Budget Allocation Policies for Real-Time Multi-Agent Path Finding

TL;DR

This paper investigates how different budget allocation policies impact the performance of real-time multi-agent pathfinding algorithms, demonstrating that intelligent distribution of planning resources improves problem-solving efficiency.

Contribution

It introduces and evaluates novel planning budget allocation policies for windowed MAPF algorithms, addressing a gap in existing real-time MAPF solutions.

Findings

Shared budget allocation is ineffective in complex scenarios.

Intelligent distribution policies solve more instances faster.

Budget-aware policies outperform uniform approaches.

Abstract

Multi-Agent Path finding (MAPF) is the problem of finding paths for a set of agents such that each agent reaches its desired destination while avoiding collisions with the other agents. This problem arises in many robotics applications, such as automated warehouses and swarms of drones. Many MAPF solvers are designed to run offline, that is, first generate paths for all agents and then execute them. In real-world scenarios, waiting for a complete solution before allowing any robot to move is often impractical. Real-time MAPF (RT-MAPF) captures this setting by assuming that agents must begin execution after a fixed planning period, referred to as the planning budget, and execute a fixed number of actions, referred to as the execution window. This results in an iterative process in which a short plan is executed, while the next execution window is planned concurrently. Existing solutions to RT-MAPF iteratively call windowed versions of MAPF algorithms in every planning period, without explicitly considering the size of the planning budget. We address this gap and explore different policies for allocating the planning budget in windowed versions of MAPF-LNS2, a state-of-the-art MAPF algorithm. Our exploration shows that the baseline approach in which all agents draw from a shared planning budget pool is ineffective in challenging scenarios. Instead, policies that intelligently distribute the planning budget among agents are able to solve more problem instances in less time.