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

Abstract

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.

Energy-Efficient Autonomous Navigation of Solar-Powered UAVs for Surveillance of Mobile Ground Targets in Urban Environments

“…A lower bound tree-RRT is designed to find out the near optimal path [25]. Besides, a node control strategy is proposed in order to restrict the expansion of the random tree [26]. Because of the computational efficiency, RRT-based approaches are generally suitable to run in real-time, and it also has potential to be implemented in a decentralized manner [27].…”

A Method of Enhancing Rapidly-Exploring Random Tree Robot Path Planning Using Midpoint Interpolation

“…Furthermore, it is indeed possible for the robot to be able to optimize its path by determining the quickest and safest path to its destination point in order to save time and energy. However, an algorithm that generates the optimal path increases the computation, and an algorithm that quickly generates a path does not guarantee the optimal path [5].…”

Improved Bidirectional RRT ∗ Path Planning Method for Smart Vehicle

“…e typical improvements are improving the RRT's search style and fusing the RRT with other algorithms effectively. us, the generated path is optimized again and the planning effect is further improved [12]. Among them, improving the RRT's search mode is an important improved direction.…”