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Time Optimal Trajectory Planing Based on Improved Sparrow Search Algorithm.

Xiaofeng Zhang1, Fan Xiao1, XiLiang Tong1,2,3

  • 1Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China.

Frontiers in Bioengineering and Biotechnology
|April 8, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an improved Sparrow Search Algorithm (SSA) for efficient robot trajectory planning. The enhanced method significantly reduces computation time and ensures smooth, time-optimal robot movements.

Keywords:
configuration spaceimproved sparrow search algorithminverse kinematicstime optimizationtrajectory planning

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Area of Science:

  • Robotics
  • Control Systems
  • Computational Intelligence

Background:

  • Trajectory planning is crucial for robot motion, involving path planning, inverse kinematics, and optimization.
  • Existing methods can be computationally intensive, impacting real-time applications.
  • Time-optimal trajectory planning requires efficient algorithms for both inverse kinematics and optimization.

Purpose of the Study:

  • To develop a highly smooth and time-saving approach to robot trajectory planning.
  • To improve the efficiency of inverse kinematics solutions.
  • To enhance the performance of trajectory optimization algorithms for time-optimality.

Main Methods:

  • Partitioning the joint space for faster inverse kinematics calculations.
  • Proposing an improved Sparrow Search Algorithm (SSA) for time-optimal trajectory planning.
  • Utilizing Tent chaotic mapping for initial population generation and an adaptive step factor for SSA enhancement.

Main Results:

  • Achieved a 40% reduction in inverse kinematics solution time.
  • Demonstrated improved convergence speed and global search capability of the enhanced SSA.
  • Ensured smooth robot trajectories with optimized time-optimal planning.

Conclusions:

  • The proposed joint space partitioning significantly reduces computational load in trajectory planning.
  • The enhanced SSA provides a robust and efficient method for time-optimal trajectory optimization.
  • The combined approach leads to faster, smoother, and computationally efficient robot motion planning.