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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Robots require precise trajectory tracking and obstacle avoidance in dynamic environments.
  • Existing methods struggle to balance path-following accuracy with real-time obstacle avoidance.

Purpose of the Study:

  • To propose a real-time trajectory planning framework for robots.
  • To address the challenge of simultaneous accurate trajectory tracking and dynamic obstacle avoidance.
  • To enhance robot safety and performance in unstructured environments.

Main Methods:

  • An enhanced artificial potential field (APF) approach with virtual target points for path precision.
  • A dynamic obstacle repulsion model incorporating velocity and acceleration for proactive avoidance.
  • A probabilistic obstacle motion prediction framework using motion pattern analysis.

Main Results:

  • Achieved a 55.8% reduction in trajectory tracking error compared to improved APF methods.
  • Demonstrated a 41.5% decrease in tracking error relative to Dynamic Movement Primitives (DMP) baselines.
  • Validated superior robustness and safety performance in complex, simulated dynamic obstacle scenarios.

Conclusions:

  • The proposed framework effectively balances trajectory tracking and obstacle avoidance.
  • The integration of predictive motion analysis enhances robot adaptability and safety.
  • The method shows significant improvements over existing APF and DMP approaches.