Defect detection is the most important step in the postpartum reprocessing of kiwifruit. However, there are some small defects difficult to detect. The accuracy and speed of existing detection algorithms are difficult to meet the requirements of real-time detection. For solving these problems, we developed a defect detection model based on YOLOv5, which is able to detect defects accurately and at a fast speed. The main contributions of this research are as follows: (1) a small object detection layer is added to improve the model’s ability to detect small defects; (2) we pay attention to the importance of different channels by embedding SELayer; (3) the loss function CIoU is introduced to make the regression more accurate; (4) under the prerequisite of no increase in training cost, we train our model based on transfer learning and use the CosineAnnealing algorithm to improve the effect. The results of the experiment show that the overall performance of the improved network YOLOv5-Ours is better than the original and mainstream detection algorithms. The mAP@0.5 of YOLOv5-Ours has reached 94.7%, which was an improvement of nearly 9%, compared to the original algorithm. Our model only takes 0.1 s to detect a single image, which proves the effectiveness of the model. Therefore, YOLOv5-Ours can well meet the requirements of real-time detection and provides a robust strategy for the kiwi flaw detection system.
In this article, we present a new scheme that approximates unknown sensorimotor models of robots by using feedback signals only. The formulation of the uncalibrated sensor-based regulation problem is first formulated, then, we develop a computational method that distributes the model estimation problem amongst multiple adaptive units that specialize in a local sensorimotor map. Different from traditional estimation algorithms, the proposed method requires little data to train and constrain it (the number of required data points can be analytically determined) and has rigorous stability properties (the conditions to satisfy Lyapunov stability are derived). Numerical simulations and experimental results are presented to validate the proposed method.
The robotic manipulation of composite rigiddeformable objects (i.e., those with mixed nonhomogeneous stiffness properties) is a challenging problem with clear practical applications that, despite the recent progress in the field, it has not been sufficiently studied in the literature. To deal with this issue, in this article, we propose a new visual servoing method that has the capability to manipulate this broad class of objects (which varies from soft to rigid) with the same adaptive strategy. To quantify the object's infinite-dimensional configuration, our new approach computes a compact feedback vector of 2-D contour moments features. A sliding mode control scheme is then designed to simultaneously ensure the finite-time convergence of both the feedback shape error and the model estimation error. The stability of the proposed framework (including the boundedness of all the signals) is rigorously proved with Lyapunov theory. Detailed simulations and experiments are presented to validate the effectiveness of the proposed approach. To the best of the author's knowledge, this is the first time that contour moments along with finite-time control have been used to solve this difficult manipulation problem.
Research on soft robots and swimming robots has been widely reported and demonstrated. However, none of these soft swimming robots can swim flexibly and efficiently using legs, just like a frog. This paper demonstrates a self-contained, untethered swimming robotic frog actuated by 12 pneumatic soft actuators, which can swim in the water for dozens of minutes by mimicking the paddling gait of the natural frog. We designed two kinds of pneumatic soft actuators as the joints on the robotic frog’s legs, which allows the legs to be lighter and more compact. It is found that such soft actuators have great potential in developing amphibious bionic robots, because they are fast-responding, inherently watertight and simple in structural design. The kinematic analysis in swimming locomotion was conducted for the prototype robotic frog, and the locomotion trajectory of each leg was planned based on the analysis of the paddling gait of frogs. Combined with the deformation model of the soft actuators, the robotic frog’s legs are controlled by coordinating the air pressure of each joint actuator. The robotic frog’s body is compact and the total mass is 1.29 kg. Different paddling gaits were tested to investigate swimming performance. The results show that the robotic frog has agile swimming ability and high environmental adaptability. The robotic frog can swim forward more than 0.6 m (3.4 times the body length) in one paddling gait cycle(6 s), whose average swimming velocity is about 0.1 m s−1, and the minimum turning radius is about 0.15 m (less than 1 body length).
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