Spatial path planning for hydraulic turbine flow channels using an improved RRT algorithm

Purpose The purpose of this study is to propose a path planning method based on an improved Rapidly-exploring Random Tree (RRT) algorithm to address the path planning challenges of robotic arms carrying inspection cameras in the complex and narrow flow channel spaces of hydraulic turbines. Design/methodology/approach An improved RRT algorithm is developed, optimizing the tree expansion process by adopting a direction-guided strategy and adaptive step size adjustment. A probabilistic bias sampling factor is introduced to enhance the expansion direction and efficiency. Redundant points are removed using a stepwise checking method, and the inspection path is smoothed with Bézier curves. Simulation experiments are conducted in MATLAB using a 3D model of the turbine flow channel. Findings The improved RRT algorithm reduces computation time by 88% compared to RRT-Connect and RRT-Extend, and is 71.6% faster than RRT-star. The path length is 97.89% shorter than RRT-Connect, 95.93% shorter than RRT-Extend and 80.89% fewer than RRT-star. The number of path nodes is 97.1% fewer than RRT-Connect, 97.6% fewer than RRT-Extend and 94% fewer than RRT-star. The algorithm achieves a 100% success rate, outperforming traditional path planning algorithms in overall performance. Practical implications The improved RRT algorithm provides an efficient and feasible solution for robotic path planning in hydraulic turbine defect inspection, significantly reducing computation time and path length. Originality/value The proposed improved RRT algorithm significantly enhances the efficiency and quality of path planning for robotic arms in narrow flow channel environments, offering a high-performance path planning solution for robotic defect inspection and applications.