Search Results (548)

In response to the growing need for efficient, stable, and non-destructive gripping in apple harvesting robots, this study proposes a novel rigid–flexible coupled end-effector. The design integrates an underactuated mechanism with a real-time force feedback control system. First, compression tests on ‘Red Fuji’ apples determined the minimum damage threshold to be 24.33 N. A genetic algorithm (GA) was employed to optimize the geometric parameters of the finger mechanism for uniform force distribution. Subsequently, a rigid–flexible coupled multibody dynamics model was established to simulate the grasping of small (70 mm), medium (80 mm), and large (90 mm) apples. Additionally, a harvesting experimental platform was constructed to verify the performance. Results demonstrated that by limiting the contact force of the distal phalange region silicone (DPRS) to 24 N via active feedback, the peak contact forces on the proximal phalange region silicone (PPRS) and middle phalange region silicone (MPRS) were effectively maintained below the damage threshold across all three sizes. The maximum equivalent stress remained significantly below the fruit’s yield limit, ensuring no mechanical damage occurred, with an average enveloping time of approximately 1.30 s. The experimental data showed strong agreement with the simulation, with a mean absolute percentage error (MAPE) of 5.98% for contact force and 5.40% for enveloping time. These results confirm that the proposed end-effector successfully achieves high adaptability and reliability in non-destructive harvesting, offering a valuable reference for agricultural robotics. Full article

To address the challenging requirements of citrus detection in complex orchard environments, this paper proposes ACDNet (Adaptive Citrus Detection Network), a novel deep learning framework specifically designed for automated citrus harvesting. The proposed method introduces three key innovations: (1) Citrus-Adaptive Feature Extraction (CAFE) module that combines fruit-aware partial convolution with illumination-adaptive attention mechanisms to enhance feature representation with improved efficiency; (2) Dynamic Multi-Scale Sampling (DMS) operator that adaptively focuses sampling points on fruit regions while suppressing background interference through content-aware offset generation; and (3) Fruit-Shape Aware IoU (FSA-IoU) loss function that incorporates citrus morphological priors and occlusion patterns to improve localization accuracy. Extensive experiments on our newly constructed CitrusSet dataset, which comprises 2887 images capturing diverse lighting conditions, occlusion levels, and fruit overlapping scenarios, demonstrate that ACDNet achieves superior performance with mAP@0.5 of 97.5%, precision of 92.1%, and recall of 92.8%, while maintaining real-time inference at 55.6 FPS. Compared to the baseline YOLOv8n model, ACDNet achieves improvements of 1.7%, 3.4%, and 3.6% in mAP@0.5, precision, and recall, respectively, while reducing model parameters by 11% (to 2.67 M) and computational cost by 20% (to 6.5 G FLOPs), making it highly suitable for deployment in resource-constrained robotic harvesting systems. However, the current study is primarily validated on citrus fruits, and future work will focus on extending ACDNet to other spherical fruits and exploring its generalization under extreme weather conditions. Full article

Mango is the second most widely cultivated tropical fruit in the world. Its harvesting mainly relies on manual labor. During the harvest season, the hot weather leads to low working efficiency and high labor costs. Current research on automatic mango harvesting mainly focuses on locating the fruit stem harvesting point, followed by stem clamping and cutting. However, these methods are less effective when the stem is occluded. To address these issues, this study first acquires images of four mango varieties in a mixed cultivation orchard and builds a dataset. Mango detection and occlusion-state classification models are then established based on YOLOv11m and YOLOv8l-cls, respectively. The detection model achieves an AP_0.5–0.95 (average precision at IoU = 0.50:0.05:0.95) of 90.21%, and the accuracy of the classification model is 96.9%. Second, based on the mango growth characteristics, detected mango bounding boxes and binocular vision, we propose a spatial localization method for the mango grasping point. Building on this, a mango-grasping and stem-cutting end-effector is designed. Finally, a mango harvesting robot system is developed, and verification experiments are carried out. The experimental results show that the harvesting method and procedure are well-suited for situations where the fruit stem is occluded, as well as for fruits with no occlusion or partial occlusion. The mango grasping success rate reaches 96.74%, the stem cutting success rate is 91.30%, and the fruit injury rate is less than 5%. The average image processing time is 119.4 ms. The results prove the feasibility of the proposed methods. Full article

To address issues such as slow response speed and low detection accuracy in fallen walnut picking robots in complex orchard environments, this paper proposes a detection and path planning method that integrates star-shaped lightweight convolution with NDT-RRT. The method includes the improved lightweight detection model YOLO-FW and an efficient path planning algorithm NDT-RRT. YOLO-FW enhances feature extraction by integrating star-shaped convolution (Star Blocks) and the C3K2 module in the backbone network, while the introduction of a multi-level scale pyramid structure (CA_HSFPN) in the neck network improves multi-scale feature fusion. Additionally, the loss function is replaced with the PIoU loss, which incorporates the concept of Inner-IoU, thus improving regression accuracy while maintaining the model’s lightweight nature. The NDT-RRT path planning algorithm builds upon the RRT algorithm by employing node rejection strategies, dynamic step-size adjustment, and target-bias sampling, which reduces planning time while maintaining path quality. Experiments show that, compared to the baseline model, the YOLO-FW model achieves precision, recall, and mAP@0.5 of 90.6%, 90.4%, and 95.7%, respectively, with a volume of only 3.62 MB and a 30.65% reduction in the number of parameters. The NDT-RRT algorithm reduces search time by 87.71% under conditions of relatively optimal paths. Furthermore, a detection and planning system was developed based on the PySide6 framework on an NVIDIA Jetson Xavier NX embedded device. On-site testing demonstrated that the system exhibits good robustness, high precision, and real-time performance in real orchard environments, providing an effective technological reference for the intelligent operation of fallen walnut picking robots. Full article

Addressing the issue of fruit recognition and localization failures in harvesting robots due to severe occlusion by branches and leaves in complex orchard environments, this paper proposes an occlusion avoidance method that combines a lightweight YOLOv8n model, developed by Ultralytics in the United States, with active perception. Firstly, to meet the stringent real-time requirements of the active perception system, a lightweight YOLOv8n model was developed. This model reduces computational redundancy by incorporating the C2f-FasterBlock module and enhances key feature representation by integrating the SE attention mechanism, significantly improving inference speed while maintaining high detection accuracy. Secondly, an end-to-end active perception model based on ResNet50 and multi-modal fusion was designed. This model can intelligently predict the optimal movement direction for the robotic arm based on the current observation image, actively avoiding occlusions to obtain a more complete field of view. The model was trained using a matrix dataset constructed through the robot’s dynamic exploration in real-world scenarios, achieving a direct mapping from visual perception to motion planning. Experimental results demonstrate that the proposed lightweight YOLOv8n model achieves a mAP of 0.885 in apple detection tasks, a frame rate of 83 FPS, a parameter count reduced to 1,983,068, and a model weight file size reduced to 4.3 MB, significantly outperforming the baseline model. In active perception experiments, the proposed method effectively guided the robotic arm to quickly find observation positions with minimal occlusion, substantially improving the success rate of target recognition and the overall operational efficiency of the system. The current research outcomes provide preliminary technical validation and a feasible exploratory pathway for developing agricultural harvesting robot systems suitable for real-world complex environments. It should be noted that the validation of this study was primarily conducted in controlled environments. Subsequent work still requires large-scale testing in diverse real-world orchard scenarios, as well as further system optimization and performance evaluation in more realistic application settings, which include natural lighting variations, complex weather conditions, and actual occlusion patterns. Full article

To address the issue of low depth estimation accuracy in complex goji berry orchards, this paper proposes a method for identifying and locating goji berries that combines the YOLO-VitBiS object detection network with stereo vision technology. Based on the YOLO11n backbone network, the C3K2 module in the backbone is first improved using the AdditiveBlock module to enhance its detail-capturing capability in complex environments. The AdditiveBlock introduces lightweight long-range interactions via residual additive operations, thereby strengthening global context modeling without significantly increasing computation. Subsequently, a weighted bidirectional feature pyramid network is introduced into the Neck to enable more flexible and efficient feature fusion. Finally, a lightweight shared detail-enhanced detection head is proposed to further reduce the network’s computational complexity and parameter count. The enhanced model is integrated with binocular stereo vision technology, employing the CREStereo depth estimation algorithm for disparity calculation during binocular stereo matching to derive the three-dimensional spatial coordinates of the goji berry target. This approach enables efficient and precise positioning. Experimental results demonstrate that the YOLO-VitBiS model achieves a detection accuracy of $96.6\%$, with a model size of $4.3\mathrm{MB}$ and only $1.856\mathrm{M}$ parameters. Compared to the traditional SGBM method and other deep learning approaches such as UniMatch, the CREStereo algorithm generates superior depth maps under complex conditions. Within a distance range of 400 mm to 1000 mm, the average relative error between the estimated and actual depth measurements is $2.42\%$, meeting the detection and ranging accuracy requirements for field operations and providing reliable recognition and localization support for subsequent goji berry harvesting robots. Full article

Residues in the grain tank of seed-production wheat harvesters often cause varietal admixture, challenging seed purity maintenance above 99%. To address this, an intelligent cleaning system was developed for automatic residue recognition and removal. The system utilizes an RGB-D camera and an embedded AI unit paired with an improved lightweight object detection model. This model, enhanced for feature extraction and compressed via LAMP, was successfully deployed on a Jetson Nano, achieving 92.5% detection accuracy and 13.37 FPS for real-time 3D localization of impurities. A D–H kinematic model was established for the 4-DOF cleaning manipulator. By integrating the PSO and FWA models, the motion trajectory was optimized for time-optimality, reducing movement time from 9 s to 5.96 s. Furthermore, a gas–solid coupled simulation verified the separation capability of the cyclone-type dust extraction unit, which prevents motor damage and centralizes residue collection. Field tests confirmed the system’s comprehensive functionality, achieving an average cleaning rate of 92.6%. The proposed system successfully enables autonomous residue cleanup, effectively minimizing the risk of variety mixing and significantly improving the harvest purity and operational reliability of seed-production wheat. It presents a novel technological path for efficient seed production under the paradigm of smart agriculture. Full article

Exploring the design of beneficial nonlinear restoring force structures has become a highly popular topic due to their extensive applications in energy harvesting, actuation, energy absorption, robotics, etc. However, the current literature lacks a systematic review and classification that addresses the design, modeling, and parameter identification of nonlinear restoring forces. Thus, the present paper provides a thorough examination of the latest advancements in the design of nonlinear restoring forces, as well as modeling and parameter identification in contemporary beneficial nonlinear designs. The seven design methodologies, namely magnetic coupling, oblique spring linkages, static or dynamic preloading, metamaterials, bio-inspired, MEMS (Micro-Electromechanical Systems) manufacturing, and dry friction applied approaches, are classified. The polynomial, hysteretic, and piecewise linear models are summarized for nonlinear restoring force characterization. The system parameter identification methods covering restoring force surface, Hilbert transform, time-frequency analysis, nonlinear subspace identification, unscented Kalman filter, optimization algorithms, physics-informed neural networks, and data-driven sparse regression are reviewed. Moreover, possible enhancement strategies for nonlinear system identification of nonlinear restoring forces are presented. Finally, broader implications and future directions for the design, characterization, and identification of nonlinear restoring forces are discussed. Full article

To develop a high-performance detection system for automated harvesting on resource-limited edge devices, we introduce FSA-DETR-P, a lightweight detection framework that addresses challenges such as illumination inconsistency, occlusion, and scale variation in complex orchard environments. Unlike traditional computationally intensive architectures, this model optimizes real-time detection transformers by integrating an efficient backbone for fast feature extraction, a simplified aggregation structure to minimize complexity, and an adaptive mechanism for multi-scale feature fusion. The optimized backbone improves early-stage texture extraction while reducing computational demands. The streamlined aggregation design enhances multi-level interactions without losing spatial detail, and the adaptive fusion module strengthens the detection of small, partially occluded, or ambiguous fruits. We created a domain-specific pomegranate dataset, expanded to 13,840 images with a rigorous 8:1:1 split for training, validation, and testing. The results show that the pruned and optimized model achieves a Mean Average Precision (mAP50) of 0.928 and mAP50–95 of 0.632 with reduced parameters (13.73 M) and lower computational costs (34.6 GFLOPs). It operates at 24.6 FPS on an NVIDIA Jetson Orin Nano, indicating a strong balance between accuracy and deployability, making it well-suited for orchard monitoring and robotic harvesting in real-world applications. Full article

The agricultural system faces the formidable challenge of efficiently harvesting strawberries, a labor-intensive process that has long relied on manual labor. The advent of autonomous harvesting robot systems offers a transformative solution, but their success hinges on the accuracy and efficiency of strawberry detection. In this paper, we present DPViT-YOLOV8, a novel approach that leverages advancements in computer vision and deep learning to significantly enhance strawberry detection. DPViT-YOLOV8 integrates the EfficientViT backbone for multi-scale linear attention, the Dynamic Head mechanism for unified object detection heads with attention, and the proposed C2f_Faster module for enhanced computational efficiency into the YOLOV8 architecture. We meticulously curate and annotate a diverse dataset of strawberry images on a farm. A rigorous evaluation demonstrates that DPViT-YOLOV8 outperforms baseline models, achieving superior Mean Average Precision (mAP), precision, and recall. Additionally, an ablation study highlights the individual contributions of each enhancement. Qualitative results showcase the model’s proficiency in locating ripe strawberries in real-world agricultural settings. Notably, DPViT-YOLOV8 maintains computational efficiency, reducing inference time and FLOPS compared to the baseline YOLOV8. Our research bridges the gap between computer vision and agriculture systems, offering a powerful tool to accelerate the adoption of autonomous strawberry harvesting, reduce labor costs, and ensure the sustainability of strawberry farming. Full article

To meet the growing demand for automated blueberry harvesting in smart agriculture, this study proposes an improved lightweight detection network, termed M-YOLOv11n, for fast and accurate blueberry maturity detection in complex natural environments. The proposed model enhances feature representation through an improved lightweight multi-scale design, enabling more effective extraction of fruit features under complex orchard conditions. In addition, attention-based feature refinement is incorporated to emphasize discriminative ripeness-related cues while suppressing background interference. These design choices improve robustness to scale variation and occlusion, addressing the limitations of conventional lightweight detectors in detecting small and partially occluded fruits. By incorporating MsBlock and the attention mechanism, M-YOLOv11n achieves improved detection accuracy without significantly increasing computational cost. Experimental results demonstrate that the proposed model attains 97.0% mAP50 on the validation set and maintains robust performance under challenging conditions such as occlusion and varying illumination, achieving 96.5% mAP50. With an inference speed of 176.6 FPS, the model satisfies both accuracy and real-time requirements for blueberry maturity detection. Compared with YOLOv11n, M-YOLOv11n increases the parameter count only marginally from 2.60 M to 2.61 M, while maintaining high inference efficiency. These results indicate that the proposed method is suitable for real-time deployment on embedded vision systems in smart agricultural harvesting robots and supports early yield estimation in complex field environments. Full article

Self-powered sensors, leveraging their integrated energy harvesting–signal sensing capability, effectively overcome the bottlenecks of traditional sensors, including reliance on external power resources, high maintenance costs, and challenges in large-scale distributed deployment. As a result, they have become a major research focus in fields such as flexible electronics, smart healthcare, and human–machine interaction. This paper reviews the core technical paths of six major types of self-powered sensors developed in recent years, with particular emphasis on the working principles and innovative material applications associated with frictional charge transfer and electrostatic induction, pyroelectric polarization dynamics, hydrovoltaic interfacial streaming potentials, piezoelectric constitutive behavior, battery integration mechanism, and photovoltaic effect. By comparing representative achievements in fields closely related to self-powered sensors, it summarizes breakthroughs in key performance indicators such as sensitivity, detection range, response speed, cyclic stability, self-powering methods, and energy conversion efficiency. The applications discussed herein mainly cover several critical domains, including wearable medical and health monitoring systems, intelligent robotics and human–machine interaction, biomedical and implantable devices, as well as safety and ecological supervision. Finally, the current challenges facing self-powered sensors are outlined and future development directions are proposed, providing a reference for the technological iteration and industrial application of self-powered sensors. Full article

Winter jujube harvesting is traditionally labor-intensive, yet declining labor availability and rising costs necessitate robotic automation to maintain agricultural competitiveness. Path planning for robotic arms in orchards faces challenges due to the unstructured, dynamic environment containing densely packed fruits and branches. To overcome the limitations of existing robotic path planning methods, this research proposes BMGA-RRT Connect (BVH-based Multilevel-step Gradient-descent Adaptive RRT), a novel algorithm integrating adaptive multilevel step-sizing, hierarchical Bounding Volume Hierarchy (BVH)-based collision detection, and gradient-descent path smoothing. Initially, an adaptive step-size strategy dynamically adjusts node expansions, optimizing efficiency and avoiding collisions; subsequently, a hierarchical BVH improves collision-detection speed, significantly reducing computational time; finally, gradient-descent smoothing enhances trajectory continuity and path quality. Comprehensive 2D and 3D simulation experiments, dynamic obstacle validations, and real-world winter jujube harvesting trials were conducted to assess algorithm performance. Results showed that BMGA-RRT Connect significantly reduced average computation time to 2.23 s (2D) and 7.12 s (3D), outperforming traditional algorithms in path quality, stability, and robustness. Specifically, BMGA-RRT Connect achieved 100% path planning success and 90% execution success in robotic harvesting tests. These findings demonstrate that BMGA-RRT Connect provides an efficient, stable, and reliable solution for robotic harvesting in complex, unstructured agricultural settings, offering substantial promise for practical deployment in precision agriculture. Full article

Background: Advances in breast reconstruction have transformed the recovery pathway for women undergoing mastectomy. What was once viewed mainly as a cosmetic option is now recognized as part of modern oncologic care, restoring not only body image but also confidence and quality of life. Yet, surgeons still face the same central dilemma: choosing between implant-based (IBR) and autologous reconstruction (ABR), particularly when postmastectomy radiotherapy (PMRT) is planned. Methods: We reviewed major studies published between 2014 and 2024, combining evidence from observational cohorts and recent meta-analyses that together report on more than 60,000 reconstructed breasts. Outcomes of interest included surgical complications, reconstructive failure, BREAST-Q patient-reported domains, and the impact of PMRT on both techniques. Data were interpreted in light of contemporary reconstructive innovations such as prepectoral implants, acellular dermal matrices, and robotic or sensory-nerve–enhanced autologous procedures. Results: Autologous reconstruction generally provided higher satisfaction and better psychosocial and sexual well-being, particularly in patients who received PMRT. Implant-based reconstruction offered faster recovery and shorter hospitalization but was more vulnerable to capsular contracture and reconstructive loss after irradiation. Across all eligible cohorts, reconstruction—immediate or delayed—did not increase local recurrence or compromise overall survival when adjuvant therapy was delivered without delay. Conclusions: Both IBR and ABR are oncologically safe and contribute meaningfully to recovery after mastectomy. Future progress will depend on combining precise surgical execution with new technologies—prepectoral implant positioning, robotic flap harvest, and sensory nerve coaptation—to achieve durable, natural, and patient-centered reconstruction. Full article

Background and Objectives: Robot-assisted procedures represent a significant advancement in minimally invasive liver resection techniques. Nonetheless, the introduction of a novel surgical technique in a new environment necessitates meticulous planning and a gradual, stepwise approach. This study describes the adoption of a robotic surgical platform for liver resection at a high-volume tertiary care center. Materials and Methods: We retrospectively analyzed data that had been prospectively collected from fifty robot-assisted liver resections. Descriptive statistics, including frequencies, percentages, means/medians, and standard deviations, were employed for description and summary. Results: The median operative duration was 166 min (range: 85–400 min), with an average intraoperative blood loss of 200 mL (range: 50–1000 milliliters). Intraoperative or postoperative blood transfusion was required in 8% of patients. Conversion to open resection was necessary in one patient (2%). The mean duration of hospitalization was 5 days (range: 3–20 days), with a 30-day readmission rate of 6% and no mortality within 90 days. Postoperative complications classified as Clavien-Dindo grade 3 or higher were observed in five patients (10%). The mean tumor size varied according to pathology: 58.5 mm (range: 30–120 mm) in the hepatocellular carcinoma group; 27.4 mm (range: 10–32 mm) in the secondary malignancy group; and 42.6 mm (range: 24–60 mm) in the intrahepatic cholangiocarcinoma group. The median number of lymph nodes harvested during lymphadenectomy (IHHCA/GBCA) was 5.4, ranging from 1 to 11. The R0 resection rate for malignant tumors was 88.2% (of 30/34). Conclusions: This study validates the safe integration of robot-assisted surgery into liver disease treatment, supported by our initial experience. Despite its technical advantages, robotic-assisted liver surgery remains complex and demanding. Structured robotic training within established programs, meticulous patient selection, and a stepwise implementation approach are critical during the early phases to optimize the outcomes. Full article