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Path Planning for Unmanned Delivery Robots Based on EWB-GWO Algorithm.

Yuan Luo1, Qiong Qin1, Zhangfang Hu1

  • 1Key Laboratory of Optoelectronic Information Sensing and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

Sensors (Basel, Switzerland)
|February 28, 2023
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Summary

An improved Gray Wolf Optimization (GWO) algorithm, EWB-GWO, enhances autonomous path planning for mobile robots in complex environments. This method achieves optimal path length and smoothness, outperforming other metaheuristics.

Keywords:
BOA algorithmGWO algorithmadaptive nonlinear inertia weightsmobile robotopposition-based learningpath planningtwo-dimensional chaotic mapping

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Area of Science:

  • Robotics and Artificial Intelligence
  • Optimization Algorithms
  • Computational Intelligence

Background:

  • Mobile robots are increasingly vital for tasks like unmanned delivery.
  • Autonomous navigation in complex environments is crucial for robot efficiency.
  • Existing optimization algorithms like GWO have limitations in path planning.

Purpose of the Study:

  • To propose an enhanced Gray Wolf Optimization (GWO) algorithm for autonomous path planning in complex environments.
  • To improve convergence speed, accuracy, and exploration-exploitation balance of GWO.
  • To reduce the time complexity of path planning for mobile robots.

Main Methods:

  • Modified GWO with a two-dimensional Tent-Sine coupled chaotic mapping for initial population diversity.
  • Incorporated elite strategy-based opposition-based learning, adaptive inertia weight, and random wandering from Butterfly Optimization Algorithm (BOA).
  • Enhanced initial population generation using Bresenham's line algorithm for visual-field line detection.

Main Results:

  • The proposed EWB-GWO algorithm demonstrates competitive performance against similar metaheuristics.
  • Achieved optimal path length and superior smoothness metrics in path planning simulations.
  • Reduced the number of iterations and time complexity for path planning tasks.

Conclusions:

  • The EWB-GWO algorithm offers a significant improvement for mobile robot autonomous path planning.
  • The integration of chaotic mapping and BOA strategies enhances GWO's effectiveness in complex scenarios.
  • This approach provides a robust and efficient solution for real-world robotic navigation challenges.