Integrated Intelligent Control of Redundant Degrees-of-Freedom Manipulators via the Fusion of Deep Reinforcement Learning and Forward Kinematics Models

Redundant degree-of-freedom (DOF) manipulators offer increased flexibility and are better suited for obstacle avoidance, yet precise control of these systems remains a significant challenge. This paper addresses the issues of slow training convergence and suboptimal stability that plague current dee...

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Bibliographic Details
Published in:Machines (Basel) 2024-10, Vol.12 (10), p.667
Main Authors: Chen, Yushuo, Su, Shijie, Ni, Kai, Li, Cunjun
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Control algorithms
Control stability
Control systems
Control tasks
Convergence
Deep learning
Degrees of freedom
Denavit–Hartenberg parameters
forward kinematics
Genetic algorithms
Kinematics
Manipulators
Markov decision process
Markov processes
Methods
neural network
Neural networks
Obstacle avoidance
Redundancy
redundant degrees
Robot arms
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Summary:Redundant degree-of-freedom (DOF) manipulators offer increased flexibility and are better suited for obstacle avoidance, yet precise control of these systems remains a significant challenge. This paper addresses the issues of slow training convergence and suboptimal stability that plague current deep reinforcement learning (DRL)-based control strategies for redundant DOF manipulators. We propose a novel DRL-based intelligent control strategy, FK-DRL, which integrates the manipulator’s forward kinematics (FK) model into the control framework. Initially, we conceptualize the control task as a Markov decision process (MDP) and construct the FK model for the manipulator. Subsequently, we expound on the integration principles and training procedures for amalgamating the FK model with existing DRL algorithms. Our experimental analysis, applied to 7-DOF and 4-DOF manipulators in simulated and real-world environments, evaluates the FK-DRL strategy’s performance. The results indicate that compared to classical DRL algorithms, the FK-DDPG, FK-TD3, and FK-SAC algorithms improved the success rates of intelligent control tasks for the 7-DOF manipulator by 21%, 87%, and 64%, respectively, and the training convergence speeds increased by 21%, 18%, and 68%, respectively. These outcomes validate the proposed algorithm’s effectiveness and advantages in redundant manipulator control using DRL and FK models.
ISSN:2075-1702
2075-1702
DOI:10.3390/machines12100667