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
Automatic Configuration of Multi-Robot Systems: Planning for Multiple Steps
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Methods for task allocation via agent coalition formation
Artificial Intelligence
COBOS: Cooperative backoff adaptive scheme for multirobot task allocation
IEEE Transactions on Robotics
Multi-robot coalition formation
IEEE Transactions on Robotics
A cluster-based approach for disturbed, spatialized, distributed information gathering systems
PRIMA'10 Proceedings of the 13th international conference on Principles and Practice of Multi-Agent Systems
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This paper addresses the challenge of forming appropriate heterogeneous robot teams to solve tightly-coupled, potentially multi-robot tasks, in which the robot capabilities may vary over the environment in which the task is being performed. Rather than making use of a permanent tightly-coupled robot team for performing the task, our approach aims to recognize when tight coupling is needed, and then only form tight cooperative teams at those times. This results in important cost savings, since coordination is only used when the independent operation of the team members would put mission success at risk. Our approach is to define a new semantic information type, called environmentally dependent information, which allows us to capture certain environmentally-dependent perceptual constraints on vehicle capabilities. We define locations at which the robot team must transition between tight and weak cooperation as critical junctures. Note that these critical juncture points are a function of the robot team capabilities and the environmental characteristics, and are not due to a change in the task itself. We calculate critical juncture points by making use of our prior ASyMTRe approach, which can automatically configure heterogeneous robot team solutions to enable sharing of sensory capabilities across robots. We demonstrate these concepts in experiments involving a human-controlled blimp and an autonomous ground robot in a target localization task.