基于RGB-D相机算法和深度学习协同的葡萄园自主导航本地运动规划器
Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy
摘要：
随着农业3.0和4.0的到来，研究人员越来越关注通过将自动化和机器人技术引入农业过程来开发创新的智能农业和精准农业技术。自主农业田间机器已经得到农民和工业界的极大关注，以降低成本、人力工作量和所需资源。然而，实现足够的自主导航能力需要不同过程的同时合作;定位，映射和路径规划只是旨在为机器提供在半结构化和非结构化环境中操作的正确技能的一些步骤。在这种情况下，本研究提出了一种低成本的本地运动规划器，仅基于RGB-D相机，低范围的硬件和双层控制算法的葡萄园自主导航。第一种算法利用视差图及其深度表示来生成用于机器人平台的比例控制。同时，基于表示学习和对照明变化的弹性的第二备用算法可以在第一块的瞬时故障的情况下控制机器。此外，由于系统的双重性质，在使用初始数据集对深度学习模型进行初始训练之后，两种算法之间的严格协同作用打开了利用来自现场的新的自动标记数据来扩展现有模型知识的可能性。机器学习算法已经使用迁移学习进行了训练和测试，并在意大利北部地区的不同实地调查期间获得了图像，然后通过模型修剪和量化优化了设备上的推理。最后，在相关环境下，使用定制的机器人平台对整个系统进行了验证。
英文摘要：
With the advent of agriculture 3.0 and 4.0, 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 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 exploits 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. 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 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 relevant environment.
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