Predicting travel time from path characteristics for wheeled robot navigation

摘要

Modern approaches to mobile robot navigation typically employ a two-tiered system where first a geometric path is computed in a potentially obstacle-laden environment, and then a reactive motion controller with obstacle-avoidance capabilities is used to follow this path to the goal. However, when multiple path candidates are present, the shortest path is not always the best choice as it may lead through narrow gaps and it may be in general hard to follow due to a lack of smoothness. The assessment of an estimated completion time is a much stronger selection criterion, but due to the lack of a dynamic model in the path computation phase the completion time is typically a priori not known. We introduce a novel approach to estimate the completion time of a path based on simple, readily available features such as the length, the smoothness, and the clearance of the path. To this end, we apply non-linear regression and train an estimator with data gained from the simulation of the actual path execution with a controller that is based on the well-known Dynamic Window Approach. As we show in the experiments, our method is able to realistically estimate the completion time for 2D grid paths using the learned predictor and highly outperforms a prediction that is only based on path length.