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Path Planning for Mobile Robot Based on Improved Bat Algorithm.

Xin Yuan1, Xinwei Yuan1, Xiaohu Wang1

  • 1College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China.

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|July 2, 2021
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Summary
This summary is machine-generated.

This study introduces an improved bat algorithm (BA) for mobile robot path planning, enhancing convergence and stability. The hybrid approach significantly reduces path length and improves obstacle avoidance compared to existing methods.

Keywords:
Cauchy disturbancebat algorithmdynamic window approachlogarithmic decreasing strategypath planning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • The standard Bat Algorithm (BA) suffers from slow convergence, low precision, and poor stability.
  • Mobile robot path planning requires efficient global optimization and dynamic obstacle avoidance.

Purpose of the Study:

  • To enhance the Bat Algorithm (BA) for improved performance in mobile robot path planning.
  • To combine the improved BA with the Dynamic Window Approach (DWA) for robust navigation.

Main Methods:

  • Developed an improved Bat Algorithm (BA) incorporating a logarithmic decreasing strategy and Cauchy disturbance.
  • Integrated the enhanced BA with the Dynamic Window Approach (DWA) for path planning.
  • Utilized an undirected weighted graph with virtual points for path switching strategies.

Main Results:

  • The improved Bat Algorithm (BA) demonstrated superior performance over the basic BA and Particle Swarm Optimization (PSO) in finding optimal solutions.
  • The hybrid path planning method significantly reduced path length compared to the standard Dynamic Window Approach (DWA).
  • Simulations confirmed the effectiveness of the path switching strategy for dynamic obstacle avoidance.

Conclusions:

  • The enhanced Bat Algorithm (BA) offers improved convergence rate, precision, and stability for optimization tasks.
  • The proposed hybrid BA-DWA path planning method is effective for mobile robots, achieving shorter paths and better obstacle avoidance.
  • The integration of path switching strategies enhances the adaptability of mobile robots in dynamic environments.