Competency adjustment and workload balancing framework in multirobot task allocation. (27th November 2018)

• Record Type: Journal Article
• Authors: Lee, Dong-Hyun; Zaheer, Sheir Afgen; Han, Ji-Hyeong; Kim, Jong-hwan; Matson, Eric
• Is Part Of: International journal of advanced robotic systems. Volume 15:Number 6(2018:Nov./Dec.)
• Publication Date: 2018-11-27
• Subjects: Decentralized task allocation -- multirobot systems -- competency adjustment -- workload balancing; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881418812960
• Languages: English
• ISSNs: 1729-8806

Abstract

This article proposes a framework for competency adjustment and workload balancing in multirobot task allocation. Competency represents the ability of a robot to execute a task in terms of quality and cost, and workload balancing denotes distribution of workload among robots. This framework considers the quality and cost of a robot for a task and adjusts them in accordance with environmental changes. For workload balancing, the framework utilizes the concept of subsidy to encourage participation from the less active members of the robot team. The proposed framework is implemented in a simulated cleaning mission. Simulation results demonstrate that this framework can adjust the competency in accordance with environmental changes and distribute workload among robots in a balanced manner.