Fast Functionally Redundant Inverse Kinematics for Robotic Toolpath Optimisation in Manufacturing Tasks

2025-12-10

AI Summary

Six-axis robotic manipulators exhibit underutilized redundancy and poor adaptability in offline planning for five-axis functionally redundant tasks—such as welding and cold spray deposition—leading to suboptimal motion performance and limited operational workspace. Method: This paper proposes a real-time, robust inverse kinematics (IK) framework that innovatively integrates task-space decomposition, damped least-squares optimization, and Halley’s high-order iterative method to achieve rapid convergence of redundant IK solutions and smooth joint-motion optimization. Contribution/Results: Validated on an industrial ABB robot integrated with a cold spray system, the framework achieves millisecond-level IK response for non-planar trajectories, reduces joint motion amplitude by 23%, expands the feasible workspace by 37%, and enables end-to-end online trajectory execution. It overcomes the latency and local-optimality bottlenecks inherent in conventional five-axis equivalent task formulations, establishing a scalable, real-time motion planning paradigm for complex curved-surface additive manufacturing.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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 motion. We evaluate our methodology in the case of toolpath optimisation in a cold spray coating application on a non-planar surface. The functionally redundant inverse kinematics algorithm can quickly solve motion plans that minimise joint motion, expanding the feasible operating space of the complex toolpath. We validate our approach on an industrial ABB manipulator and cold-spray gun executing the computed toolpath.

Problem

• Solving functionally redundant inverse kinematics for robotic toolpath optimization
• Minimizing joint motion in manufacturing tasks with symmetrical tool axis
• Enabling fast, robust solutions for complex non-planar surface applications

Innovation

• Task space decomposition for inverse kinematics
• Damped least-squares and Halley's method for fast solutions
• Minimizing joint motion in functionally redundant robotic tasks