Dynamically feasible data-driven trajectory generation for high performance control of robot manipulators

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Robot manipulators are significantly more accurate than their human counterparts and enhance the repeatability of various tasks. However, manufactures still provide reduced information regarding the robot controller functions, typically affecting robots' predictability. Additionally, industrial examples show that system transparency could be improved, helping users to understand the process and apply corrective actions. One step in the direction of improved predictability is the identification of the Limiter block associated with the KUKA robot controllers that limit the acceleration and jerk obtained during motions. This work aims to provide dynamically feasible trajectories with reduced performance deterioration. Specifically, the Limiter's behaviour was analyzed and later predicted in a iterative manner, aiming for the maximum velocity, acceleration and jerk that can be achieved, starting from the current robot state and given specific robot information. Using the predicted bounds, the trajectory generator will determine the maximum achievable point that fulfills all the constraints, running an optimization problem in an iterative fashion. Nonetheless, the practical experiments represent a significant component allowing data collection and results validation.