On distance utility in the exploration task

Performance of exploration strategies strongly depends on the process of determination of a next robot goal. Current approaches define different utility functions how to evaluate and select possible next goal candidates. One of the mostly used evaluation criteria is the distance cost that prefers candidates close to the current robot position. If this is the only criterion, simply the nearest candidate is chosen as the next goal. Although this criterion is simple to implement and gives feasible results there are situations where the criterion leads to wrong decisions. This paper presents the distance cost that reflects traveling through all goal candidates. The cost is determined as a solution of the Traveling Salesman Problem using the Chained Lin-Kernighan heuristic. The cost can be used as a stand-alone criterion as well as it can be integrated into complex decision systems. Experimental results for open-space and office-like experiments show that the proposed approach outperforms the standard one in the length of the traversed trajectory during the exploration while the computational burden is not significantly increased.