LAUSR

As a fundamental capability of mobile robots, path planning highly relies on the accurate localization of the robot. However, limited consideration for the localizability (which describes the capability of acquiring accurate localization) has been made in path planning. This brings a high risk of choosing a path that is optimal but results in the robot easily getting lost. There exist two key challenges to address this problem: 1) How to evaluate the localizability of a path and its impact on path planning. 2) How to balance the localizability of a path and the standard path planning criteria (e.g., shortest travel distance, obstacle-free path, etc.. To overcome the two challenges a new path evaluation method is required. So we first analyzed the uncertainty that comes from dead-reckoning and map matching. Then the localizability was estimated by the fusion of the uncertainty coming from both of them. Based on that, the impact of the localizability on the path planning task has been evaluated by an evaluation function. By combining the localizability evaluation function with traditional criteria (e.g., shortest length, obstacle-free path, etc.), a new path evaluation function for path planning is established. Both simulation and experimental studies show that the new path evaluation function can offer a balance between the localizability and the traditional criteria for path planning.