Privacy Preserving Multi Agent Path Finding

TL;DR

This paper introduces privacy-preserving algorithms for multi-agent path finding, addressing planning and execution privacy constraints, with empirical evaluation demonstrating cost improvements.

Contribution

It formulates two privacy constraints for MAPF and adapts existing algorithms, providing a framework and post-processing method to enhance privacy and efficiency.

Findings

Proposed a framework for planning-level privacy using mock agents.

Adapted PIBT and LaCAM algorithms to preserve execution-level privacy.

Empirical results show significant cost reduction with post-processing.

Abstract

In the multi-agent path finding (MAPF) problem, a group of agents search in a graph for a path for each agent where no two paths collide. While in all applications of MAPF the agents must not collide with each other, in some of them the agents may not wish to share their paths due to privacy constraints. In this work, we formulate two types of privacy constraints for MAPF and propose algorithms that preserve them. The first type of privacy we consider is planning-level privacy, which means that during planning, the agents cannot identify exactly the planned location of the other agents. We propose a general framework for obtaining planning-level privacy, which works by adding mock agents to the planning process. The second type of privacy we consider is execution-level privacy, which is relevant when agents have limited sensing capabilities. Execution-level privacy is preserved if none of the agents is allowed to sense the location of the other agents during execution. We show how to adapt two popular MAPF algorithms, namely PIBT and LaCAM, such that they preserve execution-level privacy. Lastly, we propose a post-processing technique that allows the agents to reduce the sum of costs of the returned solution without losing any privacy. We also implemented our algorithms and evaluated them empirically, showing that the proposed post-processing technique indeed improved cost significantly.