N-Dimensional Reduction Algorithm for Learning from Demonstration Path Planning

N-Dimensional Reduction Algorithm for Learning from Demonstration Path Planning

This paper presents an <i>n</i>-dimensional reduction algorithm for Learning from Demonstration (LfD) for robotic path planning, addressing the complexity of high-dimensional data. The method extends the Douglas–Peucker algorithm by incorporating velocity and orientation alongside positi...

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Bibliographic Details
Main Authors: Juliana Manrique-Cordoba, Miguel Ángel de la Casa-Lillo, José María Sabater-Navarro
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Sensors
Subjects:
learning from demonstration
hidden Markov models
data reduction
Douglas–Peucker algorithm
high-dimensional data encoding
Online Access:https://www.mdpi.com/1424-8220/25/7/2145
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