Collision-free autonomous path planning under a dynamic and uncertainty vineyard environment is the most important issue which needs to be resolved firstly in the process of improving robotic harvesting manipulator intelligence. We present and apply energy optimal and artificial potential field to develop a path planning method for six degree of freedom (DOF) serial harvesting robot under dynamic uncertain environment. Firstly, the kinematical model of Six-DOF serial manipulator was constructed by using the Denavit-Hartenberg (D-H) method. The model of obstacles was defined by axis-aligned bounding box, and then the configuration space of harvesting robot was described by combining the obstacles and arm space of robot. Secondly, the harvesting sequence in path planning was computed by energy optimal method, and the anticollision path points were automatically generated based on the artificial potential field and sampling searching method. Finally, to verify and test the proposed path planning algorithm, a virtual test system based on virtual reality was developed. After obtaining the space coordinates of grape picking point and anticollision bounding volume, the path points were drew out by the proposed method. 10 times picking tests for grape anticollision path planning were implemented on the developed simulation system, and the success rate was up to 90%. The results showed that the proposed path planning method can be used to the harvesting robot.
Just-in-time learning (JITL) has
been widely applied to data-driven
modeling to deal with the nonlinearity problems in industrial processes.
To mitigate the effects of noise existing in JITL, probabilistic JITL
(PJITL) selects samples based on the probability distributions. Considering
the existence of missing data situation, the PJITL algorithm could
also cope with that. However, traditional JITL-based methods, including
PJITL, cannot flexibly select the number of training samples for each
query sample, which would in return influence the accuracy of prediction
for a part of query samples. To solve this problem, we proposed a
method named “variable-scale PJITL” (VS-PJITL) which
can determine the sizes of the local models for each query sample
using a new sample selection criterion. Based on the Euclidean distance,
the sample selection criterion also applies to the variable-scale
JITL (VS-JITL). Then, comparisons of VS-PJITL, PJITL, JITL, and VS-JITL
are tested on a simulated data set and a real industrial data set
from the catalytic naphtha reforming process. By analyzing the two
cases above, VS-PJITL is considered to have superior performance to
the original PJITL (root-mean-square error reduced by 0.3355 and 0.4778).
The detection of defects on irregular surfaces with specular reflection characteristics is an important part of the production process of sanitary equipment. Currently, defect detection algorithms for most irregular surfaces rely on the handcrafted extraction of shallow features, and the ability to recognize these defects is limited. To improve the detection accuracy of micro-defects on irregular surfaces in an industrial environment, we propose an improved Faster R-CNN model. Considering the variety of defect shapes and sizes, we selected the K-Means algorithm to generate the aspect ratio of the anchor box according to the size of the ground truth, and the feature matrices are fused with different receptive fields to improve the detection performance of the model. The experimental results show that the recognition accuracy of the improved model is 94.6% on a collected ceramic dataset. Compared with SVM (Support Vector Machine) and other deep learning-based models, the proposed model has better detection performance and robustness to illumination, which proves the practicability and effectiveness of the proposed method.
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