H. Abdolmaleki; A. Jafari; H. Mousazadeh; A. Hajiahmad
Abstract
IntroductionAs the world population grows up, the quantity and quality of human food must be improved. The production yield of marine aquaculture and farming of aquatic organisms, as a valuable source of food, will be increased. Regular and online monitoring of the physical, chemical, and biological ...
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IntroductionAs the world population grows up, the quantity and quality of human food must be improved. The production yield of marine aquaculture and farming of aquatic organisms, as a valuable source of food, will be increased. Regular and online monitoring of the physical, chemical, and biological qualities of water and environmental parameters in such these controlled environments can be achieved by using advanced world technologies, such as autonomous boats. In this study, simulation of an autonomous boat has been done to help better understanding and control of this type of vessel in various environments such as dams, ports, rivers, aquatic ecosystems, and aquaculture. Hence, the main goal of this paper is to simulate and evaluate the guidance and navigation system of an autonomous boat based on the Fourth order of Runge-Kutta for determining the changes of water quality indices in a fish farming ponds.Materials and Methods In order to achieve the main goal of this study, an autonomous boat was designed and built. This boat as a general-purpose robotic trimaran boat has dimensions of 110 cm x 37 cm x40 cm and is made of Plexiglas 2 mm thick. Maximum forward speed of the boat is 125 cm s-1 (at 6850 rpm of brushless motors) and the turning radius is less than 61 cm. The environmental data can be transferred using Internet of Things (IOT), smartphones, SMS, and mini PC. The position and heading of the boat are determined using GPS and IMU data. The hydrodynamic and aerodynamic forces, moments, and coefficients of the boat model are determined and then applied in the mathematical simulation as the input of classic Runge-Kutta (RK4). The performance of the robotic boat navigational and control systems evaluated in a rectangular track with a length of 20 m and a width of 15 m in a fish farming pond in Karaj and 4 waypoints. The local coordinates of four corner of the mentioned rectangular in the pond was (0, 0), (0, 20), (15, 20), and (15, 0). The purpose of control system was to conduct the actuators in such way that boat be able to go to the next point. When the boat reaches the target distance of one m of the desired point, the next point will be introduced as a new target. The set point of boat speed was 0.4 m s-1 and zero state vector was [0, 0, 0, 0, 0, 0]. Results and DiscussionThe maximum error of position and heading of the autonomous boat is 135 cm and 11 degrees, respectively. Also, in the speed PID controller test (40 cm s-1), the average and standard deviation of the speed calculated as 40 cm s-1 and 2 cm s-1, respectively. Maximum difference between the heading obtained from the Kalman filter and received from the GPS is 11 degrees. In some situations that high precision of heading angle is not required, the GPS data can provide such accuracy of the heading. Among the variables of longitudinal, latitude, time to reach the target area, yaw rate, heading, and forward speed the minimum and maximum of percentage error are related to forward speed and yaw rate, respectively. These values show good performance of the simulated model and PID controllers.Conclusion In this study, motion simulation and evaluation of a robotic boat was carried out using a model boat and MATLAB software. The mathematical model simulated the real boat behavior correctly and the boat can be used safely in fish farming ponds to monitor environmental conditions and water quality.
N. Loveimi; A. Akram; N. Bagheri; A. Hajiahmad
Abstract
Introduction Remote sensing and using satellite images have been widely considered due to the high speed of measurement and great area of coverage. Canola is a source of edible oil and its cultivation in Iran and the world is developing. Comparing with other crops, canola, because of its yellow flowers, ...
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Introduction Remote sensing and using satellite images have been widely considered due to the high speed of measurement and great area of coverage. Canola is a source of edible oil and its cultivation in Iran and the world is developing. Comparing with other crops, canola, because of its yellow flowers, has a different canopy color, and only a few researches have been carried out in order to assess the spectral indices for prediction of its yield. Therefore, the main objective of this research is to evaluate some spectral vegetation indices to estimate the yield of canola in different growth stages. Materials and Methods The study was performed in 2016-2017 in Karaj, Iran. Three canola farms were chosen for the evaluation of the relationship between yield and some vegetation indices derived from the Sentinel-2 sensor. The sensor data were processed in five stages: before flowering, early flowering, peak of flowering, green and dry maturity, and the vegetation indices were extracted for each of them. This research was pixel-based and the pixels network of each studied farm was determined by RTKGPS. During harvesting time, for measurement of grain yield, five samples, four from the corners and one from the center of the pixel, were taken and their average was considered as the representative amount of the pixel. Totally, 112 pixels from three studied farms were used to calibrate the predictive models. By using Simple Linear Regression (SLR) models, ten new and conventional vegetation indices were assessed. Also, Multivariate Linear Regression (MLR) models and Artificial Neural Net (ANN) models with four bands, three visible bands and NIR band, as inputs, were used to predict the canola yield. In order to validate the SLR and MLR models, the "K-Fold" method of cross-validation was used, and for the validation of ANN models, 15% of data were used; 70% for the train, 15% for validation, and 15% for the test. Results and Discussion The results showed that, on the basis of SLR models, among the growth stages, the highest coefficient of determination (R2) in each of the vegetation indices belonged to one of the two stages: the peak of flowering and green maturity. According to SLR models, among the vegetation indices in different stages, the NDYI in the peak of the flowering stage had the highest correlation with yield (R2 = 73%). Also, the RVI with 29%, BNDVI with 52%, NDVI with 56%, and GNDVI with 35% had the highest R2 in the before flowering, early flowering, peak of flowering, green and dry maturity stages, respectively. MLR models resulted to the best yield predictive model at the peak of flowering stage (R2 = 76% for the calibration and R2 = 73% and RMSE = 0.641 for the validation). For ANN models, the strongest model achieved at peak of flowering stage (R2 = 92% for the calibration (train) and R2 = 77% and RMSE = 0.612 for the validation (test)). It seems that the results are affected by yellow flowers of canola, and absorption of blue light by their petals. Therefore, in the peak of the flowering stage, the reflection of the blue light is more likely to belong to green leaves and stems. Therefore, any index such as NDYI, which the blue reflection is subtracted in its equation, represents better the number of flowers, and since the density of flowers is directly related to the yield, the yield will be predicted with more precision. Conclusion The results of the analysis of the indices by SLR models showed that the correlation of each of the vegetation indices with the canola yield, in different stages of growth, has a considerable difference. Based on this model, the highest R2 in each of these indices happened in the peak of flowering or green maturity stage, and among these indices in different stages, the NDYI in the peak of the flowering stage had the highest R2. Finally, in both of the MLR and ANN models, with four bands, three visible bands and near-infrared band, as inputs, the best yield predictive model resulted in the peak of the flowering stage.
A. Hajiahmad; A. Jafari; A. R. Keyhani; H. Goli; B. No'doust
Abstract
In this paper, a low-cost dynamometer for rolling, steered wheels is described. The dynamometer was constructed to determine whether such an instrumented mechanism was practical. Four S-beam load cells, an Opto-counter and a potentiometer were used to obtain all moments, and forces using dynamic and ...
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In this paper, a low-cost dynamometer for rolling, steered wheels is described. The dynamometer was constructed to determine whether such an instrumented mechanism was practical. Four S-beam load cells, an Opto-counter and a potentiometer were used to obtain all moments, and forces using dynamic and kinematic analysis. Minimal simplifying assumptions considered for the required calculations. Overturning, aligning and rolling resistance moments besides vertical force are directly measured by the load cells. The Opto-counter detects wheel angular velocity and the potentiometer was used to measure the steering angle. The results showed that the mechanism was very well calibrated with a coefficient of determination of over 0.99 and can be used to define wheel dynamic behavior.
H. Goli; S. Minaei; A. Jafari; A. R. Keyhani; A. Hajiahmad; H. Abdolmaleki; A. M. Borghaee
Abstract
In this research, four different positioning methods were compared in order to evaluate their accuracy, using a remotely controlled robot on a specific route. These methods included: using a single GPS module, combining the data from three GPS modules, using an Inertial Measurement Unit (IMU), and GPS/IMU ...
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In this research, four different positioning methods were compared in order to evaluate their accuracy, using a remotely controlled robot on a specific route. These methods included: using a single GPS module, combining the data from three GPS modules, using an Inertial Measurement Unit (IMU), and GPS/IMU data fusion. The comparison of these four methods showed that GPS/IMU data fusion along with a Kalman filter was the most precise method, having a root mean square error of 23.4cm. Integrating the data acquired simultaneously from three GPS modules with fixed and equally spaced position and far enough from each other, had a root mean square error of 31.3cm was the second most precise method. . Also analysis of the IMU data showed that due to cumulative errors, it was not a suitable method using a single IMU for positioning.
