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<p>An improved Rapidly-exploring Random Tree (RRT) algorithm is proposed to address the issues of random search, slow convergence, and inefficient paths in robotic arm path planning.Firstly, a sampling strategy that integrates probabilistic target bias and obstacle-free dynamic space is introduced to guide the expansion direction of the random tree, thereby overcoming the blind search and local optima tendencies of the traditional RRT algorithm. Secondly, a target gravitational component expansion strategy with adaptive step-size adjustment is proposed to enhance obstacle avoidance capability during random tree expansion. Finally, the Minimum Snap method is applied to optimize the pruned path, reducing redundant points and improving path smoothness by addressing curvature discontinuity. Simulation results demonstrate that the proposed algorithm outperforms the standard RRT, Bias-RRT, and dynamic space RRT algorithms in terms of planning time, path cost, and iteration count across various environments, validating the effectiveness and superiority of the proposed approach.</p>

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Path Planning for Slab Manipulating Robotic Arms Based on Improved RRT Combined with Minimum Snap

  • 期刊论文

  • 发表日期：

    2026年2月20日

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  • Guanghua Yin,
  • Bing Wu,
  • Xinyu Bao,
  • Xiaonong Guo,
  • Qiang Xie

摘要

An improved Rapidly-exploring Random Tree (RRT) algorithm is proposed to address the issues of random search, slow convergence, and inefficient paths in robotic arm path planning.Firstly, a sampling strategy that integrates probabilistic target bias and obstacle-free dynamic space is introduced to guide the expansion direction of the random tree, thereby overcoming the blind search and local optima tendencies of the traditional RRT algorithm. Secondly, a target gravitational component expansion strategy with adaptive step-size adjustment is proposed to enhance obstacle avoidance capability during random tree expansion. Finally, the Minimum Snap method is applied to optimize the pruned path, reducing redundant points and improving path smoothness by addressing curvature discontinuity. Simulation results demonstrate that the proposed algorithm outperforms the standard RRT, Bias-RRT, and dynamic space RRT algorithms in terms of planning time, path cost, and iteration count across various environments, validating the effectiveness and superiority of the proposed approach.