Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy
With the advent of agriculture 3.0 and 4.0, in view of efficient and sustainable use of resources, researchers are increasingly focusing on the development of innovative smart farming and precision agriculture technologies by introducing automation and robotics into the agricultural processes. Auton...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/8/2/27 |
| _version_ | 1827716236427919360 |
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| author | Diego Aghi Vittorio Mazzia Marcello Chiaberge |
| author_facet | Diego Aghi Vittorio Mazzia Marcello Chiaberge |
| author_sort | Diego Aghi |
| collection | DOAJ |
| description | With the advent of agriculture 3.0 and 4.0, in view of efficient and sustainable use of resources, researchers are increasingly focusing on the development of innovative smart farming and precision agriculture technologies by introducing automation and robotics into the agricultural processes. Autonomous agricultural field machines have been gaining significant attention from farmers and industries to reduce costs, human workload, and required resources. Nevertheless, achieving sufficient autonomous navigation capabilities requires the simultaneous cooperation of different processes; localization, mapping, and path planning are just some of the steps that aim at providing to the machine the right set of skills to operate in semi-structured and unstructured environments. In this context, this study presents a low-cost, power-efficient local motion planner for autonomous navigation in vineyards based only on an RGB-D camera, low range hardware, and a dual layer control algorithm. The first algorithm makes use of the disparity map and its depth representation to generate a proportional control for the robotic platform. Concurrently, a second back-up algorithm, based on representations learning and resilient to illumination variations, can take control of the machine in case of a momentaneous failure of the first block generating high-level motion primitives. Moreover, due to the double nature of the system, after initial training of the deep learning model with an initial dataset, the strict synergy between the two algorithms opens the possibility of exploiting new automatically labeled data, coming from the field, to extend the existing model’s knowledge. The machine learning algorithm has been trained and tested, using transfer learning, with acquired images during different field surveys in the North region of Italy and then optimized for on-device inference with model pruning and quantization. Finally, the overall system has been validated with a customized robot platform in the appropriate environment. |
| first_indexed | 2024-03-10T19:36:38Z |
| format | Article |
| id | doaj.art-6f4156d0eb4f4e2f9af795fcb67ce163 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-10T19:36:38Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-6f4156d0eb4f4e2f9af795fcb67ce1632023-11-20T01:39:41ZengMDPI AGMachines2075-17022020-05-01822710.3390/machines8020027Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning SynergyDiego Aghi0Vittorio Mazzia1Marcello Chiaberge2Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, 10129 Turin, ItalyPolitecnico di Torino Interdepartmental Centre for Service Robotics (PIC4SeR), 10129 Turin, ItalyPolitecnico di Torino Interdepartmental Centre for Service Robotics (PIC4SeR), 10129 Turin, ItalyWith the advent of agriculture 3.0 and 4.0, in view of efficient and sustainable use of resources, researchers are increasingly focusing on the development of innovative smart farming and precision agriculture technologies by introducing automation and robotics into the agricultural processes. Autonomous agricultural field machines have been gaining significant attention from farmers and industries to reduce costs, human workload, and required resources. Nevertheless, achieving sufficient autonomous navigation capabilities requires the simultaneous cooperation of different processes; localization, mapping, and path planning are just some of the steps that aim at providing to the machine the right set of skills to operate in semi-structured and unstructured environments. In this context, this study presents a low-cost, power-efficient local motion planner for autonomous navigation in vineyards based only on an RGB-D camera, low range hardware, and a dual layer control algorithm. The first algorithm makes use of the disparity map and its depth representation to generate a proportional control for the robotic platform. Concurrently, a second back-up algorithm, based on representations learning and resilient to illumination variations, can take control of the machine in case of a momentaneous failure of the first block generating high-level motion primitives. Moreover, due to the double nature of the system, after initial training of the deep learning model with an initial dataset, the strict synergy between the two algorithms opens the possibility of exploiting new automatically labeled data, coming from the field, to extend the existing model’s knowledge. The machine learning algorithm has been trained and tested, using transfer learning, with acquired images during different field surveys in the North region of Italy and then optimized for on-device inference with model pruning and quantization. Finally, the overall system has been validated with a customized robot platform in the appropriate environment.https://www.mdpi.com/2075-1702/8/2/27agricultural field machinesstereo visiondeep learningautonomous navigationedge aitransfer learning |
| spellingShingle | Diego Aghi Vittorio Mazzia Marcello Chiaberge Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy Machines agricultural field machines stereo vision deep learning autonomous navigation edge ai transfer learning |
| title | Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy |
| title_full | Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy |
| title_fullStr | Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy |
| title_full_unstemmed | Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy |
| title_short | Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy |
| title_sort | local motion planner for autonomous navigation in vineyards with a rgb d camera based algorithm and deep learning synergy |
| topic | agricultural field machines stereo vision deep learning autonomous navigation edge ai transfer learning |
| url | https://www.mdpi.com/2075-1702/8/2/27 |
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