Dynamic quality aware path planning for 6 DoF robotic arms using BiRRT and metaheuristic optimization based on B...

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.