Confidence random tree

This article introduces a novel confidence random tree-based sampling path planning algorithm for mobile service robots operating in real environments. The algorithm is time efficient, can accommodate narrow corridors, enumerates possible solutions, and minimizes the cost of the path. These benefits are realized by incorporating notable approaches from other existing path planning algorithms into the proposed algorithm. During path selection, the algorithm considers the length and safety of each path via a sampling and rejection method. The algorithm operates as follows. First, the confidence of a path is computed based on the clearance required to ensure the safety of the robot, where the clearance is defined as the distance between the path and the closest obstacle. Then, the sampling method generates a tree graph in which the edge lengths are controlled by the confidence. In a low confidence space, such as a narrow corridor, the corresponding graph has denser samples with short edges while in a high confidence space, the samples are widely spaced with longer edges. Finally, a rejection method is employed to ensure a reasonably short computation time by optimizing the sample density by rejecting unnecessary samples. The performance of the proposed algorithm is validated by comparing the experimental results to those of several commonly used algorithms