Multi-Robot Coordination and Cooperation with Task Precedence Relationships

TL;DR

This paper introduces a novel multi-robot task planning framework that integrates task precedence, coordination, and cooperation, utilizing graph-based models and approximation algorithms to enhance efficiency in complex missions.

Contribution

It presents a new formulation for multi-robot task planning incorporating precedence, coordination, and cooperation, and develops scalable solution methods including network flow and heuristics.

Findings

Approximate solutions efficiently handle large-scale problems.

Task graph formulation effectively models complex dependencies.

Heuristic algorithms provide practical solutions for real-world scenarios.

Abstract

We propose a new formulation for the multi-robot task planning and allocation problem that incorporates (a) precedence relationships between tasks; (b) coordination for tasks allowing multiple robots to achieve increased efficiency; and (c) cooperation through the formation of robot coalitions for tasks that cannot be performed by individual robots alone. In our formulation, the tasks and the relationships between the tasks are specified by a task graph. We define a set of reward functions over the task graph's nodes and edges. These functions model the effect of robot coalition size on the task performance, and incorporate the influence of one task's performance on a dependent task. Solving this problem optimally is NP-hard. However, using the task graph formulation allows us to leverage min-cost network flow approaches to obtain approximate solutions efficiently. Additionally, we explore a mixed integer programming approach, which gives optimal solutions for small instances of the problem but is computationally expensive. We also develop a greedy heuristic algorithm as a baseline. Our modeling and solution approaches result in task plans that leverage task precedence relationships and robot coordination and cooperation to achieve high mission performance, even in large missions with many agents.