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A Real-Time Reaction Obstacle Avoidance Algorithm for Autonomous Underwater Vehicles in Unknown Environments.

Zheping Yan1, Jiyun Li2, Gengshi Zhang3

  • 1Marine Assembly and Automatic Technology Institute, College of Automation, Harbin Engineering University, Harbin 150001, China. yanzheping@hrbeu.edu.cn.

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|February 3, 2018
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Summary

A new real-time reaction obstacle avoidance algorithm (RRA) enables autonomous underwater vehicles (AUVs) to navigate complex, unknown environments using forward-looking sonar. This method ensures safe, smooth, and near-optimal path planning for AUVs.

Keywords:
autonomous underwater vehicleforward looking sonarobstacle avoidancereaction obstacle avoidance algorithm

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Area of Science:

  • Robotics
  • Autonomous Systems
  • Underwater Navigation

Background:

  • Autonomous Underwater Vehicles (AUVs) require robust obstacle avoidance for operating in unknown, complex environments.
  • Existing algorithms may struggle with dynamic terrains and U-shaped obstacles, leading to potential entrapment.
  • Forward-looking sonar (FLS) provides essential environmental data for real-time navigation decisions.

Purpose of the Study:

  • To propose a novel real-time reaction obstacle avoidance algorithm (RRA) for AUVs.
  • To enhance AUV adaptability and responsiveness in unknown underwater terrains.
  • To address limitations in current obstacle avoidance strategies, including U-shape entrapment.

Main Methods:

  • The real-time reaction obstacle avoidance algorithm (RRA) is developed, processing FLS data in five distinct steps.
  • The largest polar angle algorithm (LPAA) is employed to simplify irregular obstacle outlines into convex polygons.
  • An outline memory algorithm is implemented to resolve issues with U-shaped obstacle avoidance and prevent trapping.

Main Results:

  • Simulations were conducted in three distinct unknown obstacle scenarios.
  • The RRA demonstrated effective obstacle detection and avoidance capabilities.
  • Generated AUV trajectories were consistently safe, smooth, and near-optimal.

Conclusions:

  • The proposed RRA provides an effective solution for real-time obstacle avoidance in AUVs operating in unknown environments.
  • The LPAA and outline memory algorithm contribute to simplified processing and enhanced navigation safety.
  • The algorithm shows significant potential for improving AUV mission success rates in complex underwater terrains.