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Updated: Sep 13, 2025

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Application of Multi-Strategy Controlled Rime Algorithm in Path Planning for Delivery Robots.

Haokai Lv1, Qian Qian1, Jiawen Pan1

  • 1School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.

Biomimetics (Basel, Switzerland)
|July 25, 2025
PubMed
Summary

This study enhances robot path planning using the Multi-Strategy Controlled Rime Algorithm (MSRIME). MSRIME improves delivery robot efficiency and reduces costs by optimizing trajectories and avoiding common algorithm pitfalls.

Keywords:
RIME optimization algorithmadaptive searchcontrolled elitecosine annealingdelivery robotfuch chaospath planning

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Area of Science:

  • Robotics and Automation
  • Artificial Intelligence
  • Operations Research

Background:

  • Delivery robots are crucial for automated logistics and unmanned delivery systems.
  • Existing RIME optimization algorithms have limitations in path planning, including poor exploration, lack of diversity, and step size issues.
  • These limitations result in inefficient paths, local optima, and unsmooth trajectories for delivery robots.

Purpose of the Study:

  • To improve the RIME optimization algorithm for delivery robot path planning.
  • To enhance delivery efficiency and reduce operational costs in automated logistics.
  • To address the drawbacks of the standard RIME algorithm in global exploration, search diversity, and step size adjustment.

Main Methods:

  • Proposed the Multi-Strategy Controlled Rime Algorithm (MSRIME) with a multi-strategy collaborative optimization framework.
  • Utilized an infinite folding Fuch chaotic map for enhanced population initialization and solution diversity.
  • Introduced a cooperative mechanism between elite and adaptive search strategies with a dynamic control factor.
  • Incorporated a cosine annealing strategy for improved step size adjustment and reduced parameter sensitivity.

Main Results:

  • MSRIME demonstrated significant advantages over standard algorithms in optimization capability, convergence speed, and stability.
  • The multi-strategy framework effectively mitigated the impact of coordinate and dimensional differences on path quality.
  • Experimental results showed MSRIME's superior performance in path length, running time, and smoothness across various scenarios.

Conclusions:

  • MSRIME offers a novel and effective solution for delivery robot path planning.
  • The algorithm provides practical insights for interdisciplinary research in intelligent logistics and computer science.
  • MSRIME enhances the suitability of automated logistics systems by optimizing robot navigation.