This paper presents a novel model predictive control strategy for controlling autonomous motion systems moving through an environment with obstacles of general shape. In order to solve such a generic non-convex optimization problem and find a feasible trajectory that reaches the destination, the approach employs a quadratic penalty method to enforce the obstacle avoidance constraints, and several heuristics to bypass local minima behind an obstacle. The quadratic penalty method itself aids in avoiding such local minima by gradually finding a path around the obstacle as the penalty factors are successively increased. The inner optimization problems are solved in real time using the proximal averaged Newton-type method for optimal control (PANOC), a first-order method which exhibits low runtime and is suited for embedded applications. The method is validated by extensive numerical simulations and shown to outperform state-of-the-art solvers in runtime and robustness. ⋆ This work benefits from KU Leuven-BOF PFV/10/002 Centre of Excellence: Optimization in Engineering (OPTEC), from the project G0C4515N of the Research Foundation-Flanders (FWO-Flanders), from Flanders Make ICON project: Avoidance of collisions and obstacles in narrow lanes, and from the KU Leuven Research project C14/15/067: B-spline based certificates of positivity with applications in engineering, from KU Leuven internal funding: StG/15/043, from Fonds de la Recherche Scientifique -FNRS and the Fonds Wetenschappelijk Onderzoek -Vlaanderen under EOS Project no 30468160 (SeLMA) and from FWO projects G086318N, G086518N.
We present a proximal augmented Lagrangian based solver for general convex quadratic programs (QPs), relying on semismooth Newton iterations with exact line search to solve the inner subproblems. The exact line search reduces in this case to finding the zero of a one-dimensional monotone, piecewise affine function and can be carried out very efficiently. Our algorithm requires the solution of a linear system at every iteration, but as the matrix to be factorized depends on the active constraints, efficient sparse factorization updates can be employed like in active-set methods. Both primal and dual residuals can be enforced down to strict tolerances and otherwise infeasibility can be detected from intermediate iterates.A C implementation of the proposed algorithm is tested and benchmarked against other state-of-the-art QP solvers for a large variety of problem data and shown to compare favorably against these solvers.
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