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Updated: May 14, 2026

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
06:58

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Published on: November 6, 2015

Trajectory Planning Method for a Robotic Arm Based on an Improved Multi-Objective Golden Jackal Optimization

Juan Wei1, Jiangle Wang1, Manzhi Yang1

  • 1School of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710001, China.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an improved multi-objective golden jackal optimization algorithm for robotic arm trajectory planning. The novel method optimizes operation time, motion impact, and energy consumption, enhancing efficiency and stability.

Keywords:
3-5-3 piecewise polynomial interpolationgolden jackal optimizationmulti-objective optimizationrobotic armtrajectory planning

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Published on: October 14, 2017

Area of Science:

  • Robotics
  • Optimization Algorithms
  • Control Systems

Background:

  • Industrial robotic arm trajectory planning faces challenges in simultaneously optimizing operation time, motion impact, and energy consumption.
  • Existing optimization methods often struggle to achieve a well-distributed and highly convergent Pareto-optimal solution set.

Purpose of the Study:

  • To propose a novel multi-objective optimization framework for industrial robotic arm trajectory planning.
  • To simultaneously optimize execution time, motion impact, and energy consumption.
  • To enhance the global exploration capabilities and convergence stability of optimization algorithms.

Main Methods:

  • Developed an improved multi-objective golden jackal optimization (IMGJO) algorithm by extending the original Golden Jackal Optimization.
  • Integrated tent chaotic mapping, opposition-based learning, and elitism preservation into the IMGJO algorithm.
  • Combined the IMGJO algorithm with a 3-5-3 polynomial interpolation scheme for kinematically constrained trajectory planning.

Main Results:

  • The IMGJO framework demonstrated superior performance compared to state-of-the-art algorithms in convergence speed and Pareto solution set quality.
  • Experimental validation on a Yaskawa HP-20D robotic arm showed significant reductions in operation time (2.42%), joint impacts (up to 75.82%), and energy consumption (up to 27.11%).
  • Generated trajectories were smooth, continuous, and dynamically feasible, improving operational efficiency and stability.

Conclusions:

  • The proposed IMGJO algorithm effectively addresses the multi-objective optimization challenges in robotic arm trajectory planning.
  • The framework achieves a comprehensive optimization of key performance metrics, leading to substantial improvements in robotic arm operation.
  • The method offers a promising approach for enhancing the performance and reliability of industrial robotic systems.