Trajectory scaling techniques are able to adapt online the robot motion to preserve the desired geometric path when the desired motion does not satisfy the robot limits. State-of-the-art local methods typically provide far-from-optimal solutions, while high computational burdens are the main bottleneck for the implementation of model predictive schemes. This paper proposes a predictive approach to trajectory scaling subject to kinematics and dynamics limitations. Computational complexity is reduced by linearizing nonlinear constraints around the previous output prediction. This allows the online implementation of the method even for small sampling periods. Numerical and experimental results show the effectiveness of the method.
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