Authors:
Sanem Sariel
1
;
Tucker Balch
2
and
Nadia Erdogan
1
Affiliations:
1
Istanbul Technical University, Turkey
;
2
Georgia Institute of Technology, College of Computing, United States
Keyword(s):
Distributed AI, robotics, multi-agent systems.
Related
Ontology
Subjects/Areas/Topics:
Autonomous Agents
;
Informatics in Control, Automation and Robotics
;
Mobile Robots and Autonomous Systems
;
Robotics and Automation
Abstract:
In this paper, we present the design and implementation of a multi-robot cooperation framework to collectively execute inter-dependent tasks of an overall complex mission requiring diverse capabilities. Given a heterogeneous team of robots and task dependencies, the proposed framework provides a distributed mechanism for assigning tasks to robots in an order that efficiently completes the mission. The approach is robust to unreliable communication and robot failures. It is a distributed auction-based approach, and therefore scalable. In order to obtain optimal allocations, effective bid evaluations are needed. Additionally to maintain optimality in noisy environments, dynamic re-allocations of tasks are needed as implemented in dynamic task selection and coalition maintenance scheme that we propose. Real-time contingencies are handled by recovery routines, called Plan B precautions in our framework. Here, in this paper, we present performance results of our framework for robustness i
n simulations that include variable message loss rates and robot failures. Experiments illustrate robustness of our approach against several contingencies.
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