A penalty method for nonlinear programs with set exclusion constraints

A common requirement in optimal control problems arising autonomous navigation is that the decision variables are constrained to be outside certain sets. Such set exclusion constraints represent obstacles must avoided by motion system. This paper presents a simple and efficient method for solving optimization with general implicit constraints. The embeds quadratic penalty framework solves inner using proximal algorithm deals directly We derive convergence results this transforming generated iterates points of reformulated problem complementarity Furthermore, practical application solution validated numerical simulations model predictive approach path planning mobile robot. Finally, runtime comparison state-of-the-art solvers applied illustrates efficiency proposed method.