International Journal of Robotics Research
Fuzzy Controllers: How to Design Them, how They Work
Fuzzy Controllers: How to Design Them, how They Work
First Results in the Coordination of Heterogeneous Robots for Large-Scale Assembly
ISER '00 Experimental Robotics VII
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver
IEEE Transactions on Computers
A Hierarchical Task Allocation Method Based on Task Case and its Application to Multi-Robot Hunting
ICINIS '08 Proceedings of the 2008 First International Conference on Intelligent Networks and Intelligent Systems
Methods for task allocation via agent coalition formation
Artificial Intelligence
Feedback coordination of limited capability mobile robots
International Journal of Intelligent Systems Technologies and Applications
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
CoMutaR: a framework for multi-robot coordination and task allocation
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Development of a navigation system for heterogeneous mobile robots
International Journal of Intelligent Systems Technologies and Applications
Multi-robot coalition formation
IEEE Transactions on Robotics
Multirobot systems: a classification focused on coordination
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Towards a robust feedback system for coordinating a hierarchical multi-robot system
Robotics and Autonomous Systems
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Task allocation mechanisms are employed by multi-robot systems to efficiently distribute tasks between different robots. Currently, many task allocation methods rely on detailed expert knowledge to coordinate robots. However, it may not be feasible to dedicate an expert human user to a multi-robot system. Hence, a non-expert user may have to specify tasks to a team of robots in some situations. This paper presents a novel reduced human user input multi-robot task allocation technique that utilises Fuzzy Inference Systems (FISs). A two-stage primary and secondary task allocation process is employed to select a team of robots comprising manager and worker robots. A multi-robot mapping and exploration task is utilised as a model task to evaluate the task allocation process. Experiments show that primary task allocation is able to successfully identify and select manager robots. Similarly, secondary task allocation successfully identifies and selects worker robots. Both task allocation processes are also robust to parameter variation permitting intuitive selection of parameter values.