Development Of Agricultural Robots Research Articles

Cooperative Grape Harvesting Using Heterogeneous Autonomous Robots

The development of agricultural robots is an increasingly popular research field aiming at addressing the widespread labor shortages in the farming industry and the ever-increasing food production demands. In many cases, multiple cooperating robots can be deployed in order to reduce task duration, perform an operation not possible with a single robot, or perform an operation more effectively. Building on previous results, this application paper deals with a cooperation strategy that allows two heterogeneous robots to cooperatively carry out grape harvesting, and its implementation is demonstrated. More specifically, the cooperative grape harvesting task involves two heterogeneous robots, where one robot (i.e., the expert) is assigned the grape harvesting task, whereas the second robot (i.e., the helper) is tasked with supporting the harvesting task by carrying the harvested grapes. The proposed cooperative harvesting methodology ensures safe and effective interactions between the robots. Field experiments have been conducted in order firstly to validate the effectiveness of the coordinated navigation algorithm and secondly to demonstrate the proposed cooperative harvesting method. The paper reports on the conclusions drawn from the field experiments, and recommendations for future enhancements are made. The potential of sophisticated as well as explainable decision-making based on logic for enhancing the cooperation of autonomous robots in agricultural applications is discussed in the context of mathematical lattice theory.

Tomato detection based on convolutional neural network for robotic application

AbstractThe development of agricultural robots and the promotion of agricultural production automation are important means to alleviate the shortage of agricultural labor. Fruit and vegetable detection is a prerequisite for accurate harvesting by robots. It directly determines the efficiency and quality of harvesting operations. In order to meet the requirements of target positioning and recognition of tomato harvesting robots, this paper studies tomato recognition technology based on YOLOv3 convolutional neural network algorithm. And the tomato detection process of the YOLOv3 model is presented. The YOLOv3 model training dataset is constructed based on greenhouse tomato plants. The test results show that the model based on the YOLOv3 convolutional neural network has a better detection effect.

Development of Monitoring Robot System for Tomato Fruits in Hydroponic Greenhouses

Crop monitoring is highly important in terms of the efficient and stable performance of tasks such as planting, spraying, and harvesting, and for this reason, several studies are being conducted to develop and improve crop monitoring robots. In addition, the applications of deep learning algorithms are increasing in the development of agricultural robots since deep learning algorithms that use convolutional neural networks have been proven to show outstanding performance in image classification, segmentation, and object detection. However, most of these applications are focused on the development of harvesting robots, and thus, there are only a few studies that improve and develop monitoring robots through the use of deep learning. For this reason, we aimed to develop a real-time robot monitoring system for the generative growth of tomatoes. The presented method detects tomato fruits grown in hydroponic greenhouses using the Faster R-CNN (region-based convolutional neural network). In addition, we sought to select a color model that was robust to external light, and we used hue values to develop an image-based maturity standard for tomato fruits; furthermore, the developed maturity standard was verified through comparison with expert classification. Finally, the number of tomatoes was counted using a centroid-based tracking algorithm. We trained the detection model using an open dataset and tested the whole system in real-time in a hydroponic greenhouse. A total of 53 tomato fruits were used to verify the developed system, and the developed system achieved 88.6% detection accuracy when completely obscured fruits not captured by the camera were included. When excluding obscured fruits, the system’s accuracy was 90.2%. For the maturity classification, we conducted qualitative evaluations with the assistance of experts.

Simulation software and virtual environments for acceleration of agricultural robotics: Features highlights and performance comparison

Research efforts for development of agricultural robots that can effectively perform tedious field tasks have grown significantly in the past decade. Agricultural robots are complex systems that require interdisciplinary collaborations between different research groups for effective task delivery in unstructured crops and plants environments. With the exception of milking robots, the extensive research works that have been carried out in the past two decades for adaptation of robotics in agriculture have not yielded a commercial product to date. To accelerate this pace, simulation approach and evaluation methods in virtual environments can provide an affordable and reliable framework for experimenting with different sensing and acting mechanisms in order to verify the performance functionality of the robot in dynamic scenarios. This paper reviews several professional simulators and custom-built virtual environments that have been used for agricultural robotic applications. The key features and performance efficiency of three selected simulators were also compared. A simulation case study was demonstrated to highlight some of the powerful functionalities of the Virtual Robot Experimentation Platform. Details of the objects and scenes were presented as the proof-of-concept for using a completely simulated robotic platform and sensing systems in a virtual citrus orchard. It was shown that the simulated workspace can provide a configurable and modular prototype robotic system that is capable of adapting to several field conditions and tasks through easy testing and debugging of control algorithms with zero damage risk to the real robot and to the actual equipment. This review suggests that an open-source software platform for agricultural robotics will significantly accelerate effective collaborations between different research groups for sharing existing workspaces, algorithms, and reusing the materials. Keywords: agricultural robotics, precision agriculture, virtual orchards, digital agriculture, simulation software, multi-robots DOI: 10.25165/j.ijabe.20181104.4032 Citation: Shamshiri R R, Hameed I A, Pitonakova L, Weltzien C, Balasundram S K, Yule I J, et al. Simulation software and virtual environments for acceleration of agricultural robotics: Features highlights and performance comparison. Int J Agric & Biol Eng, 2018; 11(4): 15–31.

Development of Agricultural Robot: The Usefulness of Simulation-Based Design with UQ

Development of Agricultural Robot: The Usefulness of Simulation-Based Design with UQ

Research and Development of Agricultural Robots in the World

Research and Development of Agricultural Robots in the World