Fuzzy Controllers: How to Design Them, how They Work
Fuzzy Controllers: How to Design Them, how They Work
Coverage for robotics – A survey of recent results
Annals of Mathematics and Artificial Intelligence
From fireflies to fault-tolerant swarms of robots
IEEE Transactions on Evolutionary Computation
Feedback coordination of limited capability mobile robots
International Journal of Intelligent Systems Technologies and Applications
Distributed sensor analysis for fault detection in tightly-coupled multi-robot team tasks
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
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
Adaptive data-driven error detection in swarm robotics with statistical classifiers
Robotics and Autonomous Systems
A two-tiered global path planning strategy for limited memory mobile robots
Robotics and Autonomous Systems
Coordinated multi-robot exploration
IEEE Transactions on Robotics
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
Development of a reduced human user input task allocation method for multiple robots
Robotics and Autonomous Systems
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Restricting the usage of a team of robots to a few expert human users can be disadvantageous. In applications such as exploration, it may not always be possible for human experts to travel to sites, resulting in negative consequences. It is preferable to have a robotic system that is capable of coordinating itself based on inputs provided by non-expert human users. Hence, this paper presents the development of a robust feedback system for coordinating a hierarchical team of robots where inputs are specified by non-expert human users. Experiments with a multi-robot mapping and exploration task show that the feedback system successfully detects and corrects three types of failures. These are poor performance, partial failure and complete failure. Moreover, the system is robust to threshold value variation and monitor time interval variation within the tested limits.