Task Allocation in Robotic Swarms: Explicit Communication Based Approaches

TL;DR

This paper investigates multi-robot task allocation in unknown environments, proposing four distributed hybrid methods for cooperative discovery and coverage of tasks, analyzing their performance, scalability, and robustness.

Contribution

It introduces four novel self-organized distributed methods for task allocation in robotic swarms operating in unknown environments.

Findings

Methods effectively enable cooperative task discovery and coverage.

Hybrid methods demonstrate good scalability and robustness.

Performance varies with environment complexity and robot failure scenarios.

Abstract

In this paper we study multi robot cooperative task allocation issue in a situation where a swarm of robots is deployed in a confined unknown environment where the number of colored spots which represent tasks and the ratios of them are unknown. The robots should cover this spots as far as possible to do cleaning and sampling actions desirably. It means that they should discover the spots cooperatively and spread proportional to the spots area and avoid from remaining idle. We proposed 4 self-organized distributed methods which are called hybrid methods for coping with this scenario. In two different experiments the performance of the methods is analyzed. We compared them with each other and investigated their scalability and robustness in term of single point of failure.