In this article, we discuss a sequential algorithm for the computation of a minimum-time speed profile over a given path, under velocity, acceleration, and jerk constraints. Such a problem arises in industrial contexts, such as automated warehouses, where LGVs need to perform assigned tasks as fast as possible in order to increase productivity. It can be reformulated as an optimization problem with a convex objective function, linear velocity and acceleration constraints, and nonconvex jerk constraints, which, thus, represent the main source of the difficulty. While existing nonlinear programming (NLP) solvers can be employed for the solution of this problem, it turns out that the performance and robustness of such solvers can be enhanced by the sequential line-search algorithm proposed in this article. At each iteration, a feasible direction, with respect to the current feasible solution, is computed, and a step along such direction is taken in order to compute the next iterate. The computation of the feasible direction is based on the solution of a linearized version of the problem, and the solution of the linearized problem, through an approach that strongly exploits its special structure, represents the main contribution of this work. The efficiency of the proposed approach with respect to existing NLP solvers is proven through different computational experiments.

A Sequential Algorithm for Jerk Limited Speed Planning / Consolini, L.; Locatelli, M.; Minari, A.. - In: IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING. - ISSN 1545-5955. - (2021), pp. 1-18. [10.1109/TASE.2021.3111758]

A Sequential Algorithm for Jerk Limited Speed Planning

Consolini L.;Locatelli M.;Minari A.
2021

Abstract

In this article, we discuss a sequential algorithm for the computation of a minimum-time speed profile over a given path, under velocity, acceleration, and jerk constraints. Such a problem arises in industrial contexts, such as automated warehouses, where LGVs need to perform assigned tasks as fast as possible in order to increase productivity. It can be reformulated as an optimization problem with a convex objective function, linear velocity and acceleration constraints, and nonconvex jerk constraints, which, thus, represent the main source of the difficulty. While existing nonlinear programming (NLP) solvers can be employed for the solution of this problem, it turns out that the performance and robustness of such solvers can be enhanced by the sequential line-search algorithm proposed in this article. At each iteration, a feasible direction, with respect to the current feasible solution, is computed, and a step along such direction is taken in order to compute the next iterate. The computation of the feasible direction is based on the solution of a linearized version of the problem, and the solution of the linearized problem, through an approach that strongly exploits its special structure, represents the main contribution of this work. The efficiency of the proposed approach with respect to existing NLP solvers is proven through different computational experiments.
A Sequential Algorithm for Jerk Limited Speed Planning / Consolini, L.; Locatelli, M.; Minari, A.. - In: IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING. - ISSN 1545-5955. - (2021), pp. 1-18. [10.1109/TASE.2021.3111758]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2905629
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