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A Bio-inspired trajectory planning method for robotic manipulators based on improved bacteria foraging optimization

Zhiqiang Wang1, Jinzhu Peng1, Shuai Ding1

  • 1School of Electrical Engineering, Zhengzhou University, No. 100 of Science Avenue, Zhengzhou 450001, China.

Mathematical Biosciences and Engineering : MBE
|December 14, 2021
PubMed
Summary

This study introduces a new bio-inspired trajectory planning method for robots using an improved bacteria foraging optimization algorithm (IBFOA) and Tau-J* guidance. The method enhances robotic system stability, optimality, and speed.

Keywords:
Tau theoryimproved bacteria foraging optimization algorithminterpolation algorithmmulti-objective optimizationrobotic manipulatortrajectory planning

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Area of Science:

  • Robotics
  • Computational Intelligence
  • Control Systems

Background:

  • Traditional trajectory planning methods face challenges with computational burden and unsmooth paths.
  • Bio-inspired algorithms offer potential for optimizing complex robotic movements.

Purpose of the Study:

  • To propose a novel bio-inspired trajectory planning method for robotic systems.
  • To address computational and smoothness issues in existing trajectory planning algorithms.

Main Methods:

  • Utilizing an improved bacteria foraging optimization algorithm (IBFOA) with adaptive factors and elite preservation.
  • Employing an improved intrinsic Tau jerk (Tau-J*) guidance strategy for higher-order movement.
  • IBFOA determines optimal control points; Tau-J* generates smooth trajectories between these points.

Main Results:

  • The proposed method demonstrates eminent stability in trajectory planning.
  • Simulation tests confirm the optimality of the generated trajectories.
  • The method exhibits significant rapidity capabilities for robotic systems.

Conclusions:

  • The novel bio-inspired trajectory planning method effectively overcomes limitations of traditional approaches.
  • The integration of IBFOA and Tau-J* provides a robust solution for robotic trajectory generation.
  • The developed method offers superior performance in terms of stability, optimality, and speed.