Developing an IoT-Enabled Cloud Management Platform for Agricultural Machinery Equipped with Automatic Navigation Systems

Zhang, Fan; Zhang, Wenyu; Luo, Xiwen; Zhang, Zhigang; Lu, Yueteng; Wang, Ben

doi:10.3390/agriculture12020310

Cited by 13 publications

(5 citation statements)

References 33 publications

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“…To verify the performance of the robot visual navigation and obstacle avoidance control system described in Sections 3 and 4, a Three-Wheeled Differential Mobile Robot (TWDMR) was used [50]. It is mainly composed of a vision camera, laser radar (RPLIDAR S2, measuring radius 30 m, frequency 32 K), a wheeled robot chassis, and ROS software control system.…”

Section: System Compositionmentioning

confidence: 99%

Visual Navigation and Obstacle Avoidance Control for Agricultural Robots via LiDAR and Camera

Han,

Wu,

Luo

et al. 2023

Remote Sensing

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Obstacle avoidance control and navigation in unstructured agricultural environments are key to the safe operation of autonomous robots, especially for agricultural machinery, where cost and stability should be taken into account. In this paper, we designed a navigation and obstacle avoidance system for agricultural robots based on LiDAR and a vision camera. The improved clustering algorithm is used to quickly and accurately analyze the obstacle information collected by LiDAR in real time. Also, the convex hull algorithm is combined with the rotating calipers algorithm to obtain the maximum diameter of the convex polygon of the clustered data. Obstacle avoidance paths and course control methods are developed based on the danger zones of obstacles. Moreover, by performing color space analysis and feature analysis on the complex orchard environment images, the optimal H-component of HSV color space is selected to obtain the ideal vision-guided trajectory images based on mean filtering and corrosion treatment. Finally, the proposed algorithm is integrated into the Three-Wheeled Mobile Differential Robot (TWMDR) platform to carry out obstacle avoidance experiments, and the results show the effectiveness and robustness of the proposed algorithm. The research conclusion can achieve satisfactory results in precise obstacle avoidance and intelligent navigation control of agricultural robots.

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Section: System Compositionmentioning

confidence: 99%

Visual Navigation and Obstacle Avoidance Control for Agricultural Robots via LiDAR and Camera

Han,

Wu,

Luo

et al. 2023

Remote Sensing

Self Cite

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“…Using cloud computing technology in a smart farming platform is another solution that can address scalability challenges related to capacity due to flexible and robust data collection, management, and processing capabilities [83]. Cloud computing provides a high level of flexibility by providing remote services for monitoring and managing farm data.…”

Section: Scalabilitymentioning

confidence: 99%

A Platform Approach to Smart Farm Information Processing

et al. 2022

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With the rapid growth of population and the increasing demand for food worldwide, improving productivity in farming procedures is essential. Smart farming is a concept that emphasizes the use of modern technologies such as the Internet of Things (IoT) and artificial intelligence (AI) to enhance productivity in farming practices. In a smart farming scenario, large amounts of data are collected from diverse sources such as wireless sensor networks, network-connected weather stations, monitoring cameras, and smartphones. These data are valuable resources to be used in data-driven services and decision support systems (DSS) in farming applications. However, one of the major challenges with these large amounts of agriculture data is their immense diversity in terms of format and meaning. Moreover, the different services and technologies in a smart farming ecosystem have limited capability to work together due to the lack of standardized practices for data and system integration. These issues create a significant challenge in cooperative service provision, data and technology integration, and data-sharing practices. To address these issues, in this paper, we propose the platform approach, a design approach intended to guide building effective, reliable, and robust smart farming systems. The proposed platform approach considers six requirements for seamless integration, processing, and use of farm data. These requirements in a smart farming platform include interoperability, reliability, scalability, real-time data processing, end-to-end security and privacy, and standardized regulations and policies. A smart farming platform that considers these requirements leads to increased productivity, profitability, and performance of connected smart farms. In this paper, we aim at introducing the platform approach concept for smart farming and reviewing the requirements for this approach.

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“…Under the autonomous farm operation circumstances [23][24][25][26] , the grain truck automatically moves with the crop harvester and completes the storage and transfer operations, which is a common form of agricultural machinery collaborative operation [27,28] , as shown in Figure 1a.…”

Section: Grain Truck Controller Strategymentioning

confidence: 99%

Control Strategy of Grain Truck Following Operation Considering Variable Loads and Control Delay

Chong

et al. 2022

Agriculture

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Considering the slow response and unstable velocity of agricultural machinery caused by soil resistance, actuator delay, environmental change, velocity fluctuation, and other internal and external factors under real working conditions, a kind of agricultural machinery following a control system that considers variable load and control delay was proposed. Taking distance-keeping, velocity-following, and acceleration-following as parameters, the controller model was deduced, and the influence of different values of model parameters on the driving stability of agricultural machinery was analyzed in detail. In addition, this paper describes a kind of agricultural machinery following a strategy that can realize the graded adjustment of vehicle distance with the dynamic increase in vehicle weight. Then, the following strategy, under the influence of velocity and quality, was simulated and verified using MATLAB/Simulink (MATLAB2016a, mathworks: Natick, Massachusetts, USA). When the crop harvester was at 1.5 m/s and the amplitude of velocity fluctuation was 0.3 m and 1.3 m, respectively, the grain truck could adjust its velocity to keep up with the crop harvester to complete the operation task. Simulation verification was carried out for the proposed graded adjustment of vehicle distance of agricultural machinery following strategy. The unit mass of the crops was set at 360 kg, and the vehicle distance changed at 18s to adapt to the graded adjustment of the vehicle distance following strategy. Finally, a real-vehicle validation test was carried out, and the results show that the grain truck velocity can keep up with the change of crop harvester velocity on the basis of maintaining the desired vehicle distance, the grain truck velocity can keep up with the change of crop harvester velocity on the road condition, which verifies the effectiveness and feasibility of the proposed method.

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Developing an IoT-Enabled Cloud Management Platform for Agricultural Machinery Equipped with Automatic Navigation Systems

Cited by 13 publications

References 33 publications

Visual Navigation and Obstacle Avoidance Control for Agricultural Robots via LiDAR and Camera

Visual Navigation and Obstacle Avoidance Control for Agricultural Robots via LiDAR and Camera

A Platform Approach to Smart Farm Information Processing

Control Strategy of Grain Truck Following Operation Considering Variable Loads and Control Delay

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