Redundant Robotic Arm Repeat Motion via Neural Inverse Kinematics
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Solution Overview
Problem
Traditional methods for programming repeating motions in redundant robotic arms, such as pseudo-inverse methods and numerical solvers, are computationally intensive and lack real-time performance, leading to inefficiencies in industrial production due to non-repeating motions and errors.
Innovation Solution
A method utilizing a variable parameter convergence differential neural network to solve the inverse kinematics problem by converting it into a time-varying convex quadratic programming problem, introducing a repeating motion indicator, and solving it through a Lagrangian function-based matrix equation, resulting in faster convergence and higher precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional pseudo-inverse method is used to solve inverse kinematics, then the robotic arm can complete tasks, but the calculation is computationally intensive and lacks real-time performance
Solution Approach 1:
The patent replaces traditional numerical iterative methods with a neural network-based computational system. The neural network is trained offline to learn the inverse kinematics mapping, and during runtime, it directly computes joint angles from end-effector positions without iterative calculation, substituting heavy mechanical computation with a trained intelligent system that provides real-time performance.
Solution Approach 2:
The patent performs preliminary training of the neural network offline before actual robotic operations. During this preliminary phase, the network learns the complex inverse kinematics relationships from training data. Once trained, the network can rapidly compute solutions during runtime without requiring heavy computation, thus achieving real-time performance while maintaining accuracy.
2Reliability
If traditional numerical method solver is used, then the inverse kinematics problem can be solved, but the computation is intensive and efficiency is low
Solution Approach 1:
The patent substitutes traditional numerical solvers (which require iterative computation) with a pre-trained neural network that directly maps end-effector positions to joint angles. This substitution eliminates the need for repeated numerical iterations during runtime, dramatically improving calculation efficiency while preserving solution accuracy through the network's learned mapping.
Solution Approach 2:
The patent changes the computational parameters from iterative numerical methods to a direct neural network inference process. By transforming the problem from solving differential equations numerically to evaluating a trained neural network function, the computation shifts from intensive iterative calculation to efficient forward propagation, achieving both accuracy and speed.
3Adaptability or versatility
If the robotic arm performs non-repeating motion, then flexibility is achieved, but errors accumulate and additional reset operations are required, reducing production efficiency
Solution Approach 1:
The patent implements a feedback mechanism where the neural network continuously receives real-time end-effector position data and computes the corresponding joint angles to maintain the desired trajectory. This closed-loop control with neural network-based inverse kinematics ensures that the robotic arm returns to the initial position after each cycle, enabling repeating motions without error accumulation while maintaining motion flexibility.
Data Source
AI summary
A method is presented for programming a repeating motion of a redundant robotic arm on the basis of a variable parameter convergence differential neural network. The method may include establishing an inverse kinematics equation, creating an inverse kinematics problem, introducing a repeating motion indicator, converting a time-varying convex quadratic programming problem into a time-varying matrix equation, and integrating an optimal solution to obtain an optimal solution of a joint angle. The use of the variable parameter convergence differential neural network to solve the repeating redundant mechanical motion has the advantages of high computational efficiency, high real-time performance, and enhanced robot arm robustness.