Predicting the Energy Consumption of a Robot in an Exploration Task Using Optimized Neural Networks
This paper 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 proposed solution uses machine learning models trained on a subset of possible exploration task...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/8/920 |
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| author | Liesle Caballero Álvaro Perafan Martha Rinaldy Winston Percybrooks |
| author_facet | Liesle Caballero Álvaro Perafan Martha Rinaldy Winston Percybrooks |
| author_sort | Liesle Caballero |
| collection | DOAJ |
| description | This paper 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 proposed solution uses machine learning models trained on a subset of possible exploration tasks but able to make predictions on untested scenarios. Additionally, the proposed model does not use any kinematic or dynamic models of the robot, which are not always available. The method is based on a neural network with hyperparameter optimization to improve performance. Tabu List optimization strategy is used to determine the hyperparameter values (number of layers and number of neurons per layer) that minimize the percentage relative absolute error (%RAE) while maximize the Pearson correlation coefficient (R) between predicted data and actual data measured under a number of experimental conditions. Once the optimized artificial neural network is trained, it can be used to predict the performance of an exploration algorithm on arbitrary variations of a grid map scenario. Based on such prediction, it is possible to know the energy needed for the robot to complete the exploration task. A total of 128 tests were carried out using a robot executing two exploration algorithms in a grid map with the objective of locating a target whose location is not known a priori by the robot. The experimental energy consumption was measured and compared with the prediction of our model. A success rate of 96.093% was obtained, measured as the percentage of tests where the energy budget suggested by the model was enough to actually carry out the task when compared to the actual energy consumed in the test, suggesting that the proposed model could be useful for energy budgeting in actual mobile robot applications. |
| first_indexed | 2024-03-10T12:22:51Z |
| format | Article |
| id | doaj.art-172636c38c954608ae8c6041ebe21ed7 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T12:22:51Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-172636c38c954608ae8c6041ebe21ed72023-11-21T15:17:35ZengMDPI AGElectronics2079-92922021-04-0110892010.3390/electronics10080920Predicting the Energy Consumption of a Robot in an Exploration Task Using Optimized Neural NetworksLiesle Caballero0Álvaro Perafan1Martha Rinaldy2Winston Percybrooks3Faculty of Engineering, Universidad ITSA, Barranquilla 080002, ColombiaBiomedical Signal Processing and Artificial Intelligence Laboratory BSPAI, Universidad del Norte, Barranquilla 080002, ColombiaBiomedical Signal Processing and Artificial Intelligence Laboratory BSPAI, Universidad del Norte, Barranquilla 080002, ColombiaBiomedical Signal Processing and Artificial Intelligence Laboratory BSPAI, Universidad del Norte, Barranquilla 080002, ColombiaThis paper 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 proposed solution uses machine learning models trained on a subset of possible exploration tasks but able to make predictions on untested scenarios. Additionally, the proposed model does not use any kinematic or dynamic models of the robot, which are not always available. The method is based on a neural network with hyperparameter optimization to improve performance. Tabu List optimization strategy is used to determine the hyperparameter values (number of layers and number of neurons per layer) that minimize the percentage relative absolute error (%RAE) while maximize the Pearson correlation coefficient (R) between predicted data and actual data measured under a number of experimental conditions. Once the optimized artificial neural network is trained, it can be used to predict the performance of an exploration algorithm on arbitrary variations of a grid map scenario. Based on such prediction, it is possible to know the energy needed for the robot to complete the exploration task. A total of 128 tests were carried out using a robot executing two exploration algorithms in a grid map with the objective of locating a target whose location is not known a priori by the robot. The experimental energy consumption was measured and compared with the prediction of our model. A success rate of 96.093% was obtained, measured as the percentage of tests where the energy budget suggested by the model was enough to actually carry out the task when compared to the actual energy consumed in the test, suggesting that the proposed model could be useful for energy budgeting in actual mobile robot applications.https://www.mdpi.com/2079-9292/10/8/920machine learningenergy consumptionenergy predictionartificial neural network |
| spellingShingle | Liesle Caballero Álvaro Perafan Martha Rinaldy Winston Percybrooks Predicting the Energy Consumption of a Robot in an Exploration Task Using Optimized Neural Networks Electronics machine learning energy consumption energy prediction artificial neural network |
| title | Predicting the Energy Consumption of a Robot in an Exploration Task Using Optimized Neural Networks |
| title_full | Predicting the Energy Consumption of a Robot in an Exploration Task Using Optimized Neural Networks |
| title_fullStr | Predicting the Energy Consumption of a Robot in an Exploration Task Using Optimized Neural Networks |
| title_full_unstemmed | Predicting the Energy Consumption of a Robot in an Exploration Task Using Optimized Neural Networks |
| title_short | Predicting the Energy Consumption of a Robot in an Exploration Task Using Optimized Neural Networks |
| title_sort | predicting the energy consumption of a robot in an exploration task using optimized neural networks |
| topic | machine learning energy consumption energy prediction artificial neural network |
| url | https://www.mdpi.com/2079-9292/10/8/920 |
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