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Breaking and entering : a novel
\"At 49, Beatrice Billings is rudderless. Her marriage is stale, her relationship with her son Thomas is limited to text messages--hostile haikus that he sends from university--and she is the primary caregiver for her mother, who is in the early stages of dementia. She has a complicated relationship with her older sister Ariel, with whom she carries on ongoing arguments in her head. Bea laments the loss of momentum she remembers feeling in her thirties, when she and everyone she knew was busy buying houses, having children, and renovating kitchens. Now she is reflecting on her life, worried about her inability to memorize a simple yoga sequence, and about the fact that she enjoys the idea of many things more than the actual things themselves (teaching, reading, sex). When Bea finds that she has both a talent and a passion for picking locks, the sense of anticipation that had been missing from her life returns. Breaking into other people's houses is something she's good at: she is a quick study, subtle, discreet, and never greedy. It's a dangerous hobby that makes her feel alive--and so she begins the guilty analysis of other people's lives, and eventually, her own.\"-- Provided by publisher.
Book
Smart desktop item picking robot design
In response to the diverse needs of novel robot application scenarios, this paper designs a smart desktop item picking robot, encompassing its working principle, structure, and transmission calculations. The smart desktop picking robot can alleviate the labor intensity associated with repetitive simple motions in desktop-level item picking, enhance work efficiency, reduce workloads, and effectively meet the demands of daily work and production. The smart desktop item picking robot designed in this paper features a simple structure, easy operation, and adaptability to various work and living scenarios.
Journal Article
A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5
The apple target recognition algorithm is one of the core technologies of the apple picking robot. However, most of the existing apple detection algorithms cannot distinguish between the apples that are occluded by tree branches and occluded by other apples. The apples, grasping end-effector and mechanical picking arm of the robot are very likely to be damaged if the algorithm is directly applied to the picking robot. Based on this practical problem, in order to automatically recognize the graspable and ungraspable apples in an apple tree image, a light-weight apple targets detection method was proposed for picking robot using improved YOLOv5s. Firstly, BottleneckCSP module was improved designed to BottleneckCSP-2 module which was used to replace the BottleneckCSP module in backbone architecture of original YOLOv5s network. Secondly, SE module, which belonged to the visual attention mechanism network, was inserted to the proposed improved backbone network. Thirdly, the bonding fusion mode of feature maps, which were inputs to the target detection layer of medium size in the original YOLOv5s network, were improved. Finally, the initial anchor box size of the original network was improved. The experimental results indicated that the graspable apples, which were unoccluded or only occluded by tree leaves, and the ungraspable apples, which were occluded by tree branches or occluded by other fruits, could be identified effectively using the proposed improved network model in this study. Specifically, the recognition recall, precision, mAP and F1 were 91.48%, 83.83%, 86.75% and 87.49%, respectively. The average recognition time was 0.015 s per image. Contrasted with original YOLOv5s, YOLOv3, YOLOv4 and EfficientDet-D0 model, the mAP of the proposed improved YOLOv5s model increased by 5.05%, 14.95%, 4.74% and 6.75% respectively, the size of the model compressed by 9.29%, 94.6%, 94.8% and 15.3% respectively. The average recognition speeds per image of the proposed improved YOLOv5s model were 2.53, 1.13 and 3.53 times of EfficientDet-D0, YOLOv4 and YOLOv3 and model, respectively. The proposed method can provide technical support for the real-time accurate detection of multiple fruit targets for the apple picking robot.
Journal Article
Design and Research of Large Ginkgo Biloba Leaf Picker
In view of the different growth patterns of ginkgo branches and the need to avoid injury to Ginkgo branches when picking ginkgo leaves, this design provides a kind of ginkgo leaf picking machine that can carry out large-scale mechanized picking in the ginkgo leaf planting area and improve ginkgo leaf picking efficiency. The picker puts the picking mechanism in the front of the tractor, which is driven by the tractor. The power needed for the picking mechanism to rotate is provided by the tractor. The picking mechanism contains the storage mechanism of ginkgo leaves in order to achieve harvesting and gathering. Mechanization of ginkgo leaf picking, the picking efficiency of ginkgo biloba leaves was improved to a greater extent.
Journal Article
A Performance Analysis of a Litchi Picking Robot System for Actively Removing Obstructions, Using an Artificial Intelligence Algorithm
Litchi is a highly favored fruit with high economic value. Mechanical automation of litchi picking is a key link for improving the quality and efficiency of litchi harvesting. Our research team has been conducting experiments to develop a visual-based litchi picking robot. However, in the early physical prototype experiments, we found that, although picking points were successfully located, litchi picking failed due to random obstructions of the picking points. In this study, the physical prototype of the litchi picking robot previously developed by our research team was upgraded by integrating a visual system for actively removing obstructions. A framework for an artificial intelligence algorithm was proposed for a robot vision system to locate picking points and to identify obstruction situations at picking points. An intelligent control algorithm was developed to control the obstruction removal device to implement obstruction removal operations by combining with the obstruction situation at the picking point. Based on the spatial redundancy of a picking point and the obstruction, the feeding posture of the robot was determined. The experiment showed that the precision of segmenting litchi fruits and branches was 88.1%, the recognition success rate of picking point recognition was 88%, the average error of picking point localization was 2.8511 mm, and an overall success rate of end-effector feeding was 81.3%. These results showed that the developed litchi picking robot could effectively implement obstruction removal.
Journal Article
Investigating the Impact of Picking Modes on the Picking Process of Peach (Prunus persica) Using Experimental and Simulation Analysis
To explore robotic peach picking in different modes, this study examined the effects of various peach picking modes on harvesting force and time. A finite element model of peach harvesting structure was established, and harvesting experiment parameters were based on the Box–Behnken design. Harvesting was simulated to collect response time and force data. Subsequently, the optimal harvesting rate under different picking modes was determined. Different picking modes were tested by simulating identical fruit harvesting in the laboratory at the optimal harvesting speed to determine the peak harvesting force and duration. The Bend mode had the lowest picking pressure and the shortest average picking time at 0.7 MPa and 4.2 s, respectively. The Pull and Twist modes had similar pressures and picking times at 1.2 and 1.1 MPa and 5.2 and 5.6 s, respectively. Harvesting in the orchard allowed for harvesting force and duration measurement under different picking modes. The differences in picking pressure and time among the three picking modes increased compared with those of simulated picking, with specific patterns being observed. Picking pressure appeared at P1max, and differences in picking time were prevalent during separation. This study offers valuable insights for future improvements in harvesting modes and efficiency enhancement.
Journal Article
Pepper-YOLO: an lightweight model for green pepper detection and picking point localization in complex environments
In the cultivation of green chili peppers, the similarity between the fruit and background color, along with severe occlusion between fruits and leaves, significantly reduces the efficiency of harvesting robots. While increasing model depth can enhance detection accuracy, complex models are often difficult to deploy on low-cost agricultural devices. This paper presents an improved lightweight Pepper-YOLO model based on YOLOv8n-Pose, designed for simultaneous detection of green chili peppers and picking points. The proposed model introduces a reversible dual pyramid structure with cross-layer connections to enhance high-and low-level feature extraction while preventing feature loss, ensuring seamless information transfer between layers. Additionally, RepNCSPELAN4 is utilized for feature fusion, improving multi-scale feature representation. Finally, the C2fCIB module replaces the CIB module to further optimize the detection and localization of large-scale pepper features. Experimental results indicate that Pepper-YOLO achieves an object detection accuracy of 82.2% and a harvesting point localization accuracy of 88.1% in complex scenes, with a Euclidean distance error of less than 12.58 pixels. Additionally, the model reduces the number of parameters by 38.3% and lowers complexity by 28.9%, resulting in a final model size of 4.3MB. Compared to state-of-the-art methods, our approach demonstrates better parameter efficiency. In summary, Pepper-YOLO exhibits high precision and real-time performance in complex environments, with a lightweight design that makes it well-suited for deployment on low-cost devices.
Journal Article
Assessment tools for clinical excoriation (skin picking) disorder: a mini review for diagnosing and monitoring symptoms severity
Excoriation (Skin Picking) Disorder (SPD) is a psychiatric condition characterized by repetitive skin picking, often affecting areas like the face, arms, and hands. It has its own diagnostic classification in ICD-10, DSM-5-TR and PDM-2. Individuals with SPD may use various tools to pick at their skin and often struggle to stop the behavior. The disorder typically arises during adolescence and is more common in females, with a lifetime prevalence of 1.4% in adults. SPD is associated with decreased quality of life and increased rates of anxiety disorders, depression, and substance abuse. Validated assessment tools are essential for diagnosing and monitoring SPD symptoms. The Skin Picking Scale (SPS), Skin Picking Scale-Revised (SPS-R), Skin Picking Impact Scale (SPIS), and Skin Picking Symptom Assessment Scale (SP-SAS) are commonly used instruments for evaluating the severity of SPD symptoms. While these tools have shown reliability and validity, there are limitations, including potential biases in self-reporting and the need for further validation in different populations and languages. Future research is needed to enhance the effectiveness of screening and assessment tools for SPD in clinical settings.
Journal Article
A comparative analysis of different paperless picking systems
Purpose
– Warehouse picking is often referred to as the most labour-intensive, expensive and time consuming operation in manual warehouses. These factors are becoming even more crucial due to recent trends in manufacturing and warehousing requiring the processing of orders that are always smaller and needed in a shorter time. For this reason, in recent years more efficient and better performing systems have been developed, employing various technological solutions that can support pickers during their work. The purpose of this paper is to introduce a comparison of five paperless picking systems (i.e. barcodes handheld, RFID tags handheld, voice picking, traditional pick-to-light, RFID pick-to-light).
Design/methodology/approach
– Warehouse picking is often referred to as the most labour-intensive, expensive and time consuming operation in manual warehouses. These factors are becoming even more crucial due to recent trends in manufacturing and warehousing requiring the processing of orders that are always smaller and needed in a shorter time. For this reason, in recent years more efficient and better performing systems have been developed, employing various technological solutions that can support pickers during their work. The present paper introduces a comparison of five paperless picking systems (i.e. barcodes handheld, RFID tags handheld, voice picking, traditional pick-to-light, RFID pick-to-light.
Findings
– The proposed approach contributes to the understanding of the performance of different technologies in different application fields; some solutions are more suitable for a low-level warehouse, others bring greater benefits in the case of picking from multilevel shelving.
Originality/value
– The study concerns an issue that until now has received very little attention in the literature. It compares some traditional solutions with some innovative ones by an economic evaluation. The presented hourly cost function also takes into account the different errors arising and their probability of occurrence.
Journal Article
Automated tea shoot picking using the YOLO network and Mamba images segmentation for top-view detection with a monocular camera
Detection and localization of tea shoots (one bud with two leaves) are critical steps in the automation of tea harvesting. Using red, green, blue-depth (RGB-D) camera to detect and locate tea shoots from side angles results in significant occlusion of tea shoots, as well as loss of depth information. To achieve automated, intelligent, and precise tea harvesting, this paper proposes a method for detecting and locating tea shoots from the top using a monocular camera. Firstly, the “You Only Look Once” (YOLO) network is employed to detect tea shoots regions in images collected by the monocular camera and to crop individual tea shoot top images. For these cropped images, a U-shaped images segmentation model based on Mamba is proposed. This model achieves a mean intersection over union (MIoU) of 87.80% and an accuracy (ACC) of 95.63%, precisely locating the specific tea shoots top regions. The center of the circumscribed circle of this region is used as the position for the next step in the picking process, accurately guiding the picking effector to the top of the tea shoot. Finally, the picking effector, controlled by feedback signals from infrared sensors, performs up-and-down reciprocation and cutting actions to complete the picking process. This method effectively avoids the problem of depth information loss during localization with RGB-D camera. To verify the effectiveness of the proposed approach, picking experiments were conducted on HouKui tea within a simulated tea garden environment, achieving a tea shoot picking success rate of 75.54%. The results indicate that this method offers significant application value and provides a new perspective for the development of automated tea shoots picking.
Journal Article