Sensor Fusion for Social Navigation on a Mobile Robot Based on Fast Marching Square and Gaussian Mixture Model
Mobile robot navigation has been studied for a long time, and it is nowadays widely used in multiple applications. However, it is traditionally focused on two-dimensional geometric characteristics of the environments. There are situations in which robots need to share space with people, so additiona...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/22/8728 |
| _version_ | 1797464016001433600 |
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| author | Alicia Mora Adrian Prados Alberto Mendez Ramon Barber Santiago Garrido |
| author_facet | Alicia Mora Adrian Prados Alberto Mendez Ramon Barber Santiago Garrido |
| author_sort | Alicia Mora |
| collection | DOAJ |
| description | Mobile robot navigation has been studied for a long time, and it is nowadays widely used in multiple applications. However, it is traditionally focused on two-dimensional geometric characteristics of the environments. There are situations in which robots need to share space with people, so additional aspects, such as social distancing, need to be considered. In this work, an approach for social navigation is presented. A multi-layer model of the environment containing geometric and topological characteristics is built based on the fusion of multiple sensor information. This is later used for navigating the environment considering social distancing from individuals and groups of people. The main novelty is combining fast marching square for path planning and navigation with Gaussian models to represent people. This combination allows to create a continuous representation of the environment from which smooth paths can be extracted and modified according to dynamically captured data. Results prove the practical application of the method on an assistive robot for navigating indoor scenarios, including a behavior for crossing narrow passages. People are efficiently detected and modeled to assure their comfort when robots are around. |
| first_indexed | 2024-03-09T18:00:56Z |
| format | Article |
| id | doaj.art-5c0cdac59ae74a4fa816f01c3be98a49 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T18:00:56Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5c0cdac59ae74a4fa816f01c3be98a492023-11-24T09:54:49ZengMDPI AGSensors1424-82202022-11-012222872810.3390/s22228728Sensor Fusion for Social Navigation on a Mobile Robot Based on Fast Marching Square and Gaussian Mixture ModelAlicia Mora0Adrian Prados1Alberto Mendez2Ramon Barber3Santiago Garrido4Robotics Lab, Universidad Carlos III de Madrid, 28911 Madrid, SpainRobotics Lab, Universidad Carlos III de Madrid, 28911 Madrid, SpainRobotics Lab, Universidad Carlos III de Madrid, 28911 Madrid, SpainRobotics Lab, Universidad Carlos III de Madrid, 28911 Madrid, SpainRobotics Lab, Universidad Carlos III de Madrid, 28911 Madrid, SpainMobile robot navigation has been studied for a long time, and it is nowadays widely used in multiple applications. However, it is traditionally focused on two-dimensional geometric characteristics of the environments. There are situations in which robots need to share space with people, so additional aspects, such as social distancing, need to be considered. In this work, an approach for social navigation is presented. A multi-layer model of the environment containing geometric and topological characteristics is built based on the fusion of multiple sensor information. This is later used for navigating the environment considering social distancing from individuals and groups of people. The main novelty is combining fast marching square for path planning and navigation with Gaussian models to represent people. This combination allows to create a continuous representation of the environment from which smooth paths can be extracted and modified according to dynamically captured data. Results prove the practical application of the method on an assistive robot for navigating indoor scenarios, including a behavior for crossing narrow passages. People are efficiently detected and modeled to assure their comfort when robots are around.https://www.mdpi.com/1424-8220/22/22/8728sensor fusionsocial navigationmulti-layer mapfast marching squaremixture Gaussian model |
| spellingShingle | Alicia Mora Adrian Prados Alberto Mendez Ramon Barber Santiago Garrido Sensor Fusion for Social Navigation on a Mobile Robot Based on Fast Marching Square and Gaussian Mixture Model Sensors sensor fusion social navigation multi-layer map fast marching square mixture Gaussian model |
| title | Sensor Fusion for Social Navigation on a Mobile Robot Based on Fast Marching Square and Gaussian Mixture Model |
| title_full | Sensor Fusion for Social Navigation on a Mobile Robot Based on Fast Marching Square and Gaussian Mixture Model |
| title_fullStr | Sensor Fusion for Social Navigation on a Mobile Robot Based on Fast Marching Square and Gaussian Mixture Model |
| title_full_unstemmed | Sensor Fusion for Social Navigation on a Mobile Robot Based on Fast Marching Square and Gaussian Mixture Model |
| title_short | Sensor Fusion for Social Navigation on a Mobile Robot Based on Fast Marching Square and Gaussian Mixture Model |
| title_sort | sensor fusion for social navigation on a mobile robot based on fast marching square and gaussian mixture model |
| topic | sensor fusion social navigation multi-layer map fast marching square mixture Gaussian model |
| url | https://www.mdpi.com/1424-8220/22/22/8728 |
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