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Hybrid path planning algorithm for underactuated AUV based on RRT star and APF.

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  • 1School of Aeronautics and Astronautics, Guilin University of Aerospace Technology, Guilin, 541004, China.

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|August 6, 2025
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Summary
This summary is machine-generated.

This study introduces a new path planning algorithm for autonomous underwater vehicles (AUVs) that significantly improves efficiency and path quality. The novel method enhances real-time performance and ensures kinematic feasibility in complex underwater environments.

Keywords:
Adaptive step sizeArtificial potential fieldAutonomous underwater vehicleCubic non-uniform B-spline curveRandom sampling

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Marine Engineering

Background:

  • Autonomous Underwater Vehicles (AUVs) face challenges with kinematic constraints and real-time path planning.
  • Existing RRT-based methods often struggle with efficiency and path quality in complex environments.

Purpose of the Study:

  • To develop a novel path planning algorithm for AUVs that addresses kinematic constraints and real-time requirements.
  • To improve sampling efficiency, path quality, and trajectory smoothness compared to conventional methods.

Main Methods:

  • Proposed Directional Cone and Goal-Biased Dynamic Artificial Potential Field RRT* (DCGB-DAPF-RRT*) algorithm.
  • Integration of Directional Cone sampling, Goal-Biased sampling, Adaptive Step Length, and Dynamic Artificial Potential Field.
  • Utilized redundant node pruning and cubic non-uniform B-spline interpolation for trajectory enhancement.

Main Results:

  • Reduced path planning time by 50.0-73.6%.
  • Decreased the number of nodes by 71.0-77.8% and iterations by 61.0-85.0%.
  • Achieved a 100% success rate with a path length of 1766.1 m and a maximum turning angle of 11.35°.

Conclusions:

  • The DCGB-DAPF-RRT* algorithm effectively satisfies AUV motion constraints and real-time demands.
  • The proposed method offers significant improvements in efficiency, path quality, and kinematic feasibility for AUV path planning.
  • Demonstrated superior performance in complex environments, ensuring successful navigation.