Improving cost estimation in market-based coordination of a distributed sensing task

While market-based approaches, such as TraderBots, have shown much promise for efficient coordination of multirobot teams, the cost estimation mechanism and its impact on solution efficiency has not been investigated. This paper provides a first analysis of the cost estimation process in the TraderBots approach applied to a distributed sensing task. In the presented implementation, path costs are estimated using the D* path planning algorithm with optimistic costing of unknown map cells. The reported results show increased team efficiency when cost estimates reflect different environmental and mission characteristics. Thus, this paper demonstrates that market-based approaches can improve team efficiency if cost estimates take into account environmental and mission characteristics. These findings encourage future research on applying learning techniques for online modification of cost estimation and in market-based coordination.