This article presents a novel framework called Simultaneous Registration and Machining (SRAM), a generalized method to improve workpiece registration using real-time acquired data in robotic contouring applications. The method allows for online corrections to the toolpath, while a live covariance estimate is simultaneously leveraged to adaptively tune the force controller aggressively when uncertainty is high, but conservatively otherwise to minimize chatter and instability. The SRAM framework is validated in simulation and shown to significantly reduce the path corrections required from the force controller, while correctly predicting optimal controller tuning adaptations. The SRAM method is proposed to improve force control stability, increase peripheral accuracy, smooth surface finish, and reduce cycle times in contouring applications.

This paper proposes a novel numerical inverse kinematics algorithm called theQuick Inverse KinematicsorQuIKmethod. The QuIK method is athird-orderalgorithm that uses both the first- and second-order derivative information to iteratively converge to a solution. Numerical inverse kinematics methods are readily implemented on any serial robot and do not rely on joint alignment. However, they typically are slower and less robust. The second-order derivative term allows the QuIK algorithm to converge more rapidly and more robustly than existing algorithms. A damped extension to the QuIK method is also proposed to increase reliability near singularities. The QuIK methods are tested in terms of evaluation speed, reliability, and singularity robustness against the Newton–Raphson method and several other modern algorithms. The proposed QuIK methods outperform all other tested algorithms in terms of speed and robustness, and have strong performance near singularities. The QuIK algorithms are proposed as faster and more robust “drop-in” replacements to the Newton–Raphson methods in inverse kinematics. C++ and MATLAB codebases are made available.