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.