Task Allocation using a Team of Robots

TL;DR

This paper presents a comprehensive formulation of multi-robot task allocation, encompassing various constraints, objectives, and dynamic information, and reviews existing solution methods including optimization and market-based approaches.

Contribution

It introduces a generalized framework for task allocation that unifies multiple existing models and discusses diverse solution strategies.

Findings

The formulation captures robot, task, and environment states.

It accommodates various constraints and objectives.

Existing solutions include optimization and market-based methods.

Abstract

Task allocation using a team or coalition of robots is one of the most important problems in robotics, computer science, operational research, and artificial intelligence. In recent work, research has focused on handling complex objectives and feasibility constraints amongst other variations of the multi-robot task allocation problem. There are many examples of important research progress in these directions. We present a general formulation of the task allocation problem that generalizes several versions that are well-studied. Our formulation includes the states of robots, tasks, and the surrounding environment in which they operate. We describe how the problem can vary depending on the feasibility constraints, objective functions, and the level of dynamically changing information. In addition, we discuss existing solution approaches for the problem including optimization-based approaches, and market-based approaches.