REDUNDANT ROBOTIC ARM PATH PLANNING USING RECURSIVE RANDOM INTERMEDIATE STATE ALGORITHM
DOI:
https://doi.org/10.15588/1607-3274-2025-3-16Keywords:
path planning, redundant robotic manipulator, collision avoidanceAbstract
Context. Collision-free path planning in joint space for redundant robotic manipulators remains a challenging task due to the high-dimensional configuration space and dynamically changing environments. Existing methods often struggle to balance search time and path quality, which is crucial for real-time applications.
Objective. The aim of this study is to develop a new method to plan efficient, collision-free trajectories in real time for redundant robotic manipulators.
Method. A novel sampling-based algorithm for collision-free joint space path planning for redundant robotic manipulators presented in this study. The algorithm is called the Recursive Random Intermediate State (RRIS). The RRIS algorithm primarily works by generating a set of random intermediate states and iteratively selecting the optimal one based on the number of collisions along the discretized path. Furthermore, the paper proposes an axis-aligned bounding box generation strategy and an early exit strategy to improve algorithm speed. Finally, repeated calls of the algorithm are proposed to improve its reliability. The performance of the RRIS algorithm is evaluated through a set of comprehensive tests and compared with the popular RRT Connect algorithm implemented in Open Motion Planning Library.
Results. Experimental evaluations show that the RRIS algorithm under the test conditions produces collision-free paths with significantly shorter average lengths and reduces search time by approximately three times compared to the RRT Connect algorithm.
Conclusions. The proposed RRIS algorithm demonstrates a promising approach to real-time path planning for redundant robotic manipulators. By combining strategic intermediate state sampling with efficient collision evaluation and early termination mechanisms, the algorithm offers a robust alternative to known methods.
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