Machine Learning and Optimization Techniques for Solving Inverse Kinematics in a 7-DOF Robotic Arm

TL;DR

This paper investigates various optimization techniques for solving inverse kinematics in a 7-DOF robotic arm, introducing a novel method that significantly outperforms traditional algorithms in speed.

Contribution

The paper presents a new hybrid optimization approach that combines machine learning and numerical methods, achieving over 200 times faster solutions than traditional Particle Swarm Optimization.

Findings

The novel method is over 200 times faster than Particle Swarm Optimization.

Thirteen optimization techniques were explored for inverse kinematics.

A hybrid search approach enhances efficiency in solving complex robotic kinematics.

Abstract

As the pace of AI technology continues to accelerate, more tools have become available to researchers to solve longstanding problems, Hybrid approaches available today continue to push the computational limits of efficiency and precision. One of such problems is the inverse kinematics of redundant systems. This paper explores the complexities of a 7 degree of freedom manipulator and explores 13 optimization techniques to solve it. Additionally, a novel approach is proposed to contribute to the field of algorithmic research. This was found to be over 200 times faster than the well-known traditional Particle Swarm Optimization technique. This new method may serve as a new field of search that combines the explorative capabilities of Machine Learning with the exploitative capabilities of numerical methods.