Autonomous Navigation in Complex Environments
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914632146354176 |
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| author | Gerstenslager, Andrew Lewis, Jomol McKenna, Liam Patel, Poorva |
| author_facet | Gerstenslager, Andrew Lewis, Jomol McKenna, Liam Patel, Poorva |
| contents | This paper explores the application of CNN-DNN network fusion to construct a robot navigation controller within a simulated environment. The simulated environment is constructed to model a subterranean rescue situation, such that an autonomous agent is tasked with finding a goal within an unknown cavernous system. Imitation learning is used to train the control algorithm to use LiDAR and camera data to navigate the space and find the goal. The trained model is then tested for robustness using Monte-Carlo. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_03267 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Autonomous Navigation in Complex Environments Gerstenslager, Andrew Lewis, Jomol McKenna, Liam Patel, Poorva Robotics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Systems and Control This paper explores the application of CNN-DNN network fusion to construct a robot navigation controller within a simulated environment. The simulated environment is constructed to model a subterranean rescue situation, such that an autonomous agent is tasked with finding a goal within an unknown cavernous system. Imitation learning is used to train the control algorithm to use LiDAR and camera data to navigate the space and find the goal. The trained model is then tested for robustness using Monte-Carlo. |
| title | Autonomous Navigation in Complex Environments |
| topic | Robotics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2401.03267 |