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A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
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Robotic arm trajectory optimization based on multiverse algorithm.

Junjie Liu1, Hui Wang1, Xue Li2

  • 1School of Mechanical and Electronic Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.

Mathematical Biosciences and Engineering : MBE
|March 11, 2023
PubMed
Summary
This summary is machine-generated.

An improved multiverse algorithm (IMVO) optimizes industrial manipulator trajectory planning for time, energy, and impact. This enhanced algorithm addresses limitations of the original multiverse algorithm, improving convergence speed and global search capabilities.

Keywords:
optimal energy consumptionoptimal impactoptimal timeoptimization algorithmtrajectory planning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Industrial manipulators require efficient trajectory planning for tasks involving time, energy, and impact minimization.
  • Existing multi-universe algorithms (MVO) offer robustness but suffer from slow convergence and local optima.
  • Six-degree-of-freedom (6-DoF) manipulators present complex optimization challenges in trajectory planning.

Purpose of the Study:

  • To propose an improved multiverse algorithm (IMVO) for optimizing the trajectory planning of 6-DoF industrial manipulators.
  • To enhance convergence speed and global search capability compared to the standard MVO.
  • To simultaneously optimize time, energy consumption, and impact in manipulator trajectory planning.

Main Methods:

  • Modification of the MVO for multi-objective optimization to derive Pareto solution sets.
  • Implementation of an improved wormhole probability curve and adaptive parameter adjustment.
  • Integration of population mutation fusion to enhance global search.
  • Construction of a weighted objective function for optimization using IMVO.

Main Results:

  • The IMVO algorithm demonstrates improved timeliness in 6-DoF manipulator trajectory operations within constraints.
  • Significant improvements were observed in optimizing time, energy consumption, and impact.
  • The enhanced algorithm overcomes the local optimum problem and accelerates convergence.

Conclusions:

  • The proposed IMVO is an effective method for optimizing multi-objective trajectory planning in 6-DoF industrial manipulators.
  • IMVO offers a superior approach to balancing time, energy, and impact objectives.
  • This research contributes to more efficient and robust industrial robotic operations.