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<p>The rapidly exploring random tree star (RRT*) algorithm is widely used in path planning due to its versatility and ability to adapt to various environments. However, the high degree of randomness in RRT* results in slow initial solution convergence rate and low-quality initial solution. To address these issues, this paper proposes a modified RRT* algorithm named fast forwarding connect RRT*(FFC-RRT*). Firstly, a new constrained sampling method is introduced, allowing the random tree to be sampled within a specific region, thereby improving the algorithm’s effective sampling rate. Secondly, an adaptive hybrid sampling method is employed, enabling the algorithm to better adapt to various environments. Finally, performance comparisons were conducted between the proposed algorithm, RRT*, F-RRT*, and FC-RRT* in various simulation environments. The results show that FFC-RRT* achieves a faster initial path planning speed than the other three algorithms in different environments, especially in complex environments, FFC-RRT* is 84.83%, 72.81%, and 16.68% faster than RRT*, F-RRT*, and FC-RRT*, respectively. Overall, the proposed algorithm demonstrates superior performance, providing faster initial path planning convergence rate while maintaining higher path quality.</p>

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Ffc-rrt*: An adaptive hybrid sampling path planning algorithm with rapid initial solution convergence

  • 期刊论文

  • 发表日期：

    2025年4月17日

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  • Zhang Su,
  • Qiang Lv,
  • Zhen Huang,
  • Tingting Yu,
  • Xiangli Wang

摘要

The rapidly exploring random tree star (RRT*) algorithm is widely used in path planning due to its versatility and ability to adapt to various environments. However, the high degree of randomness in RRT* results in slow initial solution convergence rate and low-quality initial solution. To address these issues, this paper proposes a modified RRT* algorithm named fast forwarding connect RRT*(FFC-RRT*). Firstly, a new constrained sampling method is introduced, allowing the random tree to be sampled within a specific region, thereby improving the algorithm’s effective sampling rate. Secondly, an adaptive hybrid sampling method is employed, enabling the algorithm to better adapt to various environments. Finally, performance comparisons were conducted between the proposed algorithm, RRT*, F-RRT*, and FC-RRT* in various simulation environments. The results show that FFC-RRT* achieves a faster initial path planning speed than the other three algorithms in different environments, especially in complex environments, FFC-RRT* is 84.83%, 72.81%, and 16.68% faster than RRT*, F-RRT*, and FC-RRT*, respectively. Overall, the proposed algorithm demonstrates superior performance, providing faster initial path planning convergence rate while maintaining higher path quality.