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Path Planning Algorithm for Multi-Locomotion Robot Based on Multi-Objective Genetic Algorithm with Elitist Strategy.

Chong Liu1, Aizun Liu1, Ruchao Wang1

  • 1School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China.

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Summary
This summary is machine-generated.

This study introduces a novel path planning algorithm for multi-locomotion robots (MLRs). The algorithm optimizes multiple objectives like power, time, and safety, enhancing robot navigation capabilities.

Keywords:
elitist strategygenetic algorithmmulti-locomotion robotmulti-objective optimizationpath planning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Multi-locomotion robots (MLRs) require adaptable locomotion for diverse terrains.
  • Different locomotion modes present trade-offs in power consumption, speed, and stability.
  • Existing path planning methods often struggle with multi-objective optimization for MLRs.

Purpose of the Study:

  • To develop an advanced path planning algorithm for MLRs.
  • To address the conflicting objectives of power consumption, time, falling risk, and path smoothness.
  • To improve the global search capability and avoid local optima in MLR path planning.

Main Methods:

  • Implementation of a multi-objective genetic algorithm with an elitist strategy (MLRMOEGA).
  • Introduction of novel map analysis and population diversity expansion operators.
  • Simulation-based validation using MATLAB.

Main Results:

  • The MLRMOEGA algorithm effectively optimizes multiple objectives simultaneously.
  • Demonstrated superior performance compared to traditional genetic algorithms in simulations.
  • Successfully generated paths aligned with decision-maker preferences under varying objective weights.
  • Enhanced global search ability and reduced susceptibility to local optima.

Conclusions:

  • The proposed MLRMOEGA algorithm offers an effective solution for complex MLR path planning.
  • The algorithm provides a flexible framework for balancing competing navigation objectives.
  • Significant improvements in global search and optimization capabilities were achieved.