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BGSE-RRT*: A Goal-Guided and Multi-Sector Sampling-Expansion Path Planning Algorithm for Complex Environments.

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Summary
This summary is machine-generated.

BGSE-RRT* enhances robot path planning in complex environments by improving efficiency and path quality. This novel algorithm achieves significant reductions in planning time and path length while increasing safety distances.

Keywords:
RRT*bi-tree adaptive switchinggoal-guidedlocal multi-sectorpath planningsampling-point-guided

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Science

Background:

  • Conventional RRT* algorithms exhibit limitations in planning efficiency and path quality for robot navigation in complex ground environments.
  • Efficient and safe robot path planning is crucial for autonomous systems operating in dynamic and unstructured settings.

Purpose of the Study:

  • To introduce BGSE-RRT* (Bi-tree Cooperative, Goal-guided, low-discrepancy Sampling, multi-sector Expansion), an advanced algorithm designed to overcome the inefficiencies of traditional RRT*.
  • To improve the speed, path quality, and safety of robot path planning in complex terrains.

Main Methods:

  • BGSE-RRT* employs a nonlinear switching probability via bi-tree cooperative adaptive switching with KD-Tree acceleration for balanced exploration and convergence.
  • Goal-guided expansion with dynamic target binding and adaptive step size, coupled with multi-constraint feasibility checks, accelerates convergence.
  • Multi-sector expansion using low-discrepancy sequences and sampling-point-guided expansion address blocked paths, while B-spline smoothing enhances trajectory continuity.

Main Results:

  • BGSE-RRT* demonstrated significant performance improvements across five simulation environments and ROS/real-robot validation.
  • Reductions in planning time by up to 84.71% compared to other RRT* variants.
  • Improvements in path length (2.94-6.88%) and safety distance (20.68-48.33%) were observed, with a 100% trajectory-tracking success rate in real-world tests.

Conclusions:

  • BGSE-RRT* offers a substantial advancement in robot path planning, providing a more efficient, safer, and higher-quality solution for complex environments.
  • The algorithm's adaptive strategies and novel expansion methods effectively address the limitations of existing approaches.
  • Validated through simulations and real-world robotics, BGSE-RRT* proves its efficacy and reliability for practical applications.