Is time-optimal speed planning under jerk constraints a convex problem?

We consider the speed planning problem for a vehicle moving along an assigned trajectory, under maximum speed, tangential and lateral acceleration, and jerk constraints. The problem is a nonconvex one, where nonconvexity is due to jerk constraints. We propose a convex relaxation, and we present various theoretical properties. In particular, we show that the relaxation is exact under some assumptions. Also, we rewrite the relaxation as a Second Order Cone Programming (SOCP) problem. This has a relevant practical impact, since solvers for SOCP problems are quite efficient and allow solving large instances within tenths of a second. We performed many numerical tests, and in all of them the relaxation turned out to be exact. For this reason, we conjecture that the convex relaxation is always exact, although we could not give a formal proof of this fact.

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Publication Details

Journal
Automatica
Published
2024-08-16
DOI
https://doi.org/10.1016/j.automatica.2024.111864
Citations
2
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.50
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article

Is time-optimal speed planning under jerk constraints a convex problem?

Marco Locatelli, Luca Consolini
2 citations
Automatica
Robotic Path Planning Algorithms
0.50
article

Is time-optimal speed planning under jerk constraints a convex problem?

Marco Locatelli, Luca Consolini
article en
2 citations

Abstract

We consider the speed planning problem for a vehicle moving along an assigned trajectory, under maximum speed, tangential and lateral acceleration, and jerk constraints. The problem is a nonconvex one, where nonconvexity is due to jerk constraints. We propose a convex relaxation, and we present various theoretical properties. In particular, we show that the relaxation is exact under some assumptions. Also, we rewrite the relaxation as a Second Order Cone Programming (SOCP) problem. This has a relevant practical impact, since solvers for SOCP problems are quite efficient and allow solving large instances within tenths of a second. We performed many numerical tests, and in all of them the relaxation turned out to be exact. For this reason, we conjecture that the convex relaxation is always exact, although we could not give a formal proof of this fact.

AutomaticaVol. 169
University of Parma (IT)
Openalex Percentile: Top 36%
Robotic Path Planning Algorithms
0.50
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