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Summary

This study introduces a hybrid reinforcement learning method combining simulated annealing (SA) and proximal policy optimization (PPO) for robotic arm trajectory planning. The novel approach enhances object grasping in complex environments, achieving higher success rates and efficiency.

Keywords:
collision-free graspingproximal policy optimizationreinforcement learningrobotic trajectory planningsim-to-real transfersimulated annealingunstructured environments

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Area of Science:

  • Robotics and Artificial Intelligence
  • Machine Learning
  • Control Systems

Background:

  • Robotic manipulation in unstructured environments faces challenges like local optima and convergence issues.
  • Real-time interaction and adaptability are crucial for industrial automation.

Purpose of the Study:

  • To develop a hybrid reinforcement learning framework for precise, collision-free robotic arm trajectory planning.
  • To enhance the grasping of randomly appearing objects amidst dynamic obstacles.

Main Methods:

  • A hybrid approach combining Simulated Annealing (SA) with Proximal Policy Optimization (PPO).
  • A probabilistically enhanced simulation environment with a 20% obstacle generation rate.
  • An optimized state-action space with 12-dimensional environment coding and 6-DoF joint control.

Main Results:

  • Achieved a 6.52% increase in success rate (98% vs. 92%) compared to baseline PPO.
  • Reduced steps per set by 7.14%.
  • Validated robust simulation-to-reality transfer on an AUBO-i5 robotic arm.

Conclusions:

  • The SA-PPO algorithm effectively balances exploration and convergence for adaptive robot manipulation.
  • This research establishes a new paradigm for real-time robotic response to environmental uncertainty in industrial settings.