Intelligent scheduling
Multi-Robot Task Allocation in Uncertain Environments
Autonomous Robots
Continual coordination through shared activities
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
An Autonomous Earth-Observing Sensorweb
IEEE Intelligent Systems
On-line monitoring of plan execution: A distributed approach
Knowledge-Based Systems
Directed stigmergy-based control for multi-robot systems
Proceedings of the ACM/IEEE international conference on Human-robot interaction
LUNARES: lunar crater exploration with heterogeneous multi robot systems
Intelligent Service Robotics
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This paper describes an integrated system for coordinating multiple rover behavior with the overall goal of collecting planetary surface data. The MISUS system combines techniques from planning and scheduling with machine learning to perform autonomous scientific exploration with cooperating rovers. A distributed planning and scheduling approach is used to generate efficient, multi-rover coordination plans, monitor plan execution, and perform re-planning when necessary. A machine learning clustering component is used to deduce geological relationships among collected data and select new science activities. A key concept promoted by this system is the use of goal interdependency information to perform plan optimization and increase the value of collected science data. We discuss how we represent and reason about goal dependency and utility information in our planning system and explain how this information can change dynamically during system use. We show through experimental results that our approach significantly increases overall plan quality versus a standard approach that treats goal utilities independently.