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A trajectory planning method for a casting sorting robotic arm based on a nature-inspired Genghis Khan shark

Chengjun Wang1,2, Xingyu Yao2, Fan Ding2

  • 1School of Artificial Intelligence, Anhui University of Science and Technology, Huainan 232001, China.

Mathematical Biosciences and Engineering : MBE
|March 8, 2024
PubMed
Summary

This study introduces a novel time-optimal trajectory planning method for casting sorting robotic arms, utilizing a Genghis Khan shark (GKS) heuristic algorithm and segmented polynomials. The approach enhances trajectory smoothness and efficiency, outperforming traditional methods.

Keywords:
Genghis Khan shark optimizerPSO algorithmcasting sorting manipulatorpolynomial interpolationtrajectory planning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Casting sorting robotic arms require efficient and smooth trajectories for optimal performance.
  • Existing trajectory planning methods may face challenges with local optima and convergence speed.

Purpose of the Study:

  • To develop a time-optimal trajectory planning method for casting sorting robotic arms.
  • To improve trajectory smoothness, efficiency, and convergence using a novel heuristic algorithm.

Main Methods:

  • Constructed a robotic arm model and analyzed its workspace.
  • Employed segmented interpolation polynomials (cubic and quintic) to solve for trajectory parameters.
  • Integrated a dynamic nonlinear learning factor into the Particle Swarm Optimization (PSO) algorithm, inspired by Genghis Khan shark (GKS) behavior.
  • Simulated GKS-inspired behaviors and optimized a target function for time optimality.

Main Results:

  • The proposed method resulted in a smooth and continuous trajectory velocity for the robotic arm.
  • Optimized overall runtime within defined constraints.
  • The improved PSO algorithm demonstrated superior convergence compared to the traditional PSO.
  • The GKS-inspired approach reduced the probability of getting trapped in local optima.

Conclusions:

  • The developed trajectory planning method effectively meets the efficiency and smoothness requirements for casting sorting robotic arms.
  • The GKS-inspired optimization strategy enhances convergence and avoids local optima, confirming the algorithm's effectiveness.