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Operation of the Collaborative Composite Manufacturing CCM System

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Published on: October 1, 2019

Trajectory planning for robotic manipulator based on improved DDPG algorithm.

Dehai Yu¹, Weiwei Sun¹, Zhuangzhuang Luan¹

¹Institute of Automation, Qufu Normal University, Qufu, 273165, China.

ISA Transactions

|March 12, 2026

Summary

This summary is machine-generated.

This study introduces an improved deep deterministic policy gradient (DDPG) algorithm for robotic manipulator trajectory planning. The enhanced DDPG algorithm achieves faster learning convergence and higher success rates in time-optimal planning tasks.

Keywords:

Improved deep deterministic policy gradient (DDPG)Radial basis function neural network Robotic manipulator SumTree sample pool Trajectory planning

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Area of Science:

Robotics
Artificial Intelligence
Machine Learning

Background:

Deep reinforcement learning (DRL) is increasingly used for robotic manipulator planning.
Conventional DRL methods face challenges with slow convergence and low success rates in industrial trajectory planning.

Purpose of the Study:

To propose an improved deep deterministic policy gradient (DDPG) algorithm for time-optimal robotic manipulator trajectory planning.
To enhance learning convergence speed and success rates compared to traditional DRL approaches.

Main Methods:

Integration of a radial basis function neural network for nonlinear function approximation during parameter training.
Utilization of the gradient descent algorithm for neural network weight updates.
Implementation of a SumTree sample pool for efficient high-quality sample selection.

Main Results:

The improved DDPG algorithm demonstrates steady changes in robotic manipulator joint torques and angles.
Significant improvements in algorithm utilization rate for trajectory planning tasks.
Enhanced learning efficiency compared to the traditional DDPG algorithm.

Conclusions:

The proposed improved DDPG algorithm effectively addresses limitations of conventional DRL in robotic manipulator trajectory planning.
The method offers a more efficient and successful strategy for time-optimal path planning in industrial robotics.