Multi-Agent Combinatorial Path Finding with Heterogeneous Task Duration

TL;DR

This paper addresses the complex multi-agent pathfinding problem with heterogeneous task durations, proposing two methods—one post-processing and one integrated—to optimize collision-free paths considering task execution times.

Contribution

It introduces two novel approaches for multi-agent pathfinding that incorporate heterogeneous task durations, with one method ensuring optimal solutions during planning.

Findings

The methods handle up to 20 agents and 50 targets.

They effectively incorporate task durations into path planning.

The approaches are robust to robot motion disturbances.

Abstract

Multi-Agent Combinatorial Path Finding (MCPF) seeks collision-free paths for multiple agents from their initial locations to destinations, visiting a set of intermediate target locations in the middle of the paths, while minimizing the sum of arrival times. While a few approaches have been developed to handle MCPF, most of them simply direct the agent to visit the targets without considering the task duration, i.e., the amount of time needed for an agent to execute the task (such as picking an item) at a target location. MCPF is NP-hard to solve to optimality, and the inclusion of task duration further complicates the problem. This paper investigates heterogeneous task duration, where the duration can be different with respect to both the agents and targets. We develop two methods, where the first method post-processes the paths planned by any MCPF planner to include the task duration and has no solution optimality guarantee; and the second method considers task duration during planning and is able to ensure solution optimality. The numerical and simulation results show that our methods can handle up to 20 agents and 50 targets in the presence of task duration, and can execute the paths subject to robot motion disturbance.