Precomputing Multi-Agent Path Replanning using Temporal Flexibility

TL;DR

The paper introduces FlexSIPP, an efficient precomputation-based method for multi-agent path replanning that leverages temporal flexibility to handle delays without cascading replan costs.

Contribution

It presents a novel algorithm that precomputes plans considering temporal flexibility, enabling quick replanning in multi-agent systems with delays.

Findings

FlexSIPP efficiently handles delays in multi-agent plans.

The method reduces cascading replan costs.

Experiments show real-world applicability in railway networks.

Abstract

Executing a multi-agent plan can be challenging when an agent is delayed, because this typically creates conflicts with other agents. So, we need to quickly find a new safe plan. Replanning only the delayed agent often does not yield an efficient plan, and sometimes cannot even yield a feasible one. On the other hand, replanning other agents may lead to a cascade of changes and delays and is computationally expensive. We show how to efficiently replan by tracking and using the temporal flexibility of other agents while avoiding cascading delays. This flexibility is the maximum delay an agent can take without changing the order of other agents or further delaying them. Our algorithm, FlexSIPP, precomputes all possible plans for the delayed agent and returns the changes to the other agents for any single-agent delay within the given scenario. We demonstrate our method in a real-world case study of replanning trains in the densely-used Dutch railway network and in the MovingAI benchmark set. Our experiments show that FlexSIPP provides effective solutions relevant to real-world adjustments, and within a reasonable timeframe.