Search Results (79)

To address suboptimal throughput performance in conventional intelligent apple harvesting systems predominantly employing single manipulators, a dual-arm harvesting robot prototype was engineered. Leveraging the AUBO-i5 manipulator framework and kinematic characteristics, a coordinated workspace arrangement was established. Subsequently, the dual-manipulator harvesting platform was fabricated. A dynamic task allocation methodology and intelligent fruit sequencing approach were formulated, grounded in U-tube optimization principles. This framework achieved parallel operation ratios between 82.1% and 99%, with combined trajectory lengths spanning 9.24–11.90 m. Building upon established apple harvesting knowledge, a sequencing strategy incorporating dynamic manipulator zoning was developed. Validation was conducted through V-REP kinematic simulations where end-effector poses were continuously tracked, confirming zero limb interference during coordinated motion. Field assessments yielded parallel operation rates of 85.7–93.3%, total harvest durations of 17.8–22.3 s, and inter-manipulator path differentials of 267–541 mm. Throughout testing, collision-free operation was maintained while successfully harvesting all target fruits according to planned sequences. These outcomes validate the efficacy of U-tube-based dynamic zoning and sequencing methodologies for dual-manipulator fruit harvesting in intelligent orchard applications. Full article

Berries are popular by consumers for improving vision, lowering blood sugar, improving circulation, and cardiovascular protection. They are usually small, thin-skinned, and fragile, with inconsistent ripening times. Harvesting robots are able to accurately determine the ripeness of fruits, avoiding pulp breakage and nutrient loss caused by manual squeezing. This work reviews the development and application of berry harvesting robots with market prospects in recent years. Next, this paper discusses the key technologies of berry picking robots, including fruit detection and localization technology, motion planning technology, and end-effector and harvesting mechanism. It also discusses the challenges currently faced in the development of berry harvesting robots, including external factors such as unstructured working environments and internal technical difficulties such as robot design and control. To address these challenges, future berry picking robots should focus on developing weak supervision recognition models based on deep learning, high-speed collision-free multi-arm collaborative harvesting technology, and high fault-tolerant harvesting technology to improve picking efficiency and quality, reduce fruit damage, and promote the automation and intelligence of the berry harvesting. Full article

Accurate target recognition and localization remain significant challenges for robotic fruit harvesting in unstructured orchard environments characterized by branch occlusion and leaf clutter. To address the difficulty in identifying and locating apples under such visually complex conditions, this paper proposes an improved YOLOv5-based visual recognition algorithm incorporating an efficient channel attention (ECA) module. The ECA module is strategically integrated into specific C3 layers (C3-3, C3-6, C3-9) of the YOLOv5 network architecture to enhance feature representation for occluded targets. During operation, the system simultaneously acquires apple pose information and achieves precise spatial localization through coordinate transformation matrices. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed system. The custom-designed six-degree-of-freedom (6-DOF) robotic arm exhibits a wide operational range with a maximum working angle of 120°. The ECA-enhanced YOLOv5 model achieves a confidence level of 90% and an impressive in-range apple recognition rate of 98%, representing a 2.5% improvement in the mean Average Precision (mAP) compared to the baseline YOLOv5s algorithm. The end-effector positioning error is consistently controlled within 1.5 mm. The motion planning success rate reaches 92%, with the picking completed within 23 s per apple. This work provides a novel and effective vision recognition solution for future development of harvesting robots. Full article

As a globally significant oil crop, precise ripeness identification of palm fruits directly impacts harvesting efficiency and oil quality. However, the progress and application of identifying the ripeness of palm fruits have been impeded by the computational limitations of agricultural hardware and the insufficient robustness in accurately identifying palm fruits in complex on-tree environments. To address these challenges, this paper proposes an efficient recognition network tailored for complex canopy-level palm fruit ripeness assessment. Progressive combination optimization enhances the baseline network, which utilizes the YOLOv8 architecture. This study has individually enhanced the backbone network, neck, detection head, and loss function. Specifically, the backbone integrates the StarNet framework, while the detection head incorporates the lightweight LSCD structure. To enhance recognition precision, StarNet-derived Star Blocks replace standard bottleneck modules in the neck, forming optimized C2F-Star components, complemented by DIoU loss implementation to accelerate convergence. The resultant on-tree model for recognizing palm fruit ripeness achieves substantial efficiency gains. While simultaneously elevating detection precision to 76.0% mAP@0.5, our method’s GFLOPs, parameters, and model size are only 4.5 G, 1.37 M, and 2.85 MB, which are 56.0%, 46.0%, and 48.0% of the original model. The effectiveness of the model in recognizing palm fruit ripeness in complex environments, such as uneven lighting, motion blur, and occlusion, validates its robustness. Full article

Steady-state visual evoked potential (SSVEP) paradigms are widely used in brain–computer interface (BCI) systems due to their reliability and fast response. However, traditional static stimuli may reduce user comfort and engagement during prolonged use. This study proposes a dynamic stimulation paradigm combining periodic motion trajectories with speed control. Using four frequencies (6, 8.57, 10, 12 Hz) and three waveform patterns (sinusoidal, square, sawtooth), speed was modulated at 1/5, 1/10, and 1/20 of each frequency’s base rate. An offline experiment with 17 subjects showed that the low-speed sinusoidal and sawtooth trajectories matched the static accuracy (85.84% and 83.82%) while reducing cognitive workload by 22%. An online experiment with 12 subjects participating in a fruit-slicing game confirmed its practicality, achieving recognition accuracies above 82% and a System Usability Scale score of 75.96. These results indicate that coordinated trajectory and speed modulation preserves SSVEP signal quality and enhances user experience, offering a promising approach for fatigue-resistant, user-friendly BCI application. Full article

This research addresses the enhancement of operational efficiency in apple-picking robots through the design of a bimanual spatial configuration enabling obstacle avoidance in contemporary orchard environments. A parallel coordinated harvesting paradigm for dual-arm systems was introduced, leading to the construction and validation of a six-degree-of-freedom bimanual apple-harvesting robot. Leveraging the kinematic architecture of the AUBO-i5 manipulator, three spatial layout configurations for dual-arm systems were evaluated, culminating in the adoption of a “workspace-overlapping Type B” arrangement. A functional prototype of the bimanual apple-harvesting system was subsequently fabricated. The study further involved developing control architectures for two end-effector types: a compliant gripper and a vacuum-based suction mechanism, with corresponding operational protocols established. A networked communication framework for parallel arm coordination was implemented via Ethernet switching technology, enabling both independent and synchronized bimanual operation. Additionally, an intersystem communication protocol was formulated to integrate the robotic vision system with the dual-arm control architecture, establishing a modular parallel execution model between visual perception and motion control modules. A coordinated bimanual harvesting strategy was formulated, incorporating real-time trajectory and pose monitoring of the manipulators. Kinematic simulations were executed to validate the feasibility of this strategy. Field evaluations in modern Red Fuji apple orchards assessed multidimensional harvesting performance, revealing 85.6% and 80% success rates for the suction and gripper-based arms, respectively. Single-fruit retrieval averaged 7.5 s per arm, yielding an overall system efficiency of 3.75 s per fruit. These findings advance the technological foundation for intelligent apple-harvesting systems, offering methodologies for the evolution of precision agronomic automation. Full article

Apple harvesting faces increasing challenges owing to rising labor costs and the limited seasonal workforce availability, highlighting the need for robotic harvesting solutions in precision agriculture. This study presents a 6-DOF robotic arm system designed for harvesting in dwarf hedge-planted orchards, featuring a lightweight perception module, a task-adaptive motion planner, and an adaptive soft gripper. A lightweight approach was introduced by integrating the Faster module within the C2f module of the You Only Look Once (YOLO) v8n architecture to optimize the real-time apple detection efficiency. For motion planning, a Dynamic Temperature Simplified Transition Adaptive Cost Bidirectional Transition-Based Rapidly Exploring Random Tree (DSA-BiTRRT) algorithm was developed, demonstrating significant improvements in the path planning performance. The adaptive soft gripper was evaluated for its detachment and load-bearing capacities. Field experiments revealed that the direct-pull method at 150 mN·m torque outperformed the rotation-pull method at both 100 mN·m and 150 mN·m. A custom control system integrating all components was validated in partially controlled orchards, where obstacle clearance and thinning were conducted to ensure operation safety. Tests conducted on 80 apples showed a 52.5% detachment success rate and a 47.5% overall harvesting success rate, with average detachment and full-cycle times of 7.7 s and 15.3 s per apple, respectively. These results highlight the system’s potential for advancing robotic fruit harvesting and contribute to the ongoing development of autonomous agricultural technologies. Full article

Planting tomatoes in enclosed facilities requires manual pollination assistance. Chemically-assisted pollination poses environmental pollution and food safety hazards. Contact vibration pollination is inefficient, ineffective, and prone to plant damage. This study developed a non-contact tomato pollination device based on pulse airflow, and conducted an experimental investigation on it. Firstly, a non-contact tomato pollination device based on pulse airflow was designed, based on the reciprocating motion of tomato flowers under the action of pulse airflow. Subsequently, this study took the coverage rate of pollen on the stigma as an indicator, and the optimal pulse airflow parameters were determined, which were a velocity of 1.22 m·s⁻¹, airflow angle of −19.69°, and pulse frequency of 25.64 Hz. Finally, comparative experiments were conducted between the pollination effect of tomatoes based on pulse airflow and other assisted pollination methods. The results show that tomato flowers produce a composite reciprocating vibration under the coupling effect of the inflorescence elastic force and the pulse airflow force, and the coverage of pollen on the stigma is 11.2% higher than assisted pollination using stable airflow. The use of a pulse airflow pollination method can increase the fruit setting rate by 13.21%, increase the weight per fruit by 11.46%, and increase the weight of fruits per bunch by 33.33%. Compared with chemically-assisted fruit setting, no chemical agents were used to ensure a fruit setting rate similar to chemical methods, and the number of seeds per fruit increased by 74.8. Compared with vibration pollination, it eliminated plant damage and increased the fruit setting rate by 4.45%, and improved efficiency by 18.6%. The results indicated that the pollination method based on pulse airflow is environmentally friendly, high-quality, and efficient. This study breaks through the theoretical and parameter limitations of traditional airflow pollination devices, and provides a theoretical base for the development of clean pollination equipment in facility agriculture. Full article

With the rapid development of agricultural intelligence in recent years, automatic fruit detection and counting technologies have become increasingly significant for optimizing orchard management and advancing precision agriculture. However, existing deep learning-based models are primarily designed to process static and single-frame images, thereby failing to meet the large-scale detection and counting demands in the dynamically changing scenes of modern orchards. To address these challenges, this paper proposes a multi-object fruit tracking and counting method, which integrates an improved YOLO-based object detection algorithm with a dynamically optimized Kalman filter. By optimizing the network structure, the improved YOLO detection model provides high-quality detection results for subsequent tracking tasks. Then a modified Kalman filter with a variable forgetting factor is integrated to dynamically adjust the weighting of historical data, enabling the model to adapt to changes in observation and motion noise. Moreover, fruit targets are associated using a combined strategy based on Intersection over Union (IoU) and Re-Identification (Re-ID) features, improving the accuracy and stability of object matching. Consequently, the continuous tracking and precise counting of fruits in video sequences are achieved. Experimental results with image frames of fruits in video sequence are demonstrated, showing that the proposed method performs robust and continuous tracking (MOTA of 95.0% and HOTA of 82.4%). For fruit counting, the method attains a high coefficient-of-determination of 0.85 and a low root-mean-square error (RMSE) of 1.57, exhibiting high accuracy and stability of fruit detection, tracking and counting in video sequences under complex orchard environments. Full article

Aiming to solve the problem that most existing apple-picking robots operate with a single arm and that the overall efficiency of the machine needs to be further improved, a prototype of a dual-arm picking robot was built, and its picking operation planning method was studied. Firstly, based on the configuration and motion mode of the AUBO-i5 robotic arm, the overlapping dual-arm layout of the workspace was determined. Then, a prototype of a dual-arm apple-picking robot was built, and, based on the designed dual-arm spatial layout, a dual-arm picking operation zoning planning method was proposed. The experimental results showed that in the four simulation experiments, the highest value of the maximum parallel operation proportion of the dual arms was 83%, and the lowest value was 50.6%. The highest value of the maximum operation length of the single arm was 7323 mm, and the lowest value was 5654 mm. The total length of the dual-arm operation path was 12,705 mm, and the lowest value was 8770 mm. Furthermore, a fruit-picking sequence planning method based on dual robotic arm operation was proposed. Fruit traversal simulation verification experiments were conducted. The results showed that there was no conflict between the left and right arms during the motion of the dual robotic arms. Finally, the proposed dual-arm robot operation zoning and picking sequence planning method was validated in the apple experimental station. The results showed that the proportion of dual-arm parallel operations was the lowest at 50.7% and the highest at 72.4%. The total length of the dual-arm operation path was the highest at 8604 mm and the lowest at 6511 mm. Full article

Fruit and vegetable picking robots are considered an important way to promote agricultural modernization due to their high efficiency, precision, and intelligence. However, most of the existing research has sporadically involved single application areas, such as object detection, classification, and path planning, and has not yet comprehensively sorted out the core applications of deep learning technology in fruit and vegetable picking robots, the current technological bottlenecks faced, and future development directions. This review summarizes the key technologies and applications of deep learning in the visual perception and target recognition, path planning and motion control, and intelligent control of end effectors of fruit and vegetable picking robots. It focuses on the optimization strategies and common problems related to deep learning and explores the challenges and development trends of deep learning in improving the perception accuracy, multi-sensor collaboration, multimodal data fusion, adaptive control, and human–computer interaction of fruit and vegetable picking robots in the future. The aim is to provide theoretical support and practical guidance for the practical application of deep learning technology in fruit and vegetable picking robots. Full article

In order to explore the optimal vibration parameters for the selective harvesting of coffee fruits, a high-velocity dynamic photography monitoring system was developed to analyze the vibration-assisted harvesting process. This system identified the optimal vibration position on coffee branches and facilitated theoretical energy transfer analysis, obtaining a mathematical formula for calculating the total kinetic energy of coffee branches. A single-factor experiment was conducted with the vibration position as the experimental factor and the total kinetic energy of coffee branches as the response variable. The results showed that the total kinetic energy of the branches was the highest at Vibration Position 2 (the position between the third and the fourth Y-shaped bud tips on the branch). Therefore, Vibration Position 2 was determined as the optimal vibration position. Further analysis established a mathematical model linking coffee cherry motion parameters to theoretical detachment force. A factorial experiment was conducted with vibration frequency and amplitude as test factors, using detachment rates of green, semi-ripe, and ripe cherries as indicators. The results showed that at 55 Hz and 10.10 mm amplitude, the detachment rate of ripe cherries was highest (83.33%), while green and semi-ripe cherries detached at 16.67% and 33.33%, respectively. A field validation experiment, with Vibration Position 2, 55 Hz frequency, 10.10 mm amplitude, and 1 s vibration duration, yielded actual detachment rates of 15.86%, 35.17%, and 89.50% for green, semi-ripe, and ripe cherries, respectively. The error margins compared with the theoretical values were all below 10%. These results confirm the feasibility of optimizing vibration harvesting parameters through high-velocity photography dynamic analysis. Full article

To address the limitations of single-chamber soft grippers, such as constant curvature, insufficient motion flexibility, and restricted fingertip movement, this study proposes a soft gripper inspired by the structure of the human hand. The designed soft gripper consists of three fingers, each comprising three soft joints and four phalanges. The air chambers in each joint are independently actuated, enabling flexible grasping by adjusting the joint air pressure. The constraint layer is composed of a composite material with a mass ratio of 5:1:0.75 of PDMS base, PDMS curing agent, and PTFE, which enhances the overall finger stiffness and fingertip load capacity. A nonlinear mathematical model is established to describe the relationship between the joint bending angle and actuation pressure based on the constant curvature assumption. Additionally, the kinematic model of the finger is developed using the D–H parameter method. Finite element simulations using ABAQUS analyze the effects of different joint pressures and phalange lengths on the grasping range, as well as the fingertip force under varying actuation pressures. Bending performance and fingertip force tests were conducted on the soft finger actuator, with the maximum fingertip force reaching 2.21 N. The experimental results show good agreement with theoretical and simulation results. Grasping experiments with variously sized fruits and everyday objects demonstrate that, compared to traditional single-chamber soft grippers, the proposed humanoid joint-inspired soft gripper significantly expands the grasping range and improves grasping force by four times, achieving a maximum grasp weight of 0.92 kg. These findings validate its superior grasping performance and potential for practical applications. Full article

Autonomous navigation is key to improving efficiency and addressing labor shortages in the fruit industry. Semi-structured orchards, with straight tree rows, dense weeds, thick canopies, and varying light conditions, pose challenges for tree identification and navigation line extraction. Traditional 3D lidars suffer from a narrow vertical FoV, sparse point clouds, and high costs. Furthermore, most lidar-based tree-row-detection algorithms struggle to extract high-quality navigation lines in scenarios with thin trunks and dense foliage occlusion. To address these challenges, we developed a 3D perception system using a servo motor to control the rolling motion of a 2D lidar, constructing 3D point clouds with a wide vertical FoV and high resolution. In addition, a method for trunk feature point extraction and tree row line detection for autonomous navigation has been proposed, based on trunk geometric features and RANSAC. Outdoor tests demonstrate the system’s effectiveness. At speeds of 0.2 m/s and 0.5 m/s, the average distance errors are 0.023 m and 0.016 m, respectively, while the average angular errors are 0.272° and 0.146°. This low-cost solution overcomes traditional lidar-based navigation method limitations, making it promising for autonomous navigation in semi-structured orchards. Full article

Accurately detecting apple fruit can crucially assist in estimating the fruit yield in apple orchards in complex scenarios. In such environments, the factors of density, leaf occlusion, and fruit overlap can affect the detection and counting accuracy. This paper proposes an improved YOLOv7-Tiny-PDE network model based on the YOLOv7-Tiny model to detect and count apples from data collected by drones, considering various occlusion and lighting conditions. First, within the backbone network, we replaced the simplified efficient layer aggregation network (ELAN) with partial convolution (PConv), reducing the network parameters and computational redundancy while maintaining the detection accuracy. Second, in the neck network, we used a dynamic detection head to replace the original detection head, effectively suppressing the background interference and capturing the background information more comprehensively, thus enhancing the detection accuracy for occluded targets and improving the fruit feature extraction. To further optimize the model, we replaced the boundary box loss function from CIOU to EIOU. For fruit counting across video frames in complex occlusion scenes, we integrated the improved model with the DeepSort tracking algorithm based on Kalman filtering and motion trajectory prediction with a cascading matching algorithm. According to experimental results, compared with the baseline YOLOv7-Tiny, the improved model reduced the total parameters by 22.2% and computation complexity by 18.3%. Additionally, in data testing, the p-value improved by 0.5%; the R-value rose by 2.7%; the mAP and F1 scores rose by 4% and 1.7%, respectively; and the MOTA value improved by 2%. The improved model is more lightweight and can preserve a high detection accuracy well, and hence, it can be applied to detection and counting tasks in complex orchards and provides a new solution for fruit yield estimation using lightweight devices. Full article