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Motion Planning of six-dof arm robot Based on Improved DDPG Algorithm

Motion Planning of Six-DOF Arm Robot Based on Improved DDPG ...

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第三十九届中国控制会议

作者： Zhuang Li Hongbin Ma Yazhe Ding Chen Wang Ying Jin School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

This paper presents an improved deep deterministic policy gradient algorithm based on a six-dof（six multi-degree-offreedom） arm robot. First, we build a robot model based on the DH（Denavit-Hartenberg） parameters of the UR5 arm robot. Then,we improved the experience pool of the traditional DDPG（deep deterministic policy gradient） algorithm by adding a success experience pool and a collision experience pool. Next, the reward function is improved to increase the degree of successful reward and the penalty of collision. Finally, the training is divided into segments, the front three axes are trained first, and then the six axes. The simulation results in ROS（robot Operating System） show that the improved DDPG algorithm can effectively solve the problem that the six-dof arm robot moves too far in the configuration space. The trained model can reach the target area in five steps. Compared with the traditional DDPG algorithm, the improved DDPG algorithm has fewer training episodes,but achieves better results.

关键词： Motion Planning Improved DDPG six-dof arm robot ROS

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Reinforcement Learning Control for robot arm Grasping Based on Improved DDPG

Reinforcement Learning Control for Robot Arm Grasping Based ...

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第40届中国控制会议

作者： Guangjun Qi Yuan Li School of Automation Beijing Institute of Technology

Although the traditional robot arm grasping control has high control accuracy,its price is based on high-precision hardware and lacks *** order to achieve high control accuracy and flexibility on a relatively inexpensive robot *** paper proposes an improved DDPG（Deep Deterministic Policy Gradient） reinforcement learning algorithm to control the gripping of a robot ***,build a simulation environment for a six-dof（six-degree-of-freedom） manipulator with a gripper in ROS（robot Operating System）.Then,aiming at the shortcomings of traditional DDPG rewards,research and design a composite reward *** at the problem of low sampling efficiency in the free exploration of the robot arm,a batch of teaching data was added to the experience replay pool to improve learning *** simulation experiment results show that under the same number of episode of *** improved DDPG grasping control algorithm has significantly improved the grasping success *** grasping success rate after comprehensive improvement reaches 70%,which is higher than the 36% level of unimproved DDPG.

关键词： DDPG Reward Function Demonstration six-dof arm robot

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