We present a nonlinear model predictive control algorithm for online motion planning and tracking of an omnidirectional autonomous robot. The formalism is based on the minimization of a control Hamiltonian related to the cost function. This minimization is constrained by a nonlinear plant model. The algorithm considers point obstacles and uses a potential field to penalize proximity to those obstacles. A simple and demonstrative example simulation is presented.
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Teatro et al. (2013) studied this question.
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