Development of a GNSS/INS-based automatic navigation land levelling system

Jing, Yunpeng; Li, Qian; Ye, Wenshuai; Liu, Gang

doi:10.1016/j.compag.2023.108187

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…For example, Jing et al [12] designed an autonomous navigation control board consisting of a high-precision GNSS decoding module and an inertial measurement module. This system corrected data from the inertial navigation system (INS) and the high-precision positioning module.…”

Section: Positioning Technologymentioning

confidence: 99%

Applications of Autonomous Navigation Technologies for Unmanned Agricultural Tractors: A Review

Qu,

Zhang,

Qin

et al. 2024

Machines

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The development of unmanned agricultural tractors (UAT) represents a significant step towards intelligent agricultural equipment. UAT technology is expected to lighten the workload of laborers and enhance the accuracy and efficiency of mechanized operations. Through the investigation of 123 relevant studies in the literature published in recent years, this article reviews three aspects of autonomous navigation technologies for UATs: perception, path planning and tracking, and motion control. The advantages and deficiencies of these technologies in the context of UATs are clarified by analyzing technical principles and the status of current research. We conduct summaries and analyses of existing unmanned navigation solutions for different application scenarios in order to identify current bottleneck issues. Based on the analysis of the applicability of autonomous navigation technologies in UATs, it can be seen that fruitful research progress has been achieved. The review also summarizes the common problems seen in current UAT technologies. The application of research to the sharing and integrating of multi-source data for autonomous navigation has so far been relatively weak. There is an urgent need for high-precision and high-stability sensing equipment. The universality of path planning methods and the efficiency and precision of path tracking need to be improved, and it is also necessary to develop highly reliable electrical control modules to enhance motion control performance. Overall, advanced sensors, high-performance intelligent algorithms, and reliable electrical control hardware are key factors in promoting the development of UAT technology.

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Section: Positioning Technologymentioning

confidence: 99%

Applications of Autonomous Navigation Technologies for Unmanned Agricultural Tractors: A Review

Qu,

Zhang,

Qin

et al. 2024

Machines

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“…Autonomous navigation mainly includes mapping, positioning, and path planning, and the accurate perception of environmental information is critical for precise navigation. Currently, the sensors used for environmental perception mainly include Global Navigation Satellite System (GNSS), vision, LiDAR, attitude sensor, and other sensors ( İrsel and Altinbalik, 2018 ; Jiang et al., 2023 ; Jia et al., 2015 ; Arad et al., 2020 ; Zhang et al., 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Research on autonomous navigation system of greenhouse electric crawler tractor based on LiDAR

Guo,

Li,

Wang

et al. 2024

Front. Plant Sci.

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The application of autonomous navigation technology of electric crawler tractors is an important link in the development of intelligent greenhouses. Aiming at the characteristics of enclosed and narrow space and uneven ground potholes in greenhouse planting, to improve the intelligence level of greenhouse electric crawler tractors, this paper develops a navigation system of electric crawler tractors for the greenhouse planting environment based on LiDAR technology. The navigation hardware system consists of five modules: the information perception module, the control module, the communication module, the motion module, and the power module. The software system is composed of three layers: the application layer, the data processing layer, and the execution layer. The developed navigation system uses LiDAR, Inertial Measurement Unit (IMU) and wheel speed sensor to sense the greenhouse environment and the crawler tractor’s information, employs the Gmapping algorithm to build the greenhouse environment map, and utilizes the adaptive Monte Carlo positioning algorithm for positioning. The simulation test of different global path planning algorithms in Matlab shows that the A* algorithm obtains the optimal overall global path. In the scene of map 5, the path planned by the A* algorithm is the most significant, and the number of inflection points is reduced by 40.00% and 87.50%, respectively; meanwhile, the path length is the same as that of the Dijkstra algorithm, but the runtime is reduced by 68.87% and 81.49%, respectively; compared with the RRT algorithm, the path length is reduced by 7.27%. Therefore, the A* algorithm and the Dynamic Window Approach (DWA) method are used for tractor navigation and obstacle avoidance, which ensures global path optimality while also achieving effective local path planning for obstacle avoidance. The test results suggest that the maximum lateral deviation of the built map is 6 cm, and the maximum longitudinal deviation is 16 cm, which meets the requirement of map accuracy. Additionally, the results of the navigation accuracy test indicate that the maximum lateral deviation of navigation is less than 13 cm, the average lateral deviation is less than 7 cm, and the standard lateral deviation is less than 8 cm. The maximum heading deviation is less than 14°, the average heading deviation is less than 7°, and the standard deviation is less than 8°. These results show that the developed navigation system meets the navigation accuracy requirements of electric crawler tractors in the greenhouse environment.

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“…Li et al proposed a fuzzy adaptive finite impulse response KF to fuse the position and attitude from the GNSS and IMU [18]. In order to handle the unknown a priori knowledge on an SSM or noise, Jing et al developed an adaptive square root cubature KF (CKF) to improve the path-tracking accuracy of a land leveling system, where the process noise and measurement noise are estimated online by the Sage-Husa method [19]. Wang et al further improved the robustness of the CKF by combining the maximum correntropy and resampling-free sigma-point update, which enhances the reliability of the GNSS/IMU under the GNSS-denied environment [20].…”

Section: Introductionmentioning

confidence: 99%

Improved Information Fusion for Agricultural Machinery Navigation Based on Context-Constrained Kalman Filter and Dual-Antenna RTK

Cui,

Zhang,

Wei

et al. 2024

Actuators

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Automatic navigation based on dual-antenna real-time kinematic (RTK) positioning has been widely employed for unmanned agricultural machinery, whereas GNSS inevitably suffers from signal blocking and electromagnetic interference. In order to improve the reliability of an RTK-based navigation system in a GNSS-challenged environment, an integrated navigation system is preferred for autonomous navigation, which increases the complexity and cost of the navigation system. The information fusion of integrated navigation has been dominated by Kalman filter (KF) for several decades, but the KF cannot assimilate the known knowledge of the navigation context efficiently. In this paper, the geometric characteristics of the straight path and path-tracking error were employed to formulate the constraint measurement model, which suppresses the position error in the case of RTK-degraded scenarios. The pseudo-measurements were then imported into the KF framework, and the smoothed navigation state was generated as a byproduct, which improves the reliability of the RTK positioning without external sensors. The experiment result of the mobile vehicle automatic navigation indicates that the tracking error-constrained KF (EC-KF) outperforms the trajectory-constrained KF (TC-KF) and KF when the RTK system outputs a float or single-point position (SPP) solution. In the case where the duration of the SPP solution was 20 s, the positioning errors of the EC-KF and TC-KF were reduced by 38.50% and 24.04%, respectively, compared with those of the KF.

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Development of a GNSS/INS-based automatic navigation land levelling system

Cited by 8 publications

References 22 publications

Applications of Autonomous Navigation Technologies for Unmanned Agricultural Tractors: A Review

Applications of Autonomous Navigation Technologies for Unmanned Agricultural Tractors: A Review

Research on autonomous navigation system of greenhouse electric crawler tractor based on LiDAR

Improved Information Fusion for Agricultural Machinery Navigation Based on Context-Constrained Kalman Filter and Dual-Antenna RTK

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