A Spatial AI-Based Agricultural Robotic Platform for Wheat Detection and Collision Avoidance
A Spatial AI-Based Agricultural Robotic Platform for Wheat Detection and Collision Avoidance
复制标题
DOI：
10.3390/ai3030042
复制
发表时间：
2022-08
期刊：
影响因子：
--
通讯作者：
Sujith Gunturu;Arslan Munir;Hayat Ullah;S. Welch;D. Flippo
中科院分区：
文献类型：
--
作者：
Sujith Gunturu;Arslan Munir;Hayat Ullah;S. Welch;D. Flippo
为了在整个小麦生长季节获得更一致的测量结果，我们构思并设计了一个自主机器人平台，利用空间人工智能（AI）在作物行中导航时进行碰撞避免。农学家的主要约束是开车时不能轧过小麦。因此，我们训练了一个空间深度学习模型，帮助机器人在田间自主导航，同时避免与小麦碰撞。为了训练这个模型，我们使用了预先标记的小麦图像的公开数据库，以及我们在田间收集的小麦图像。我们使用MobileNet单次射击检测器（SSD）作为我们的深度学习模型来检测田间的小麦。为了提高机器人对现场环境的实时响应帧率，我们在小麦图像上训练MobileNet SSD，并使用了一种新的立体相机Luxonis深度AI相机。新训练的模型和摄像头可以达到每秒18-23帧的帧率（fps）——足以让机器人每行驶2-3英寸就处理一次周围环境。一旦我们知道机器人能准确地探测周围环境，我们就着手解决机器人的自主导航问题。新的立体摄像头可以让机器人确定自己与训练过的物体之间的距离。在这项工作中，我们还开发了一种导航和避碰算法，该算法利用这些距离信息帮助机器人看到周围环境并在田间机动，从而精确地避免与小麦作物发生碰撞。进行了大量的实验来评估我们提出的方法的性能。我们还将我们提出的MobileNet SSD模型获得的定量结果与其他最先进的目标检测模型（如YOLO V5和Faster基于区域的卷积神经网络（R-CNN）模型）的结果进行了比较。详细的对比分析表明，该方法在模型精度和推理速度两方面都是有效的。
To obtain more consistent measurements through the course of a wheat growing season, we conceived and designed an autonomous robotic platform that performs collision avoidance while navigating in crop rows using spatial artificial intelligence (AI). The main constraint the agronomists have is to not run over the wheat while driving. Accordingly, we have trained a spatial deep learning model that helps navigate the robot autonomously in the field while avoiding collisions with the wheat. To train this model, we used publicly available databases of prelabeled images of wheat, along with the images of wheat that we have collected in the field. We used the MobileNet single shot detector (SSD) as our deep learning model to detect wheat in the field. To increase the frame rate for real-time robot response to field environments, we trained MobileNet SSD on the wheat images and used a new stereo camera, the Luxonis Depth AI Camera. Together, the newly trained model and camera could achieve a frame rate of 18–23 frames per second (fps)—fast enough for the robot to process its surroundings once every 2–3 inches of driving. Once we knew the robot accurately detects its surroundings, we addressed the autonomous navigation of the robot. The new stereo camera allows the robot to determine its distance from the trained objects. In this work, we also developed a navigation and collision avoidance algorithm that utilizes this distance information to help the robot see its surroundings and maneuver in the field, thereby precisely avoiding collisions with the wheat crop. Extensive experiments were conducted to evaluate the performance of our proposed method. We also compared the quantitative results obtained by our proposed MobileNet SSD model with those of other state-of-the-art object detection models, such as the YOLO V5 and Faster region-based convolutional neural network (R-CNN) models. The detailed comparative analysis reveals the effectiveness of our method in terms of both model precision and inference speed.