Today’s standard robotic systems often do not meet the industry’s demands for accurate high-speed robotic applications. Any machine, be it an existing or a new one, should be pushed to its limits to provide “optimal” efficiency. However, due to the high complexity of modern applications, a one-step overall optimization is not possible. Therefore, this contribution introduces a step-by-step sequence of multiple nonlinear optimizations. Included are optimal configurations for geometric calibration, best-exciting trajectories for parameter identification, model-based control, and time/energy optimal trajectory planning for continuous path and point-to-point trajectories. Each of these optimizations contributes to the improvement of the overall system. Existing optimization techniques are adapted and extended for use with a standard industrial robot scenario and combined with a comprehensive toolkit with discussions on the interplay between the separate components. Most importantly, all procedures are evaluated in practical experiments on a standard robot with industrial control hardware and the recorded measurements are presented, a step often missing in publications in this area. 1. Introduction State-of-the-art robotic systems are equipped with highly sophisticated industrial hardware, capable of short sample times and offering high computational power. This increasing arithmetic performance may be used to improve positioning accuracy and dynamic accuracy and compute time/energy-optimal trajectories. The proper fundamentals are found in the underlying mathematical models. This contribution focuses on giving an overview of various nonlinear optimizations. With each optimization, a certain aspect of an arbitrary robotic system is improved. Applying all of them in sequence will provide better results regarding positioning accuracy and dynamic accuracy and will reduce the cycle times. The basis for this optimization lies in the kinematic and dynamical modeling of the system. Therefore, Section 2 starts with the deviation of these models enhanced by a model-based control strategy. Special emphasis is laid on the implementation on an industrial system including time delays. In Section 3, the static position accuracy of the robot is improved by considering unavoidable tolerances in the robot kinematics. The main topic is obtaining optimal configurations for the identification process. The identification of the dynamic parameters, that are used for the model-based control, is described in Section 4. Also, the problem of optimal exciting trajectories is solved by
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