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Multi-objective trajectory optimization method for industrial robots based on improved TD3 algorithm.

Yuhang Xu1, Shuhang Kong2, Xiaowu Kong3

  • 1State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310000, China.

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Summary
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This study introduces a new algorithm for industrial robot trajectory planning, optimizing for safety, path quality, and speed in tight spaces. The method significantly reduces execution time compared to existing approaches.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Industrial robots operating in confined spaces face challenges in trajectory planning.
  • Simultaneously optimizing for collision avoidance, path quality, and execution time is complex.
  • Existing methods often struggle to balance these competing objectives effectively.

Purpose of the Study:

  • To develop a multi-objective integrated trajectory optimization algorithm for industrial robots.
  • To enhance trajectory smoothness, algorithm convergence, and stability.
  • To minimize execution time while ensuring collision-free paths.

Main Methods:

  • Utilized the Twin Delayed Deep Deterministic Policy Gradient (TD3) reinforcement learning framework.
  • Improved motion generation with a Butterworth filter and dynamic noise attenuation.
  • Employed a genetic algorithm for hyperparameter optimization and prioritized experience replay.
  • Designed a composite reward function based on time-distance information.

Main Results:

  • Achieved significant reductions in actual trajectory execution time compared to RRT, manual teaching, traditional TD3, and SAC methods.
  • Demonstrated reductions of 57.03%, 22.94%, 26.05%, and 20.5% respectively.
  • Validated the algorithm through simulations in PyBullet and real-world physical experiments using a Fairino Robot5 arm.

Conclusions:

  • The proposed multi-objective algorithm effectively optimizes industrial robot trajectories in confined spaces.
  • The integrated approach successfully balances collision avoidance, path quality, and execution time.
  • The method offers a substantial improvement in efficiency over existing trajectory planning techniques.