Improving Collaboration through Fusion of Bid Information for Market-based Multi-robot Exploration

Abstract

Using multi-robot has more advantages than using single robot for unknown environment exploration. But it brings a new problem of task allocation. Market-based method is an economic approach to allocating targets for robots through auction. However, it only considers costs in the local map of each robot. We update the local maps through fusion of both the local sensor data and the bid information, and thus the extended parts in maps enable robots to calculate costs of other robots’ targets. No extra communication is needed. The results of real robot experiments and simulations demonstrate that the improved method is more efficient than the original market-based approach and provide a proper improved method for environment exploration.