Optimal Task Assignment and Path Planning using Conflict-Based Search with Precedence and Temporal Constraints

TL;DR

This paper introduces CBS-TA-PTC, an augmented conflict-based search algorithm that efficiently solves complex multi-agent task assignment and path planning problems with precedence and temporal constraints, outperforming existing methods.

Contribution

It extends conflict-based search to handle task assignment, precedence, and temporal constraints within a unified framework, incorporating reinforcement learning objectives.

Findings

Successfully solves complex bomb-defusing tasks with constraints

Outperforms MARL and TAPF methods in efficiency

Demonstrates effectiveness in practical, constrained multi-agent scenarios

Abstract

The Multi-Agent Path Finding (MAPF) problem entails finding collision-free paths for a set of agents, guiding them from their start to goal locations. However, MAPF does not account for several practical task-related constraints. For example, agents may need to perform actions at goal locations with specific execution times, adhering to predetermined orders and timeframes. Moreover, goal assignments may not be predefined for agents, and the optimization objective may lack an explicit definition. To incorporate task assignment, path planning, and a user-defined objective into a coherent framework, this paper examines the Task Assignment and Path Finding with Precedence and Temporal Constraints (TAPF-PTC) problem. We augment Conflict-Based Search (CBS) to simultaneously generate task assignments and collision-free paths that adhere to precedence and temporal constraints, maximizing an objective quantified by the return from a user-defined reward function in reinforcement learning (RL). Experimentally, we demonstrate that our algorithm, CBS-TA-PTC, can solve highly challenging bomb-defusing tasks with precedence and temporal constraints efficiently relative to MARL and adapted Target Assignment and Path Finding (TAPF) methods.