Robust digital control for autonomous skid-steered agricultural robots

Fernandez, Benjamin S.; Herrera, Pedro Javier; Cerrada, J.A.

doi:10.1016/j.compag.2018.07.038

Cited by 15 publications

(6 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, we fit a curve equation of the steering angle σ and the navigation angle θ of the agricultural robot by using Matlab14 function Fourier, shown in Eq. (12), where the edge contour points parameters s; u ð Þ are taken every 4 cm in an O-type planning path, the parameter r is obtained by implementing our algorithm procedure, and the parameter h is obtained manually.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

An Adaptive Vision Navigation Algorithm in Agricultural IoT System for Smart Agricultural Robots

Zhang¹,

Li²,

Zhao³

et al. 2020

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

As the agricultural internet of things (IoT) technology has evolved, smart agricultural robots needs to have both flexibility and adaptability when moving in complex field environments. In this paper, we propose the concept of a vision-based navigation system for the agricultural IoT and a binocular vision navigation algorithm for smart agricultural robots, which can fuse the edge contour and the height information of rows of crop in images to extract the navigation parameters. First, the speeded-up robust feature (SURF) extracting and matching algorithm is used to obtain featuring point pairs from the green crop row images observed by the binocular parallel vision system. Then the confidence density image is constructed by integrating the enhanced elevation image and the corresponding binarized crop row image, where the edge contour and the height information of crop row are fused to extract the navigation parameters (θ, d) based on the model of a smart agricultural robot. Finally, the five navigation network instruction sets are designed based on the navigation angle θ and the lateral distance d, which represent the basic movements for a certain type of smart agricultural robot working in a field. Simulated experimental results in the laboratory show that the algorithm proposed in this study is effective with small turning errors and low standard deviations, and can provide a valuable reference for the further practical application of binocular vision navigation systems in smart agricultural robots in the agricultural IoT system.

show abstract

Section: Resultsmentioning

confidence: 99%

“…The line-detection vision navigation algorithms of traditional agricultural robots have been proposed using different crop-row recognition methods for different field applications [11][12][13]. Searcy et al [14] applied the Hough transform to the extraction of navigation parameters of agricultural robots.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Vision Navigation Algorithm in Agricultural IoT System for Smart Agricultural Robots

Zhang¹,

Li²,

Zhao³

et al. 2020

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…For such autonomous agricultural robots, for example, a robust digital control system is available that implements the concept of on-board turning (Fernandez, Herrera, Cerrada, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Robotic Platform Concept for Rapid Soil Monitoring

Bykov¹,

Berezhnov²,

Leonov³

2020

Modern S&T Equipments and Problems in Agriculture

View full text Add to dashboard Cite

This article presents the substantiation of application of a robotic platform for express monitoring of the main physical-chemical and agrochemical indicators of soils in field conditions. The concept of the robotic platform provides for its movement in the field along the trajectory given by the IV International Scientific and Practical Conference "Modern S&T Equipments and Problems in Agriculture" 29 program, collection of soil samples and immersion of probes with sensors in them. The main advantage of the proposed method is its high efficiency at low economic costs. Due to the large number of measurements per unit area of the field the estimation accuracy is quite high. At the same time, for one point of the field it is possible to make measurements in different soil horizons. The conceptual scheme of the robotic platform is offered, energy calculations are performed, specific brands of sensors and other devices are selected. The presented materials are the basis for designing, manufacturing and operation of this automated vehicle. The concept of robotic platform operation corresponds to modern digital technologies and precision farming system.

show abstract

“…The typical non-strict rolling constraints include system uncertainty, external disturbance, and noise. The uncertainties such as unmodeled dynamics and variable friction parameters (Fernandez et al, 2018) that may exist in the operation of the autonomous farming vehicle are treated as unknown parameters, and the unknown parameters can be estimated and compensated by the robust or adaptive control method. The possible wheel slip can be treated as an external disturbance (Matveev et al, 2013) or estimated as a non-negligible dynamic influence (Bayar et al, 2016; Guo et al, 2018; Han et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Improving trajectory tracking performance of autonomous farming vehicles using adaptive extended state observer and finite-time technique

Zhang

Jiao

Wang

et al. 2022

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

The ability of the autonomous farming vehicle to accurately and quickly track complex reference trajectories in the field is critical to crop yields and farmer incomes. The novelty of this paper is to design a new adaptive finite-time trajectory tracking control strategy with an adaptive extended state observer to improve the autonomous farming vehicle’s trajectory tracking accuracy and convergence speed in the complex farm operating environment. The adaptive extended state observer is constructed to deal with the slip disturbance and parameter uncertainty to improve the tracking accuracy. The finite-time control strategy is adopted to improve the convergence speed of the trajectory tracking of the autonomous farming vehicle. The effectiveness and advantages of the proposed control strategy are verified by comparing it with the active disturbance rejection control and traditional PID control strategies under MATLAB/Simulink environment.

show abstract

Robust digital control for autonomous skid-steered agricultural robots

Cited by 15 publications

References 18 publications

An Adaptive Vision Navigation Algorithm in Agricultural IoT System for Smart Agricultural Robots

An Adaptive Vision Navigation Algorithm in Agricultural IoT System for Smart Agricultural Robots

Robotic Platform Concept for Rapid Soil Monitoring

Improving trajectory tracking performance of autonomous farming vehicles using adaptive extended state observer and finite-time technique

Contact Info

Product

Resources

About