Adaptive task assignment for multiple mobile robots via swarm intelligence approach

Authors:
Dandan Zhang;Guangming Xie;Junzhi Yu;Long Wang
Affiliations:
Intelligent Control Laboratory, Department of Mechanics and Space Technologies, College of Engineering, Peking University, Beijing 100871, China;Intelligent Control Laboratory, Department of Mechanics and Space Technologies, College of Engineering, Peking University, Beijing 100871, China;Intelligent Control Laboratory, Department of Mechanics and Space Technologies, College of Engineering, Peking University, Beijing 100871, China;Intelligent Control Laboratory, Department of Mechanics and Space Technologies, College of Engineering, Peking University, Beijing 100871, China
Venue:
Robotics and Autonomous Systems
Year:
2007

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Abstract

This paper describes an adaptive task assignment method for a team of fully distributed mobile robots with initially identical functionalities in unknown task environments. A hierarchical assignment architecture is established for each individual robot. In the higher hierarchy, we employ a simple self-reinforcement learning model inspired by the behavior of social insects to differentiate the initially identical robots into ''specialists'' of different task types, resulting in stable and flexible division of labor; on the other hand, in dealing with the cooperation problem of the robots engaged in the same type of task, Ant System algorithm is adopted to organize low-level task assignment. To avoid using a centralized component, a ''local blackboard'' communication mechanism is utilized for knowledge sharing. The proposed method allows the robot team members to adapt themselves to the unknown dynamic environments, respond flexibly to the environmental perturbations and robustly to the modifications in the team arising from mechanical failure. The effectiveness of the presented method is validated in two different task domains: a cooperative concurrent foraging task and a cooperative collection task.