Fast Functionally Redundant Inverse Kinematics for Robotic Toolpath Optimisation in Manufacturing Tasks
Andrew Razjigaev, Hans Lohr, Alejandro Vargas-Uscategui, Peter King, Tirthankar Bandyopadhyay

TL;DR
This paper introduces a fast, robust inverse kinematics algorithm leveraging task space decomposition and advanced mathematical methods to optimize robotic toolpaths, reducing joint motion and expanding operational workspace in manufacturing tasks.
Contribution
A novel inverse kinematics algorithm that efficiently exploits functional redundancy for robotic toolpath optimization using a combination of task space decomposition, damped least-squares, and Halley's method.
Findings
Enables rapid computation of motion plans with minimal joint motion.
Expands the feasible workspace for complex toolpaths.
Validated on industrial robot and cold spray application.
Abstract
Industrial automation with six-axis robotic arms is critical for many manufacturing tasks, including welding and additive manufacturing applications; however, many of these operations are functionally redundant due to the symmetrical tool axis, which effectively makes the operation a five-axis task. Exploiting this redundancy is crucial for achieving the desired workspace and dexterity required for the feasibility and optimisation of toolpath planning. Inverse kinematics algorithms can solve this in a fast, reactive framework, but these techniques are underutilised over the more computationally expensive offline planning methods. We propose a novel algorithm to solve functionally redundant inverse kinematics for robotic manipulation utilising a task space decomposition approach, the damped least-squares method and Halley's method to achieve fast and robust solutions with reduced joint…
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Taxonomy
TopicsRobotic Mechanisms and Dynamics · Soft Robotics and Applications · Advanced Numerical Analysis Techniques
