Collision-Free Path Planning Method for Robots Based on an Improved Rapidly-Exploring Random Tree Algorithm

Sampling-based methods are popular in the motion planning of robots, especially in high-dimensional spaces. Among the many such methods, the Rapidly-exploring Random Tree (RRT) algorithm has been widely used in multi-degree-of-freedom manipulators and has yielded good results. However, existing RRT planners have low exploration efficiency and slow convergence speed and have been unable to meet the requirements of the intelligence level in the Industry 4.0 mode. To solve these problems, a general autonomous path planning algorithm of Node Control (NC-RRT) is proposed in this paper based on the architecture of the RRT algorithm. Firstly, a method of gradually changing the sampling area is proposed to guide exploration, thereby effectively improving the search speed. In addition, the node control mechanism is introduced to constrain the extended nodes of the tree and thus reduce the extension of invalid nodes and extract boundary nodes (or near-boundary nodes). By changing the value of the node control factor, the random tree is prevented from falling into a so-called “local trap” phenomenon, and boundary nodes are selected as extended nodes. The proposed algorithm is simulated in different environments. Results reveal that the algorithm greatly reduces the invalid exploration in the configuration space and significantly improves planning efficiency. In addition, because this method can efficiently use boundary nodes, it has a stronger applicability to narrow environments compared with existing RRT algorithms and can effectively improve the success rate of exploration.

Confidence random tree-based algorithm for mobile robot path planning considering the path length and safety

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.