Should I Replan? Learning to Spot the Right Time in Robust MAPF Execution

TL;DR

This paper introduces a neural network-based method to predict when replanning in robust MAPF execution is beneficial, aiming to reduce delays and improve safety with minimal additional computation.

Contribution

It proposes a neural network model that estimates the benefit of replanning in MAPF, trained on a large dataset with ADG-based features, to optimize execution efficiency.

Findings

The method can reduce delay impacts by up to 94.6%.

The neural network accurately predicts when replanning is advantageous.

The approach effectively balances safety and efficiency in MAPF execution.

Abstract

During the execution of Multi-Agent Path Finding (MAPF) plans in real-life applications, the MAPF assumption that the fleet's movement is perfectly synchronized does not apply. Since one or more of the agents may become delayed due to internal or external factors, it is often necessary to use a robust execution method to avoid collisions caused by desynchronization. Robust execution methods - such as the Action Dependency Graph (ADG) - synchronize the execution of risky actions, but often at the expense of increased plan execution cost, because it may require some agents to wait for the delayed agents. In such cases, the execution's cost can be reduced while still preserving safety by finding a new plan either by rescheduling (reordering the agents at crossroads) or the more general replanning capable of finding new paths. However, these operations may be costly, and the new plan may not even lead to lower execution cost than the original plan: for example, the two plans may be the exact same. Therefore, we estimate the benefit that can be achieved by single replanning in scenarios with delayed agents given an immediate state of the execution with a fully connected feed-forward neural network. The input to the neural network is a set of newly designed ADG-based features describing the robust execution's state and the impact of potential delays, and the output is an estimated benefit achievable by replanning. We train and test the network on a new labeled dataset containing 12,000 experiments, and we show that our proposed method is capable of reducing the impact of delays by up to 94.6% of the achievable reduction.