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An autonomous mobile robot path planning strategy using an enhanced slime mold algorithm.

Ling Zheng1,2, Chengzhi Hong3, Huashan Song4

  • 1School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China.

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An enhanced slime mold algorithm (ESMA) improves mobile robot path planning by integrating adaptive techniques and artificial potential fields. This method achieves shorter, collision-free paths in dynamic environments, outperforming existing algorithms.

Keywords:
artificial potential fieldautonomous mobile robotsdynamic environmentpath planningslime mold algorithm

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Path planning is crucial for autonomous mobile robots, impacting efficiency and energy consumption.
  • The primary slime mold algorithm (SMA) offers robustness but suffers from local optimization and lacks dynamic obstacle avoidance.
  • Existing enhancements like SMA-AGDE and LRSMA show improvements but can be further optimized.

Purpose of the Study:

  • To develop an enhanced slime mold algorithm (ESMA) for improved mobile robot path planning.
  • To address the limitations of SMA, specifically local optimization and dynamic obstacle avoidance.
  • To validate the effectiveness of ESMA in finding optimal, collision-free paths in complex environments.

Main Methods:

  • The enhanced slime mold algorithm (ESMA) was developed by incorporating adaptive techniques for global search.
  • An artificial potential field was integrated into ESMA to enable dynamic obstacle avoidance.
  • ESMA's performance was evaluated against SMA, SMA-AGDE, and LRSMA in simulated environments.

Main Results:

  • ESMA demonstrated a significant reduction in minimum path length compared to SMA, SMA-AGDE, and LRSMA (3.92%, 8.93%, 2.73% average reduction, respectively).
  • The algorithm successfully generated shortest collision-free paths in both static and dynamic environments.
  • ESMA also showed reductions in path minimum values and processing times compared to other algorithms.

Conclusions:

  • The proposed ESMA algorithm effectively enhances mobile robot path planning capabilities.
  • ESMA overcomes the limitations of traditional SMA by improving global search and dynamic obstacle avoidance.
  • ESMA offers a robust and efficient solution for real-world mobile robot navigation challenges.