A Neural Network Based Approach to Inverse Kinematics Problem for General Six-Axis Robots

Abstract

Inverse kinematics problems (IKP) are ubiquitous in robotics for improved robot control in widespread applications. However, the high non-linearity, complexity, and equation coupling of a general six-axis robotic manipulator pose substantial challenges in solving the IKP precisely and efficiently. To address this issue, we propose a novel approach based on neural network (NN) with numerical error minimization in this paper. Within our framework, the complexity of IKP is first simplified by a strategy called joint space segmentation, with respective training data generated by forward kinematics. Afterwards, a set of multilayer perception networks (MLP) are established to learn from the foregoing data in order to fit the goal function piecewise. To reduce the computational cost of the inference process, a set of classification models is trained to determine the appropriate forgoing MLPs for predictions given a specific input. After the initial solution is sought, being improved with a prediction error minimized, the refined solution is finally achieved. The proposed methodology is validated via simulations on Xarm6—a general 6 degrees of freedom manipulator. Results further verify the feasibility of NN for IKP in general cases, even with a high-precision requirement. The proposed algorithm has showcased enhanced efficiency and accuracy compared to NN-based approaches reported in the literature.

A Performance Analysis of a Litchi Picking Robot System for Actively Removing Obstructions, Using an Artificial Intelligence Algorithm

“…When the distance was greater than 100 mm, which was the width val of the end-effector, the end-effector would enter the space between the picking point a the obstruction and determine its own picking posture with the picking point at t endpoint, as shown in Figure 10. 5) [29] as:…”

“…The forward solution equation of the robotic arm was established as shown Equation ( 6) [29]: The Denavit-Hartenberg (D-H) parameters of the robot are shown in Table 1. The transformation matrices 0 1 T~5 6 T of each axis were constructed based on the D-H parameters, where i−1 i T is shown in Equation ( 5) [29] as: The forward solution equation of the robotic arm was established as shown in Equation ( 6) [29]:…”

Position Analysis of the Slider-Crank (R-RRT) Mechanism Using Artificial Neural Networks

“…al. [2]. In this study, an inverse kinematic problem solution approach for general six-axis robots is presented based on multilayer perception artificial neural networks.…”

An Improved Performance of the Designed Robotic Motion Control for NAO Robot Arms Using Hybrid Neural Network-Jacobian

“…The neural network is such a data-driven modeling technique that it is flexible for modeling the inverse kinematics. Because of its flexibility and learning ability, the neural network can handle the problems of the inverse kinematics, starting from the simple robots [37,38] to the robots with complex structures [39][40][41]. The inverse kinematics solution resulted from the neural network is expressed in the neural network architecture that defines the mapping from the cartesian space to the joint space.…”

“…In addition, the problem such as singularity and multiplicity does not exist in the neural network. That is why, in the design of robotic motion control, many researchers [37][38][39][40][41] preferred to use the neural network. It is important to note, the neural networkbased inverse kinematics structure is feedforward so it is classified as the open-loop control system.…”