Research on Robot Path Planning Based on Improved RRT-Connect Algorithm

Abstract

The proposed improved RRT-Connect algorithm (TRRT-Connect) addresses the issues of path elongation, excessive turns, and inadequate pass ability encountered in the standard RRT-Connect algorithm for path planning. Firstly, an improved RRT algorithm is employed to search and add a middle root node, facilitating the simultaneous expansion of four random trees to expedite algorithm convergence. Additionally, a target-biased strategy is employed for random point selection, and an attractive field is superimposed on node generation, along with integration of a greedy search strategy. Furthermore, a novel dynamic step size adjustment method is introduced, which dynamically selects appropriate step sizes by identifying the number of obstacles within the scanning region. Then, a bidirectional pruning optimization method is applied to the generated initial paths to accelerate pruning efficiency and remove redundant nodes along the paths. Finally, path smoothing is conducted at path turning points and the number of paths􀆳 turns is reduced. Simulation comparative experiments are conducted in three different environmental maps. The results indicate that the TRRT-Connect algorithm shows significant improvements compared with the standard RRT-Connect algorithm in terms of path length, number of iterations, and number of nodes. The paths generated are smoother without path turns, and there is better pass ability in densely populated obstacle areas. Experimental results confirm the effectiveness of this algorithm. Moreover, the application of the TRRT-Connect algorithm in field instance simulations reduces the transportation path length of mobile robots by 15.4% compared with traditional fixed paths, with smoother paths, further confirming the practicality of the algorithm.

Keywords

• rrt-connect algorithm; dynamic step size adjustment; bidirectional pruning optimization; path planning