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Trajectory Planning of Spraying Robot Based on Multi Strategy Improved Beluga Optimization Algorithm.

Yifang Wen1,2, Renzhong Wang1,2, Ting Huang1,2

  • 1School of Mechanical and Electrical Engineering, Suzhou Polytechnic University, Suzhou 215000, China.

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Summary
This summary is machine-generated.

This study introduces an improved beluga whale optimization algorithm for robot trajectory planning on complex surfaces. The enhanced method ensures smoother, more accurate paths, significantly improving performance for plasma-spraying robots.

Keywords:
beluga whale algorithmconstrained optimizationobjective functionspraying robottrajectory planning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Plasma-spraying robots require precise trajectory planning for complex surfaces.
  • Existing optimization algorithms struggle with balancing global search and local development.
  • Kinematics, dynamics, and environmental constraints are critical for 6-DOF mobile plasma robots.

Purpose of the Study:

  • To propose an improved beluga whale optimization (IBWO) algorithm for robot trajectory planning.
  • To enhance global search and local development capabilities of the original beluga whale algorithm.
  • To optimize trajectory planning considering time, energy consumption, and smoothness for complex surfaces.

Main Methods:

  • Analysis of system architecture, kinematics, and trajectory planning constraints for a 6-DOF mobile plasma robot.
  • Development of a constrained-objective optimization function.
  • Integration of tent chaotic mapping and sine cosine algorithm into the beluga whale optimization algorithm (IBWO).

Main Results:

  • The IBWO algorithm demonstrates superior convergence accuracy, stability, and comprehensive performance compared to original beluga optimization and particle swarm optimization.
  • Experimental validation using standard test functions and trajectory planning metrics confirmed IBWO's effectiveness.
  • The generated joint trajectories are smooth and satisfy constraints, proving suitability for complex surface spraying tasks.

Conclusions:

  • The proposed IBWO algorithm offers a significant advancement in trajectory planning for plasma-spraying robots.
  • The enhanced algorithm effectively addresses the limitations of traditional optimization methods.
  • IBWO provides a robust solution for achieving high-quality trajectory planning on intricate surfaces.