Machine Learning-based system to predict the performance of exploration algorithms for mobile robots
The thesis presented here proposes a Machine Learning based method with optimized neural networks that evaluates exploration algorithms for mobile robots based on the prediction of a performance metric. Two applications around the prediction of the performance of exploration algorithms are implement...
- Autores:
-
Caballero Tovar, Liesle Yail
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2021
- Institución:
- Universidad del Norte
- Repositorio:
- Repositorio Uninorte
- Idioma:
- eng
- OAI Identifier:
- oai:manglar.uninorte.edu.co:10584/13310
- Acceso en línea:
- http://hdl.handle.net/10584/13310
- Palabra clave:
- Aprendizaje de máquinas
Robots móviles
Algoritmos
- Rights
- openAccess
- License
- https://creativecommons.org/licenses/by/4.0/
| Summary: | The thesis presented here proposes a Machine Learning based method with optimized neural networks that evaluates exploration algorithms for mobile robots based on the prediction of a performance metric. Two applications around the prediction of the performance of exploration algorithms are implemented here. The first consists of a system that compares and selects the most appropriate exploration algorithm according to the experimental environment, without implementing or simulating additional experiments when other different experimental conditions are required to evaluate. The second application deals with the problem of determining a useful energy budget for a mobile robot in a given environment without having to carry out experimental measures for every possible exploration task. The method of prediction using Machine Learning with optimization strategies Hill Climbing-Random Restart or Tabu List shows an improvement in predictor performance. With these optimization strategies the percentage relative absolute error for Random Walk, Random Walk Without Step Back and Q_Learning exploration algorithms, it was reduced by 92.47%, 90.41% and 87.38% respectively. An experimental energy consumption was measured and compared with the prediction of our model. A success rate of 96.09% was obtained, suggesting an adequate energy predictor, which could be useful for energy budgeting in actual mobile robot applications. Many recent applications of Machine Learning focus on the use of big datasets for training, however, there are many interesting problems, where the available datasets are small. Our method based on Machine Learning techniques has generated excellent results for small databases in general. |
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