Machine Learning and Optimization Techniques for Solving Inverse Kinematics in a 7-DOF Robotic Arm
This work addresses the computational efficiency challenge in robotics for researchers and engineers, though it appears incremental as it builds on existing optimization techniques.
The paper tackled the inverse kinematics problem for a 7-DOF robotic arm by proposing a novel approach that combines machine learning and numerical methods, achieving a speed improvement of over 200 times compared to traditional Particle Swarm Optimization.
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.