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Bilevel Optimization-Based Time-Optimal Path Planning for AUVs.

Xuliang Yao1, Feng Wang2, Jingfang Wang3

  • 1College of Automation, Harbin Engineering University, Nantong Road No.145, Harbin 150001, China. yaoxuliang@hrbeu.edu.cn.

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|November 30, 2018
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Summary
This summary is machine-generated.

This study introduces a novel bilevel optimization (BIO) path planner for autonomous underwater vehicles (AUVs) in ocean currents. The BIO scheme efficiently finds time-optimal and energy-optimal paths, outperforming traditional methods.

Keywords:
autonomous underwater vehicle (AUV)bilevel optimization (BIO)energy-optimal pathpath planning

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Area of Science:

  • Robotics
  • Ocean Engineering
  • Artificial Intelligence

Background:

  • Autonomous Underwater Vehicles (AUVs) require efficient path planning in complex marine environments.
  • Ocean currents and obstacles pose significant challenges to AUV navigation.
  • Existing path planning methods can be limited by discrete motion constraints.

Purpose of the Study:

  • To develop a time-optimal path planner for AUVs using a bilevel optimization (BIO) scheme.
  • To optimize AUV navigation in grid-based environments considering ocean currents.
  • To enhance path planning efficiency and reduce discrete state transitions.

Main Methods:

  • Implementation of a bilevel optimization (BIO) scheme for path planning.
  • Upper level: Ant Colony Algorithm for collision-free channel identification.
  • Lower level: Quantum-behaved Particle Swarm Optimization for energy-optimal pathfinding within the channel.

Main Results:

  • The proposed BIO scheme demonstrated higher computation efficiency compared to the sliding wavefront expansion scheme.
  • Achieved a time-optimal and energy-optimal path for AUV navigation.
  • Prevented discrete state transitions, allowing for more continuous motion.

Conclusions:

  • The BIO scheme offers an effective approach for AUV path planning in dynamic ocean environments.
  • Integration of evolutionary algorithms enhances navigation efficiency.
  • The method provides a robust solution for complex underwater navigation challenges.