Improving resilience of sensors in planetary exploration using data-driven models
Improving the resilience of sensor systems in space exploration is a key objective since the environmental conditions to which they are exposed are very harsh. For example, it is known that the presence of flying debris and Dust Devils on the Martian surface can partially damage sensors present in r...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2023-01-01
|
| Series: | Machine Learning: Science and Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-2153/acefaa |
| _version_ | 1797693069063094272 |
|---|---|
| author | Dileep Kumar Manuel Dominguez-Pumar Elisa Sayrol-Clols Josefina Torres Mercedes Marín Javier Gómez-Elvira Luis Mora Sara Navarro Jose Rodríguez-Manfredi |
| author_facet | Dileep Kumar Manuel Dominguez-Pumar Elisa Sayrol-Clols Josefina Torres Mercedes Marín Javier Gómez-Elvira Luis Mora Sara Navarro Jose Rodríguez-Manfredi |
| author_sort | Dileep Kumar |
| collection | DOAJ |
| description | Improving the resilience of sensor systems in space exploration is a key objective since the environmental conditions to which they are exposed are very harsh. For example, it is known that the presence of flying debris and Dust Devils on the Martian surface can partially damage sensors present in rovers/landers. The objective of this work is to show how data-driven methods can improve sensor resilience, particularly in the case of complex sensors, with multiple intermediate variables, feeding an inverse algorithm (IA) based on calibration data. The method considers three phases: an initial phase in which the sensor is calibrated in the laboratory and an IA is designed; a second phase, in which the sensor is placed at its intended location and sensor data is used to train data-driven model; and a third phase, once the model has been trained and partial damage is detected, in which the data-driven algorithm is reducing errors. The proposed method is tested with the intermediate data of the wind sensor of the TWINS instrument (NASA InSight mission), consisting of two booms placed on the deck of the lander, and three boards per boom. Wind speed and angle are recovered from the intermediate variables provided by the sensor and predicted by the proposed method. A comparative analysis of various data-driven methods including machine learning and deep learning (DL) methods is carried out for the proposed research. It is shown that even a simple method such as k-nearest neighbor is capable of successfully recovering missing data of a board compared to complex DL models. Depending on the selected missing board, errors are reduced by a factor between 2.43 and 4.78, for horizontal velocity; and by a factor between 1.74 and 4.71, for angle, compared with the situation of using only the two remaining boards. |
| first_indexed | 2024-03-12T02:38:17Z |
| format | Article |
| id | doaj.art-5c89ce1a012a4b628df81f7a52fd5cb7 |
| institution | Directory Open Access Journal |
| issn | 2632-2153 |
| language | English |
| last_indexed | 2024-03-12T02:38:17Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj.art-5c89ce1a012a4b628df81f7a52fd5cb72023-09-04T10:43:48ZengIOP PublishingMachine Learning: Science and Technology2632-21532023-01-014303504110.1088/2632-2153/acefaaImproving resilience of sensors in planetary exploration using data-driven modelsDileep Kumar0https://orcid.org/0000-0002-6211-1078Manuel Dominguez-Pumar1https://orcid.org/0000-0001-5439-7953Elisa Sayrol-Clols2https://orcid.org/0000-0002-0526-9733Josefina Torres3https://orcid.org/0000-0003-1035-6740Mercedes Marín4https://orcid.org/0000-0003-2328-1303Javier Gómez-Elvira5https://orcid.org/0000-0002-9068-9846Luis Mora6https://orcid.org/0000-0002-8209-1190Sara Navarro7https://orcid.org/0000-0001-8606-7799Jose Rodríguez-Manfredi8https://orcid.org/0000-0003-0461-9815Universitat Politècnica de Catalunya (UPC) , Barcelona, SpainUniversitat Politècnica de Catalunya (UPC) , Barcelona, SpainTecnoCampus, Universitat Pompeu Fabra (UPF) , Mataró, SpainCentro de Astrobiología (INTA-CSIC) , Madrid, SpainCentro de Astrobiología (INTA-CSIC) , Madrid, SpainCentro de Astrobiología (INTA-CSIC) , Madrid, SpainCentro de Astrobiología (INTA-CSIC) , Madrid, SpainCentro de Astrobiología (INTA-CSIC) , Madrid, SpainCentro de Astrobiología (INTA-CSIC) , Madrid, SpainImproving the resilience of sensor systems in space exploration is a key objective since the environmental conditions to which they are exposed are very harsh. For example, it is known that the presence of flying debris and Dust Devils on the Martian surface can partially damage sensors present in rovers/landers. The objective of this work is to show how data-driven methods can improve sensor resilience, particularly in the case of complex sensors, with multiple intermediate variables, feeding an inverse algorithm (IA) based on calibration data. The method considers three phases: an initial phase in which the sensor is calibrated in the laboratory and an IA is designed; a second phase, in which the sensor is placed at its intended location and sensor data is used to train data-driven model; and a third phase, once the model has been trained and partial damage is detected, in which the data-driven algorithm is reducing errors. The proposed method is tested with the intermediate data of the wind sensor of the TWINS instrument (NASA InSight mission), consisting of two booms placed on the deck of the lander, and three boards per boom. Wind speed and angle are recovered from the intermediate variables provided by the sensor and predicted by the proposed method. A comparative analysis of various data-driven methods including machine learning and deep learning (DL) methods is carried out for the proposed research. It is shown that even a simple method such as k-nearest neighbor is capable of successfully recovering missing data of a board compared to complex DL models. Depending on the selected missing board, errors are reduced by a factor between 2.43 and 4.78, for horizontal velocity; and by a factor between 1.74 and 4.71, for angle, compared with the situation of using only the two remaining boards.https://doi.org/10.1088/2632-2153/acefaaspace sensor systemswind sensormachine learningdeep learningsoft sensor |
| spellingShingle | Dileep Kumar Manuel Dominguez-Pumar Elisa Sayrol-Clols Josefina Torres Mercedes Marín Javier Gómez-Elvira Luis Mora Sara Navarro Jose Rodríguez-Manfredi Improving resilience of sensors in planetary exploration using data-driven models Machine Learning: Science and Technology space sensor systems wind sensor machine learning deep learning soft sensor |
| title | Improving resilience of sensors in planetary exploration using data-driven models |
| title_full | Improving resilience of sensors in planetary exploration using data-driven models |
| title_fullStr | Improving resilience of sensors in planetary exploration using data-driven models |
| title_full_unstemmed | Improving resilience of sensors in planetary exploration using data-driven models |
| title_short | Improving resilience of sensors in planetary exploration using data-driven models |
| title_sort | improving resilience of sensors in planetary exploration using data driven models |
| topic | space sensor systems wind sensor machine learning deep learning soft sensor |
| url | https://doi.org/10.1088/2632-2153/acefaa |
| work_keys_str_mv | AT dileepkumar improvingresilienceofsensorsinplanetaryexplorationusingdatadrivenmodels AT manueldominguezpumar improvingresilienceofsensorsinplanetaryexplorationusingdatadrivenmodels AT elisasayrolclols improvingresilienceofsensorsinplanetaryexplorationusingdatadrivenmodels AT josefinatorres improvingresilienceofsensorsinplanetaryexplorationusingdatadrivenmodels AT mercedesmarin improvingresilienceofsensorsinplanetaryexplorationusingdatadrivenmodels AT javiergomezelvira improvingresilienceofsensorsinplanetaryexplorationusingdatadrivenmodels AT luismora improvingresilienceofsensorsinplanetaryexplorationusingdatadrivenmodels AT saranavarro improvingresilienceofsensorsinplanetaryexplorationusingdatadrivenmodels AT joserodriguezmanfredi improvingresilienceofsensorsinplanetaryexplorationusingdatadrivenmodels |