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On obstacle avoidance path planning in unknown 3D environments: A fluid-based framework.

Jianfa Wu1, Honglun Wang2, Menghua Zhang2

  • 1School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China; Shenyuan Honors College of Beihang University, Beijing 100191, China; Science and Technology on Aircraft Control Laboratory, Beihang University, Beijing 100191, China.

ISA Transactions
|December 4, 2020
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Summary
This summary is machine-generated.

This study introduces a novel fluid-based path planning framework using the Interfered Fluid Dynamic System (IFDS) to overcome local optima in unknown 3D environments. The method effectively guides agents around complex, non-convex obstacles for safe navigation.

Keywords:
Interfered Fluid Dynamic System (IFDS)Local optimumNature-inspired methodologyNon-convex obstaclesObstacle avoidancePath planningUnknown environments

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computational Geometry

Background:

  • Path planning in unknown 3D environments presents challenges due to limited sensor data.
  • Non-convex obstacles can trap agents in local optima, hindering successful navigation.
  • Existing methods struggle with real-time obstacle avoidance and local optimum escape.

Purpose of the Study:

  • To propose a novel fluid-based path planning framework for anti-local-optimum obstacle avoidance in unknown 3D environments.
  • To extend the Interfered Fluid Dynamic System (IFDS) for enhanced obstacle avoidance capabilities.
  • To enable autonomous navigation and escape from challenging local optima caused by non-convex obstacles.

Main Methods:

  • Discretization of the agent's detection region and generation of Spherical Virtual Obstacles (SVOs).
  • Integration of the Interfered Fluid Dynamic System (IFDS) algorithm for repulsive effect generation.
  • Introduction of direction coefficient and sink-heading angular rate adjustments to modify IFDS for non-convex obstacles.
  • Application of receding horizon control to improve overall obstacle avoidance performance.

Main Results:

  • The proposed framework successfully transforms obstacle avoidance in unknown environments into avoiding known SVOs.
  • Modified IFDS effectively addresses local optima in environments with non-convex obstacles.
  • Simulations demonstrate the agent's ability to autonomously escape complex 3D local optimum features like wall-like and cave-like obstacles.
  • Safe and successful navigation to the destination was achieved in challenging simulated environments.

Conclusions:

  • The novel fluid-based path planning framework provides an effective solution for anti-local-optimum obstacle avoidance in unknown 3D environments.
  • The integration of IFDS with specific adjustment strategies offers robust performance against non-convex obstacles.
  • The method enhances agent autonomy and navigation safety in complex and unpredictable environments.