Abstract

We propose a decentralized collision-avoidance mechanism for a group of independently controlled robots moving on a shared workspace. Existing algorithms achieve multi-robot collision avoidance either (a) in a centralized setting, or (b) in a decentralized setting with collaborative robots. We focus on the setting with competitive robots in a decentralized environment, where robots may strategically reveal their information to get prioritized. We propose the mechanism SPARCAS in this setting that, using principles of mechanism design, ensures truthful revelation of the robots' private information and provides locally efficient movement of the robots. It is free from collisions and deadlocks, and handles a dynamic arrival of robots. In practice, this mechanism scales well for a large number of robots where the optimal collision-avoiding path-finding algorithm (M*) does not scale. Yet, SPARCAS does not compromise the path optimality too much. Our mechanism prioritizes the robots in the order of their `true' higher needs, but for a higher payment. It uses monetary transfers which is small enough compared to the value received by the robots.