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Bridge Points Guided Neural Motion Planning in Complex Environments with Narrow Passages.

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This study introduces a hybrid path planning framework that efficiently navigates complex environments with narrow passages. It combines advanced sampling, structural abstraction, and neural prediction for faster, more reliable robot navigation.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Motion Planning

Background:

  • Intelligent robotic systems require robust motion and path planning for navigation in complex environments.
  • Traditional sampling-based planners struggle with narrow passages, while learning-based methods can yield infeasible paths.
  • A significant challenge lies in generating collision-free trajectories efficiently, especially in obstacle-rich configuration spaces (C-spaces).

Purpose of the Study:

  • To develop a hybrid path planning framework that overcomes the limitations of existing methods in narrow passage scenarios.
  • To improve the success rate and reduce planning time for robotic navigation in challenging environments.
  • To combine the strengths of sampling-based and learning-based approaches for more reliable and efficient path generation.

Main Methods:

  • A hybrid framework integrating improved sampling, structural abstraction, and neural prediction.
  • A modified bridge-test sampler with directional perturbations and corridor checks for narrow passage sampling.
  • Clustering of samples into representative bridge points forming a global graph, queried using greedy heuristic search and a neural local segment generator.

Main Results:

  • The proposed method significantly outperforms classical and learning-based baselines in success rate and planning time.
  • Validation across 2D maze maps, 3D voxel environments, and a complex 12-DOF manipulator task demonstrated superior performance.
  • The framework retains a generalized form of probabilistic completeness with an optional repair module.

Conclusions:

  • The hybrid framework offers a significant advancement in motion planning, particularly for environments dominated by narrow passages.
  • This approach effectively balances planning efficiency and path reliability, addressing key limitations of prior methods.
  • The method provides a robust solution for intelligent robotic navigation in complex, cluttered C-spaces.