Cooperative Multi-Robot Task Allocation with Reinforcement Learning

Bumjin Park,Jaesik Choi,Cheongwoong Kang

doi:10.3390/app12010272

Bumjin Park, Jaesik Choi + Show 1 more

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https://doi.org/10.3390/app12010272

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Abstract

This paper deals with the concept of multi-robot task allocation, referring to the assignment of multiple robots to tasks such that an objective function is maximized. The performance of existing meta-heuristic methods worsens as the number of robots or tasks increases. To tackle this problem, a novel Markov decision process formulation for multi-robot task allocation is presented for reinforcement learning. The proposed formulation sequentially allocates robots to tasks to minimize the total time taken to complete them. Additionally, we propose a deep reinforcement learning method to find the best allocation schedule for each problem. Our method adopts the cross-attention mechanism to compute the preference of robots to tasks. The experimental results show that the proposed method finds better solutions than meta-heuristic methods, especially when solving large-scale allocation problems.

Highlights

With the development of robot technology, the use of robots has increased in various fields such as automated factories, military weapons, autonomous vehicles, and drones
This problem can be defined in various ways according to the environmental settings [2]. (1) the number of tasks that a robot can handle at one time, (2) the number of robots required for the task, and (3) the consideration of future planning
The second axis relates to single-robot tasks (SR) and multi-robot tasks (MR) and the last axis refers to instantaneous assignments (IA) and time-extended assignments (TA)

Summary

Introduction

With the development of robot technology, the use of robots has increased in various fields such as automated factories, military weapons, autonomous vehicles, and drones. If there are many robots, the job of an expert is to determine the overall behavior of the robots, rather than controlling them individually In such a case, the allocation of each robot can be done using an algorithms An example is illustrated by a situation involving ten rooms of different sizes in a building with five robot vacuum cleaners ready at the battery-charging machine. This problem is an NP-hard problem, and finding an optimal solution takes a long time This problem is termed the multi-robot task allocation (MRTA) problem, involving the assignment of tasks to multiple robots to achieve a given goal [1,2,3]. A neural network enables end-to-end training, and there is no loss of information when constructing the algorithm

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