Convex optimization for trajectory generation

  • How do you optimize trajectory?

    An indirect method for solving a trajectory optimization problem proceeds in three steps: .

    1. Analytically construct the necessary and sufficient conditions for optimality,
    2. Discretize these conditions, constructing a constrained parameter optimization problem,
    3. Solve that optimization problem

Jun 16, 2021The goal of this article is to provide a comprehensive tutorial of three major convex optimization-based trajectory generation methods: lossless 

Are You Ready to use lossless convexification and sequential convex programming algorithms?

By the end of the article, the reader will not only be ready to use the lossless convexification and sequential convex programming algorithms, but also to extend them and to contribute to their many exciting modern applications

Reliable and efficient trajectory generation methods are a fundamental need for autonomous dynamical systems

What are the three major convex optimization-based trajectory generation methods?

The goal of this article is to provide a comprehensive tutorial of three major convex optimization-based trajectory generation methods: lossless convexification (LCvx), and two sequential convex programming algorithms known as SCvx and GuSTO

What is convex optimization?

Convex optimization seeks to minimize a convex objec- tive function while satisfying a set of convex constraints

The technique is expressive enough to capture many tra- jectory generation and control applications, and is ap- pealing due to the availability of solution algorithms with the following properties [26,29]:


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