Interoperability between Real and Virtual Environments Connected by a GAN for the Path-Planning Problem
Path planning is a fundamental issue in robotic systems because it requires coordination between the environment and an agent. The path-planning generator is composed of two modules: perception and planning. The first module scans the environment to determine the location, detect obstacles, estimate...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-11-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/21/10445 |
| _version_ | 1797410998785671168 |
|---|---|
| author | Javier Maldonado-Romo Mario Aldape-Pérez |
| author_facet | Javier Maldonado-Romo Mario Aldape-Pérez |
| author_sort | Javier Maldonado-Romo |
| collection | DOAJ |
| description | Path planning is a fundamental issue in robotic systems because it requires coordination between the environment and an agent. The path-planning generator is composed of two modules: perception and planning. The first module scans the environment to determine the location, detect obstacles, estimate objects in motion, and build the planner module’s restrictions. On the other hand, the second module controls the flight of the system. This process is computationally expensive and requires adequate performance to avoid accidents. For this reason, we propose a novel solution to improve conventional robotic systems’ functions, such as systems having a small-capacity battery, a restricted size, and a limited number of sensors, using fewer elements. A navigation dataset was generated through a virtual simulator and a generative adversarial network to connect the virtual and real environments under an end-to-end approach. Furthermore, three path generators were analyzed using deep-learning solutions: a deep convolutional neural network, hierarchical clustering, and an auto-encoder. Since the path generators share a characteristic vector, transfer learning approaches complex problems by using solutions with fewer features, minimizing the costs and optimizing the resources of conventional system architectures, thus improving the limitations with respect to the implementation in embedded devices. Finally, a visualizer applying augmented reality was used to display the path generated by the proposed system. |
| first_indexed | 2024-03-09T04:38:38Z |
| format | Article |
| id | doaj.art-00c9ea5077d446e6831e2084e9324446 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T04:38:38Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-00c9ea5077d446e6831e2084e93244462023-12-03T13:24:02ZengMDPI AGApplied Sciences2076-34172021-11-0111211044510.3390/app112110445Interoperability between Real and Virtual Environments Connected by a GAN for the Path-Planning ProblemJavier Maldonado-Romo0Mario Aldape-Pérez1Postgraduate Department, Instituto Politécnico Nacional, CIDETEC, Mexico City 07700, MexicoPostgraduate Department, Instituto Politécnico Nacional, CIDETEC, Mexico City 07700, MexicoPath planning is a fundamental issue in robotic systems because it requires coordination between the environment and an agent. The path-planning generator is composed of two modules: perception and planning. The first module scans the environment to determine the location, detect obstacles, estimate objects in motion, and build the planner module’s restrictions. On the other hand, the second module controls the flight of the system. This process is computationally expensive and requires adequate performance to avoid accidents. For this reason, we propose a novel solution to improve conventional robotic systems’ functions, such as systems having a small-capacity battery, a restricted size, and a limited number of sensors, using fewer elements. A navigation dataset was generated through a virtual simulator and a generative adversarial network to connect the virtual and real environments under an end-to-end approach. Furthermore, three path generators were analyzed using deep-learning solutions: a deep convolutional neural network, hierarchical clustering, and an auto-encoder. Since the path generators share a characteristic vector, transfer learning approaches complex problems by using solutions with fewer features, minimizing the costs and optimizing the resources of conventional system architectures, thus improving the limitations with respect to the implementation in embedded devices. Finally, a visualizer applying augmented reality was used to display the path generated by the proposed system.https://www.mdpi.com/2076-3417/11/21/10445path planningmachine learningindoor navigation |
| spellingShingle | Javier Maldonado-Romo Mario Aldape-Pérez Interoperability between Real and Virtual Environments Connected by a GAN for the Path-Planning Problem Applied Sciences path planning machine learning indoor navigation |
| title | Interoperability between Real and Virtual Environments Connected by a GAN for the Path-Planning Problem |
| title_full | Interoperability between Real and Virtual Environments Connected by a GAN for the Path-Planning Problem |
| title_fullStr | Interoperability between Real and Virtual Environments Connected by a GAN for the Path-Planning Problem |
| title_full_unstemmed | Interoperability between Real and Virtual Environments Connected by a GAN for the Path-Planning Problem |
| title_short | Interoperability between Real and Virtual Environments Connected by a GAN for the Path-Planning Problem |
| title_sort | interoperability between real and virtual environments connected by a gan for the path planning problem |
| topic | path planning machine learning indoor navigation |
| url | https://www.mdpi.com/2076-3417/11/21/10445 |
| work_keys_str_mv | AT javiermaldonadoromo interoperabilitybetweenrealandvirtualenvironmentsconnectedbyaganforthepathplanningproblem AT marioaldapeperez interoperabilitybetweenrealandvirtualenvironmentsconnectedbyaganforthepathplanningproblem |