Communication Dans Un Congrès Année : 2024

SCvxPyGen: Autocoding SCvx Algorithm

SCvxPyGen: Autocoding SCvx Algorithm

Résumé

In this paper, we address the embedded code generation for an optimal control algorithm, SCvx, which is particularly suitable for solving trajectory planning problems with collision avoidance constraints. Producing code compatible with embedded systems constraints will support the use of the SCvx algorithm in a real-time configuration. Existing uses of SCvx on drones or embedded platforms are currently handcrafted code. On the other hand, recent toolboxes such as SCPToolbox provide a simpler access to these trajectory planning algorithms, based on the resolution of a sequence of convex sub-problems. We define here a framework, in Python, enabling the automatic code generation for SCvx, in C, based on cVxpygen and the ecos solver. The framework is able to address problems involving non-convex constraints such as obstacle avoidance. This is a first step towards a more streamlined process to auto-code trajectory planning algorithms and convex optimization solvers.

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Dates et versions

hal-04992546 , version 1 (15-03-2025)

Identifiants

Citer

Danil Berrah, Alexandre Chapoutot, Pierre-Loïc Garoche. SCvxPyGen: Autocoding SCvx Algorithm. 2024 IEEE 63rd Conference on Decision and Control (CDC), Dec 2024, Milan, Italy. pp.5086-5093, ⟨10.1109/CDC56724.2024.10886875⟩. ⟨hal-04992546⟩
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