Redundant Robot Joint Acceleration Planning via Quadratic Optimization

The patent transforms the control planning problem from a complex logical classification task into a quadratic optimization problem by changing the mathematical parameters and formulation approach. Instead of using traditional inverse kinematics with complex branching logic for redundant joints, the patent formulates the problem as minimizing a quadratic cost function subject to constraints, which can be solved systematically using quadratic programming algorithms. This parameter transformation resolves the contradiction by maintaining redundant joints capability while eliminating the complexity of logic processing and classification calculations.

The patent replaces the mechanical-like complex logical processing system with a mathematical optimization system. Instead of using traditional control planning methods that rely on intricate logic trees and classification calculations, the patent substitutes this with a quadratic programming framework that systematically handles task coordination through mathematical optimization. This substitution dramatically improves control planning efficiency while maintaining the ability to coordinate multiple degrees of freedom for various tasks.

The patent incorporates feedback mechanisms through the quadratic optimization framework, where the cost function continuously evaluates the current joint configuration and provides feedback for adjustment. The optimization algorithm iteratively refines the solution by comparing the current state against the desired task requirements and constraints, ensuring accurate control planning results. This feedback-driven approach maintains joint configuration flexibility while systematically improving the accuracy of control planning by eliminating the uncertainty associated with complex logical calculations.