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Related Experiment Video

Updated: Feb 24, 2026

Operation of the Collaborative Composite Manufacturing CCM System
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Dynamic quality aware path planning for 6 DoF robotic arms using BiRRT and metaheuristic optimization based on B

Abdelrahman T Elgohr1,2, Maher Rashad3, Eman M El-Gendy4

  • 1Mechatronics Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt. atarek@horus.edu.eg.

Scientific Reports
|February 22, 2026
PubMed
Summary

This study introduces a new path planning framework for industrial robots. It significantly reduces motion jerk by 94-96% using optimization algorithms, ensuring safer and smoother operation in complex workspaces.

Keywords:
B-splineBi-RRTGWOMetaheuristic optimizationPath planningWGA

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Mechanical Engineering

Background:

  • Industrial robots require safe, precise motion in cluttered workspaces.
  • Existing path planning methods struggle with dynamic constraints and motion quality.

Purpose of the Study:

  • To develop and evaluate a two-stage framework for path planning and optimization of a 6-DOF industrial robotic arm.
  • To minimize trajectory length, energy consumption, and joint jerk for enhanced motion quality.

Main Methods:

  • A B-spline and bidirectional RRT-Connect planner generated a collision-free reference motion.
  • Whale Genetic Algorithm (WGA) and Grey Wolf Optimizer (GWO) optimized the baseline trajectory.
  • Optimizers minimized a composite objective including trajectory length, energy, and jerk.

Main Results:

  • The optimized trajectories reduced jerk by 94-96% compared to the baseline.
  • Minimal increases in trajectory length and energy consumption were observed.
  • Dynamically smooth, collision-free trajectories adhering to kinematic constraints were achieved.

Conclusions:

  • The proposed framework effectively minimizes jerk for industrial robot motion.
  • This methodology offers an implementation-ready solution for energy-efficient, smooth robot movements in complex environments.