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The welding areas of the workpiece must be consistent with high precision to ensure the welding success during the welding of automobile parts. The purpose of this paper is to design an automatic high-precision locating and grasping system for robotic arm guided by 2D monocular vision to meet the requirements of automatic operation and high-precision welding.

A nonlinear multi-parallel surface calibration method based on adaptive k-segment master curve algorithm is proposed, which improves the efficiency of the traditional single camera calibration algorithm and accuracy of calibration. At the same time, the multi-dimension feature of target based on k-mean clustering constraint is proposed to improve the robustness and precision of registration.

A method of automatic locating and grasping based on 2D monocular vision is provided for robot arm, which includes camera calibration method and target locating method.

The system has been integrated into the welding robot of an automobile company in China.

A method of automatic locating and grasping based on 2D monocular vision is proposed, which makes the robot arm have automatic grasping function, and improves the efficiency and precision of automatic grasp of robot arm.

The purpose of this study is to select the appropriate forecasting model at the retail stage for selected vegetables on the basis of performance analysis.

Various forecasting models such as the Box–Jenkins-based auto-regressive integrated moving average model and machine learning-based algorithms such as long short-term memory (LSTM) networks, support vector regression (SVR), random forest regression, gradient boosting regression (GBR) and extreme GBR (XGBoost/XGBR) were proposed and applied (i.e. modeling, training, testing and predicting) at the retail stage for selected vegetables to forecast demand. The performance analysis (i.e. forecasting error analysis) was carried out to select the appropriate forecasting model at the retail stage for selected vegetables.

From the obtained results for a case environment, it was observed that the machine learning algorithms, namely LSTM and SVR, produced the better results in comparison with other different demand forecasting models.

The results obtained from the case environment cannot be generalized. However, it may be used for forecasting of different agriculture produces at the retail stage, capturing their demand environment.

The implementation of LSTM and SVR for the case situation at the retail stage will reduce the forecast error, daily retail inventory and fresh produce wastage and will increase the daily revenue.

The demand forecasting model selection for agriculture produce at the retail stage on the basis of performance analysis is a unique study where both traditional and non-traditional models were analyzed and compared.