A Trajectory Tracking Control Algorithm of Nonholonomic Wheeled Mobile Robot

Rui, Deng; Zhang, Qingfang; Gao, Rui; Li, Mingkang; Liang, Ping; Gao, Xueshan

doi:10.1109/icarm52023.2021.9536154

Cited by 5 publications

(1 citation statement)

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“…Trajectory generation methods which are rooted in PDE have the advantage that they are easy to implement, entirely interpretable, and can provide theoretical guarantees regarding optimality, robustness, and other concerns. This is to distinguish them from sampling and learning based algorithms (for example [15,16,17,18,19]) which often sacrifice interpretability for efficiency. The main drawbacks of the PDEbased methods are the lack of efficiency and scalability.…”

Section: Introductionmentioning

confidence: 99%

Time-Optimal Paths for Simple Cars with Moving Obstacles in the Hamilton-Jacobi Formulation

Parkinson

Ceccia

2022

2022 American Control Conference (ACC)

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We present a partial-differential-equation-based optimal path-planning framework for curvature constrained motion, with application to vehicles in 2-and 3-spatialdimensions. This formulation relies on optimal control theory, dynamic programming, and a Hamilton-Jacobi-Bellman equation. Many authors have developed similar models and work employed grid-based numerical methods to solve the partial differential equation required to generate optimal trajectories. However, these methods can be inefficient and do not scale well to high dimensions. We describe how efficient and scalable algorithms for solutions of high dimensional Hamilton-Jacobi equations can be developed to solve similar problems very efficiently, even in high dimensions, while maintaining the Hamilton-Jacobi formulation. We demonstrate our method with several examples.

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