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Efficient path planning for continuum robotic arm using improved RRT algorithm
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AI
DOI：10.1007/s12206-026-0453-0.png)
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Abstract
En 中文
Continuum robotic arms require path planning in narrow, obstacle-rich environments. Existing methods primarily use sampling-based algorithms like rapidly-exploring random tree (RRT), which suffer from strong randomness, slow convergence, and suboptimal results. To address these issues, this study proposes an improved RRT algorithm. First, the initial step size is calculated using environmental information, with obstacle data dynamically adjusting the step size during expansion to enhance exploration. Second, target-biased sampling combined with an optimized nearest-node selection strategy improves search efficiency for rapid feasible path generation. Finally, high-order B & eacute;zier curves were applied to optimize the path for smoothness. MATLAB simulations and physical experiments demonstrate the algorithm efficiently generates valid paths among multiple obstacles in 3D space. Compared to conventional RRT, it reduces path cost by 33.1 %, planning time by 98.1 %, and node count by 72.1 %, validating its effectiveness.
Keywords:
Continuum robotic arm
Kinematic analysis
Path planning
Curve fitting
Journal
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