Opportunistic optimization for market-based multirobot control

Multirobot coordination, if made efficient and robust, promises high impact on automation. The challenge is to enable robots to work together in an intelligent manner to execute a global task. The market approach has had considerable success in the multirobot coordination domain. This paper investigates the effects of introducing opportunistic optimization with leaders to enhance market-based multirobot coordination. Leaders are able to optimize within subgroups of robots by collecting information about their tasks and status, and re-allocating the tasks within the subgroup in a more profitable manner. The presented work considers the effects of a leader optimizing a single subgroup, and some effects of multiple leaders optimizing overlapping subgroups. The implementations were tested on a variation of the distributed traveling salesman problem. Presented results show that global costs can be reduced, and hence task allocation can be improved, utilizing leaders.

Authors

• M. Bernardine Dias
• Anthony Stentz

Keywords

• Robot kinematics
• Intelligent robots
• Protocols
• Contracts
• Robustness
• Costs
• Robot sensing systems
• Biosensors
• Biological system modeling
• Systems biology
• Global Cost
• Task Allocation
• Marketing Approach
• Traveling Salesman Problem
• Global Task
• Team Members
• Local Information
• Cost Function
• Clustering Algorithm
• Applicability Domain
• Global Reduction
• Optimal Plan
• Binary Tree
• Centralized Approach
• Task Domain
• Clustering Task
• Swarm Robotics
• Robot Capabilities
• Robotic Group
• Single Robot