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<p>The Batch informed trees (BIT*) algorithm is widely used for path planning in robotic manipulators. However, its path search efficiency can still be optimized. Therefore, this paper proposes a Halton sequence-based BIT*&#xa0;(Halton BIT*) algorithm. The Halton sequence is employed for uniform sampling to reduce the randomness and clustering of sample point distribution. An adaptive strategy adjusts the batch size to mitigate its influence on performance. Additionally, sampling distribution is optimized by restricting it to obstacle-free space. During the path search process, Gaussian guided sampling is utilized to identify the turning points and perform perturbation, thereby reducing circuitous path. Lastly, cubic spline interpolation is utilized to generate a smooth path for the manipulator. A simulation environment was built on the MATLAB platform. Simulations of collision-free path planning were conducted for Halton BIT*, BIT*, FMT*, GBIT*, TOPO, and Informed RRT* algorithms in four obstacle scenarios. Finally, the obstacle avoidance ability of Halton BIT* is validated on the PyBullet platform. In Cartesian space, the path planning time of Halton BIT* is reduced by 85.2%, 38.1% and 46.9% compared with BIT*, FMT* and Informed RRT*, respectively. Compared with GBIT* and TOPO, the path length of Halton BIT* in joint space is reduced by 3.9% and 10%. The results demonstrate that the Halton BIT* algorithm significantly improves planning efficiency and maintains path quality, and is feasible for path planning in manipulators.</p>

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Halton BIT* path planning algorithm for manipulator in obstacle space

  • 期刊论文

  • 发表日期：

    2026年7月2日

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  • Jialu Huang,
  • Jinning Zhi,
  • Zhibin Yao,
  • Dongao Zhou,
  • Jianen Da

摘要

The Batch informed trees (BIT*) algorithm is widely used for path planning in robotic manipulators. However, its path search efficiency can still be optimized. Therefore, this paper proposes a Halton sequence-based BIT* (Halton BIT*) algorithm. The Halton sequence is employed for uniform sampling to reduce the randomness and clustering of sample point distribution. An adaptive strategy adjusts the batch size to mitigate its influence on performance. Additionally, sampling distribution is optimized by restricting it to obstacle-free space. During the path search process, Gaussian guided sampling is utilized to identify the turning points and perform perturbation, thereby reducing circuitous path. Lastly, cubic spline interpolation is utilized to generate a smooth path for the manipulator. A simulation environment was built on the MATLAB platform. Simulations of collision-free path planning were conducted for Halton BIT*, BIT*, FMT*, GBIT*, TOPO, and Informed RRT* algorithms in four obstacle scenarios. Finally, the obstacle avoidance ability of Halton BIT* is validated on the PyBullet platform. In Cartesian space, the path planning time of Halton BIT* is reduced by 85.2%, 38.1% and 46.9% compared with BIT*, FMT* and Informed RRT*, respectively. Compared with GBIT* and TOPO, the path length of Halton BIT* in joint space is reduced by 3.9% and 10%. The results demonstrate that the Halton BIT* algorithm significantly improves planning efficiency and maintains path quality, and is feasible for path planning in manipulators.