Planning Smooth and Obstacle-Avoiding B-Spline Paths for Autonomous Mining Vehicles
By T. Berglund, A. Brodnik, Håkan Jonsson, Mats Staffanson, I. Söderkvist. 2010
214 citations recorded by the source corpus.
Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Control and Systems Engineering
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In Computer Vision and Pattern Recognition
In Control and Systems Engineering
Citations in the corpus
- A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning (2017, neutral): “Emails: { litingsun, chengpeng2014, wzhan, tomizuka} @berkeley.edu. i) graph-search-based, such as A ∗ (see [4]), ii) sampling-based (e.g., RRT, RRT*, see [5], [6]), iii) interpolating-curve-based [7], [8], and iv) optimization-based [9], [10].”
- An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve (2021, unknown): “To track these paths, a robot has to frequently switch their motion states among ‘stop’, ‘rotation’ and ‘restart’ [12].”
- A Review of Motion Planning Techniques for Automated Vehicles (2016, background): “Spline Curves: A spline is a piecewise polynomial parametric curve divided in sub-intervals that can be defined as polynomial curves [28], [76], b-splines [88], [89] (that can also”