A Novel 3D Ring-Based Flapper Valve for Soft Robotic Applications

In this paper, the design and testing of a novel valve for the intuitive spatial control of soft or continuum manipulators are presented. The design of the valve is based on the style of a hydraulic flapper valve, but with simultaneous control of three pressure feed points, which can be used to drive three antagonistically arranged hydraulic actuators for positioning soft robots. The variable control orifices are arranged in a rotationally symmetric radial pattern to allow for an inline mounting configuration of the valve within the body of a manipulator. Positioning the valve ring at various 3D configurations results in different pressurizations of the actuators and corresponding spatial configurations of the manipulator. The design of the valve is suitable for miniaturization and use in applications with size constraints such as small soft manipulators and surgical robotics. Experimental validation showed that the performance of the valve can be reasonably modeled and can effectively drive an antagonistic arrangement of three actuators for soft manipulator control.

A Manipulator Control Method Based on Deep Deterministic Policy Gradient with Parameter Noise

Focusing on the motion control problem of two link manipulator, a manipulator control approach based on deep deterministic policy gradient with parameter noise is proposed. Firstly, the manipulator simulation environment is built. And then the three deep reinforcement learning models named the deep deterministic policy gradient (DDPG), asynchronous advantage actor-critical (A3C) and distributed proximal policy optimization (DPPO) are established for training according to the target setting, state variables and reward & punishment mechanism of the environment model. Finally the motion control of two link manipulator is realized. After comparing and analyzing the three models, the DDPG approach based on parameter noise is proposed for further research to improve its applicability, so as to cut down the debugging time of the manipulator model and reach the goal smoothly. The experimental results indicate that the DDPG approach based on parameter noise can control the motion of two link manipulator effectively. The convergence speed of the control model is significantly promoted and the stability after convergence is improved. In comparison with the traditional control approach, the DDPG control approach based on parameter noise has higher efficiency and stronger applicability.

Design of Manipulator Control System of Forest Picking Robot Based on Fractional Order PID Sliding Mode Control

This paper studies the control system design of forest picking robot manipulator based on fractional PID sliding mode control. In this paper, the structure characteristics, learning algorithm and application of fractional order PID sliding mode control in manipulator control are analyzed. In this paper, the nonlinear approximation property of fractional order PID sliding mode control is theoretically verified. This paper analyzes the basic structure of picking manipulator system in detail. At the same time, the Lagrange Euler method is used to deduce the dynamic equation of the two degree of freedom series manipulator, and the inertia characteristics, Coriolis force and centripetal force characteristics, heavy torque characteristics are analyzed. The nonlinear system model of manipulator based on S-function is established in MATLAB, and the dynamic model is transformed into the form of second-order differential equation to facilitate the introduction of the designed algorithm.

Research on manipulator control based on deep reinforcement learning

With the gradual improvement of the influence of intelligent robots in production and life, it has greatly facilitated people's production and life. Therefore, people's requirements for intelligent robots are also increasing, and are developing towards more humanization and intelligence. However, at present, there are still many imperfections in the field of intelligent robot technology in China. In order to solve the problems in work, we must further strengthen the research on artificial intelligence theory and robot technology. Only in this way can we realize the all-round development of intelligent robot system. So this paper will discuss the deep reinforcement learning in the theory of artificial intelligence, and explain its basic theory, research status, existing problems and future development direction. Moreover, under the background of the overall improvement of the current industrial development level, this paper will also talk about the manipulator widely used in the industrial field and the research status of manipulator control based on deep reinforcement learning, hoping to provide effective help for the development of related fields.

Manipulator Kinematics and Dynamics On Differentiable Manifolds: Part II Dynamics

The manipulator differentiable manifold formulation presented in Part I of this paper is used to create algorithms for forward and inverse kinematics on maximal, singularity free, path connected manifold components. Existence of forward and inverse configuration mappings in manifold components is extended to obtain forward and inverse velocity mappings. Computational algorithms for forward and inverse configuration and velocity analysis on a time grid are derived for each of the four categories of manipulator treated. Manifold parameterizations derived in Part I are used to transform variational equations of motion in Cartesian generalized coordinates to second order ordinary differential equations of manipulator dynamics, in terms of both input and output coordinates, avoiding ad-hoc derivation of equations of motion. This process is illustrated in evaluating terms required for equations of motion of the four model manipulators analyzed in Part I. It is shown that computation involved in forward and inverse kinematics and in evaluation of equations of manipulator dynamics can be carried out in real-time on modern microprocessors, supporting on-line implementation of modern methods of manipulator control.

Adaptive Fractional-Order Nonsingular Fast Terminal Sliding Mode Control for Manipulators

When the manipulator system is subject to unknown disturbance, in order to improve the tracking accuracy of the manipulator, this paper designs a fractional-order nonsingular fast terminal sliding mode (FONFTSM) controller. The controller is divided into three parts. First of all, in order to improve the performance of the sliding stage, this paper designs a FONFTSM surface. By introducing a fractional-order operator, the convergence speed and accuracy of the system state are effectively improved. Secondly, in view of the problems of large chattering and slow convergence speed in the reaching stage, this paper designs a variable exponential power-reaching law (VEPRL), which has the ability to change the exponential coefficients according to the system state adaptively. At the same time, an adaptive law is designed to adjust the coefficients of the reaching law adaptively, which enhances the robustness of the control system. Finally, a disturbance observer is used to estimate the unknown external disturbance in real time so as to perform feedforward compensation for the control system, which effectively improves the accuracy of the manipulator control system. The stability of the manipulator control system is proved by the Lyapunov function. Simultaneously, the controller designed in this paper is compared with different controllers, which proves that the controller designed in this paper has strong robustness, high control accuracy, and fast convergence speed.