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A Novel Multi-Objective Trajectory Planning Method for Robots Based on the Multi-Objective Particle Swarm

Jiahui Wang1, Yongbo Zhang1,2, Shihao Zhu1

  • 1School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China.

Sensors (Basel, Switzerland)
|December 17, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multi-objective trajectory planning method for space robots, optimizing travel time, energy, and smoothness. The approach utilizes B-spline functions and an improved particle swarm optimization algorithm for enhanced performance in space exploration.

Keywords:
B-splineMOPSOPuma560 robotmulti-objective trajectory planning

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Area of Science:

  • Robotics
  • Space Exploration
  • Computational Optimization

Background:

  • Space robot performance is critical for exploration missions.
  • Key performance indicators include travel time, energy consumption, and trajectory smoothness.
  • Efficient trajectory planning is essential for maximizing robot functionality.

Purpose of the Study:

  • To propose a multi-objective trajectory planning method for space robots.
  • To optimize critical performance indexes: travel time, energy consumption, and smoothness.
  • To ensure trajectories meet practical engineering requirements for space missions.

Main Methods:

  • Kinematic and dynamic analysis of the Puma560 robot.
  • Construction of joint space trajectories using fifth-order B-spline functions.
  • Optimization via an improved multi-objective particle swarm optimization (MOPSO) algorithm.

Main Results:

  • Achieved continuous position, velocity, acceleration, and jerk for robot joints.
  • Demonstrated good distribution uniformity, convergence, and diversity of the Pareto front.
  • Successfully optimized multiple objectives to meet engineering demands.

Conclusions:

  • The proposed MOPSO-based trajectory planning method effectively balances multiple performance objectives.
  • The method provides optimized trajectories suitable for real-world space exploration applications.
  • Visualization confirmed the successful implementation of the optimized robot motion.