Task Allocation Algorithm Based on Robot Ability and Relevance with Group Collaboration in a Robot Team

Abstract

Task allocation for mobile robots team is a crucial issue before performing a certain task. The algorithm we propose here is named RARGC-a task allocation algorithm based on robot ability and relevance with group collaboration, where robot ability is weighted by belief degree, relevance stands for a fresh concept of ¿history relevance¿ between every two robots to establish reasonable groups for better collaboration, and group collaboration includes inter and inner group help strategy be adopted as different nodes being failures in unknown environment. RARGC emphasizes the role of ¿agent node¿ in each group that is responsible for task competition and group leadership; formation maintenance as well as task execution with changing agents. Simulation on player/stage shows that the mechanism is feasible and valid.