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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/7/2145 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850212752247750656 |
|---|---|
| author | Juliana Manrique-Cordoba Miguel Ángel de la Casa-Lillo José María Sabater-Navarro |
| author_facet | Juliana Manrique-Cordoba Miguel Ángel de la Casa-Lillo José María Sabater-Navarro |
| author_sort | Juliana Manrique-Cordoba |
| collection | DOAJ |
| description | 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 position, enabling more precise trajectory simplification. A magnitude-based normalization process preserves proportional relationships across dimensions, and the reduced dataset is used to train Hidden Markov Models (HMMs), where continuous trajectories are discretized into identifier sequences. The algorithm is evaluated in 2D and 3D environments with datasets combining position and velocity. The results show that incorporating additional dimensions significantly enhances trajectory simplification while preserving key information. Additionally, the study highlights the importance of selecting appropriate encoding parameters to achieve optimal resolution. The HMM-based models generated new trajectories that retained the patterns of the original demonstrations, demonstrating the algorithm’s capacity to generalize learned behaviors for trajectory learning in high-dimensional spaces. |
| format | Article |
| id | doaj-art-c74bafd0cfde4be8abeb1a66eb6f0cb6 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-c74bafd0cfde4be8abeb1a66eb6f0cb62025-08-20T02:09:17ZengMDPI AGSensors1424-82202025-03-01257214510.3390/s25072145N-Dimensional Reduction Algorithm for Learning from Demonstration Path PlanningJuliana Manrique-Cordoba0Miguel Ángel de la Casa-Lillo1José María Sabater-Navarro2Bioengineering Institute, Miguel Hernandez University of Elche, 03202 Elche, SpainBioengineering Institute, Miguel Hernandez University of Elche, 03202 Elche, SpainBioengineering Institute, Miguel Hernandez University of Elche, 03202 Elche, SpainThis 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 position, enabling more precise trajectory simplification. A magnitude-based normalization process preserves proportional relationships across dimensions, and the reduced dataset is used to train Hidden Markov Models (HMMs), where continuous trajectories are discretized into identifier sequences. The algorithm is evaluated in 2D and 3D environments with datasets combining position and velocity. The results show that incorporating additional dimensions significantly enhances trajectory simplification while preserving key information. Additionally, the study highlights the importance of selecting appropriate encoding parameters to achieve optimal resolution. The HMM-based models generated new trajectories that retained the patterns of the original demonstrations, demonstrating the algorithm’s capacity to generalize learned behaviors for trajectory learning in high-dimensional spaces.https://www.mdpi.com/1424-8220/25/7/2145learning from demonstrationhidden Markov modelsdata reductionDouglas–Peucker algorithmhigh-dimensional data encoding |
| spellingShingle | Juliana Manrique-Cordoba Miguel Ángel de la Casa-Lillo José María Sabater-Navarro N-Dimensional Reduction Algorithm for Learning from Demonstration Path Planning Sensors learning from demonstration hidden Markov models data reduction Douglas–Peucker algorithm high-dimensional data encoding |
| title | N-Dimensional Reduction Algorithm for Learning from Demonstration Path Planning |
| title_full | N-Dimensional Reduction Algorithm for Learning from Demonstration Path Planning |
| title_fullStr | N-Dimensional Reduction Algorithm for Learning from Demonstration Path Planning |
| title_full_unstemmed | N-Dimensional Reduction Algorithm for Learning from Demonstration Path Planning |
| title_short | N-Dimensional Reduction Algorithm for Learning from Demonstration Path Planning |
| title_sort | n dimensional reduction algorithm for learning from demonstration path planning |
| topic | learning from demonstration hidden Markov models data reduction Douglas–Peucker algorithm high-dimensional data encoding |
| url | https://www.mdpi.com/1424-8220/25/7/2145 |
| work_keys_str_mv | AT julianamanriquecordoba ndimensionalreductionalgorithmforlearningfromdemonstrationpathplanning AT miguelangeldelacasalillo ndimensionalreductionalgorithmforlearningfromdemonstrationpathplanning AT josemariasabaternavarro ndimensionalreductionalgorithmforlearningfromdemonstrationpathplanning |