Search Results (126)

The vertical-wheel PDC bit adds a rotatable wheel cutter to conventional fixed PDC blades, creating a dual-structure cooperative rock-breaking system. A synergistic design theory is established through the following consecutive steps. Firstly, a fully coupled digital model of the wheel cutters, fixed blades and rock was built; load-calculation methods for each cutter type were derived, enabling the WOB distribution to be predicted by simulation. Secondly, for complex drilling modes, such as mixed-mode rotary steering, the wheel must be located at the instantaneous resultant force point of the bit to maximize buffering and torque mitigation; the locus of this point was traced while drilling. Thirdly, a proportional relationship between relative cutter exposure and weight on bit share was validated and used to synchronize the cutting trajectories of the two structures. Finally, systematic design criteria for wheel diameter, shaft inclination, normal offset, offset distance, cutter shape and wheel count were formulated. The results provide a theoretical basis and a technical roadmap for high-efficiency, long-life VW-PDC bit design. Full article

Screw-drive in-pipe robots are widely used for inspection and maintenance of pipeline infrastructure because their tilted-wheel locomotion enables continuous traversal of horizontal, vertical, and curved pipes. However, most existing designs rely on passive spring mechanisms to generate wall-contact forces, making traction performance highly sensitive to pipe-diameter variations, friction changes, and manufacturing tolerances. This paper presents an adaptive screw-drive in-pipe robot that integrates adjustable radial geometry, embedded Hall-effect force sensing, and closed-loop gripping-force control. A unified mechanical–geometric model is developed to describe the coupling between actuator displacement, spring compression, wheel-tilt geometry, and pipe-diameter variation. Based on this model, a minimum safe gripping-force condition is derived and used to define a reference force for real-time control. A proportional–derivative controller regulates the gripping force of the front traction module, while a rear stabilizing module ensures axial alignment and suppresses body rotation. Simulation results under realistic diameter transitions and external disturbances demonstrate stable force regulation, preservation of a positive traction margin, and reduced unnecessary actuator effort. The proposed approach enables robust and energy-aware screw-drive locomotion in variable-diameter pipelines. A physical prototype of the robot has been fabricated to support the forthcoming experimental campaign; however, the validation presented in this study is limited to modeling and simulation, with experimental evaluation planned for future work. Full article

This study investigates concrete chute transportation technology for 1000 m ultra-long inclined shafts through design calculations, laboratory tests, and field trials. By optimizing concrete mix proportions, the research resolves segregation issues in ultra-long chute concrete. Field investigations identified alumina ceramic and ultra-high molecular wear-resistant materials as suitable inner lining options. Through grinding wheel wear tests and finite element simulations, both materials demonstrated adequate wear resistance for concrete discharge operations. To meet the requirements for lightweight construction, durability, and rapid replacement, the chute diameter, ceramic sheet thickness, and multi-length sections were optimized. Customized configurations included eight-section fiberglass pipes with alumina ceramic linings and four-section ultra-high molecular wear-resistant chutes. Field tests confirmed both materials satisfied operational needs. Economic analysis concluded that ultra-high molecular wear-resistant materials are recommended as the preferred inner lining for ultra-long concrete chutes. Full article

Aiming at the challenge of simultaneously optimizing ride comfort and wheel grounding performance for mining dump trucks under severe road conditions, this paper proposes a hydro-pneumatic suspension parameter design method based on an improved multi-objective particle swarm optimization (IMOPSO) algorithm. First, a dynamic model of the hydro-pneumatic suspension is established, incorporating the coupled nonlinear characteristics of the valve system and the gas chamber. The accuracy of the model is verified through bench tests. Subsequently, the influence of key parameters, including the damping orifice diameter, check valve seat hole diameter, and initial gas charging height, on the vertical dynamic performance of the vehicle, is systematically analyzed. On this basis, a multi-objective optimization model is constructed with the objective of minimizing the root mean square (RMS) values of both the sprung mass acceleration and the dynamic tire load. To enhance the global search capability and convergence performance of the MOPSO algorithm, adaptive inertia weighting, dynamic flight parameter update, and an enhanced mutation strategy are introduced. Simulation results demonstrate that the optimized suspension achieves significant improvements under various road conditions. On class-C roads, the RMS values of the sprung mass acceleration (SMA) and the dynamic tire load (DTL) are reduced by 37.6% and 15.8%, respectively, while the suspension rattle space (SRS) decreases by 10.2%. Under transient bump roads, the peak-to-peak (Pk-Pk) values of the same two indicators drop by 38.9% and 44.9%, respectively. Furthermore, compared to the NSGA-II algorithm, the proposed method demonstrates superior performance in terms of convergence stability and overall performance balance. These results indicate that the proposed design effectively balances ride comfort, wheel grounding performance, and driving safety. This study provides a theoretical foundation and an engineering-feasible method for the performance balancing and parameter co-design of suspension systems in heavy-duty engineering vehicles. Full article

Forest surveying and inspection face significant challenges due to unstructured environments, variable terrain conditions, and the high costs of manual data collection. Although mobile robotics and artificial intelligence offer promising solutions, reliable autonomous navigation in forest, terrain-aware path planning, and tree parameter estimation remain open challenges. In this paper, we present the results of the AI4FOREST project, which addresses these issues through three main contributions. First, we develop an autonomous mobile robot, integrating SLAM-based navigation, 3D point cloud reconstruction, and a vision-based deep learning architecture to enable tree detection and diameter estimation. This system demonstrates the feasibility of generating a digital twin of forest while operating autonomously. Second, to overcome the limitations of classical navigation approaches in heterogeneous natural terrains, we introduce a machine learning-based surrogate model of wheel–soil interaction, trained on a large synthetic dataset derived from classical terramechanics. Compared to purely geometric planners, the proposed model enables realistic dynamics simulation and improves navigation robustness by accounting for terrain–vehicle interactions. Finally, we investigate the impact of point cloud density on the accuracy of forest parameter estimation, identifying the minimum sampling requirements needed to extract tree diameters and heights. This analysis provides support to balance sensor performance, robot speed, and operational costs. Overall, the AI4FOREST project advances the state of the art in autonomous forest monitoring by jointly addressing SLAM-based mapping, terrain-aware navigation, and tree parameter estimation. Full article

To address the challenges in unstructured orchard environments, including high geometric similarity between fruit trees (with the measured average Euclidean distance difference between point cloud descriptors of adjacent trees being less than 0.5 m), significant dynamic interference (e.g., interference from pedestrians or moving equipment can occur every 5 min), and uneven terrain, this paper proposes an improved mapping algorithm named OSC-LIO (Orchard Scan Context Lidar Inertial Odometry via Smoothing and Mapping). The algorithm designs a dynamic point filtering strategy based on Euclidean clustering and spatiotemporal consistency within a 5-frame sliding window to reduce the interference of dynamic objects in point cloud registration. By integrating local semantic features such as fruit tree trunk diameter and canopy height difference, a two-tier verification mechanism combining “global and local information” is constructed to enhance the distinctiveness and robustness of loop closure detection. Motion compensation is achieved by fusing data from an Inertial Measurement Unit (IMU) and a wheel odometer to correct point cloud distortion. A three-level hierarchical indexing structure—”path partitioning, time window, KD-Tree (K-Dimension Tree)”—is built to reduce the time required for loop closure retrieval and improve the system’s real-time performance. Experimental results show that the improved OSC-LIO system reduces the Absolute Trajectory Error (ATE) by approximately 23.5% compared to the original LIO-SAM (Tightly coupled Lidar Inertial Odometry via Smoothing and Mapping) in a simulated orchard environment, while enabling stable and reliable path planning and autonomous navigation. This study provides a high-precision, lightweight technical solution for autonomous navigation in orchard scenarios. Full article

The hard bottom layer in paddy fields significantly impacts the driving stability, operational quality, and efficiency of agricultural machinery. Continuously improving the precision and efficiency of unmanned, precision operations for paddy field machinery is essential for realizing unmanned smart rice farms. Addressing the unclear influence patterns of hard bottom contours on typical scenarios of agricultural machinery motion and posture changes, this paper employs a rice transplanter chassis equipped with GNSS and AHRS. It proposes methods for acquiring motion state information and hard bottom contour data during agricultural operations, establishing motion state expression models for key points on the machinery antenna, bottom of the wheel, and rear axle center. A correlation analysis method between motion state and hard bottom contour parameters was established, revealing the influence mechanisms of typical hard bottom contours on machinery trajectory deviation, attitude response, and wheel trapping. Results indicate that hard bottom contour height and local roughness exert extremely significant effects on agricultural machinery heading deviation and lateral movement. Heading variation positively correlates with ridge height and negatively with wheel diameter. The constructed mathematical model for heading variation based on hard bottom contour height difference and wheel diameter achieves a coefficient of determination R² of 0.92. The roll attitude variation in agricultural machinery is primarily influenced by the terrain characteristics encountered by rear wheels. A theoretical model was developed for the offset displacement of the antenna position relative to the horizontal plane during roll motion. The accuracy of lateral deviation detection using the posture-corrected rear axle center and bottom of the wheel center improved by 40.7% and 39.0%, respectively, compared to direct measurement using the positioning antenna. During typical vehicle-trapping events, a segmented discrimination function for trapping states is developed when the terrain profile steeply declines within 5 s and roughness increases from 0.008 to 0.012. This method for analyzing how hard bottom terrain contours affect the position and attitude changes in agricultural machinery provides theoretical foundations and technical support for designing wheeled agricultural robots, path-tracking control for unmanned precision operations, and vehicle-trapping early warning systems. It holds significant importance for enhancing the intelligence and operational efficiency of paddy field machinery. Full article

Traditional wheeled ground robots offer high energy efficiency and excellent mobility on flat terrain but are constrained by their fixed structures, making it difficult to overcome obstacles or adapt to complex environments. To address these limitations, this paper presents a morphing wheeled land–air robot (MW-LAR) that integrates ground locomotion and quadrotor flight. By incorporating foldable arms and variable-diameter wheels, the MW-LAR can not only switch between ground and flight modes, but also achieve transitions between wheeled and legged locomotion in the ground mode. The foldable arms support seamless aerial-to-ground transitions and in-flight morphing, while the variable-diameter wheels facilitate efficient obstacle traversal on the ground. Benefiting from the design of foldable arms, two complementary landing approaches, namely direct quadrotor landing and ground-mode landing, are implemented to explore different aerial-to-ground transition modes and to improve landing safety and switching efficiency. Experimental results demonstrate that the MW-LAR achieves stable and energy-efficient performance across multiple locomotion modes and complex environments, highlighting its potential for integrated land–air mobility applications. Full article

Automated precision drilling is essential for aircraft skin manufacturing, yet current robotic systems face dual challenges: chatter-induced inaccuracies in hole quality and limited access to confined spaces such as air inlets. To overcome these limitations, this paper develops a compact drilling robot for drilling large-curvature skins of aircraft air inlets. Targeting the precision drilling requirements for complex-curvature aircraft air inlets, we present the robot’s overall design scheme, detailing each module’s composition to ensure precision drilling. In-depth analysis of the robot’s large-curvature adaptability precisely calculates the wheel assembly dimensions. To ensure high-precision drilling bit entry into guide mechanisms, a flexible drilling spindle mechanism is designed, with calculated and verified elastic ranges. An integrated intelligent control system is developed, combining vision recognition, real-time pose adjustment, and automated drilling workflow planning. Finally, traversability and drilling capabilities are validated using a simplified air inlet model. Test results confirm successful traversal on R200 mm curvature skins and automated drilling of Carbon Fiber-Reinforced Polymer (CFRP)/7075 aluminum stacks with a diameter of Φ4–Φ6 mm, achieving dimensional errors of less than 0.05 mm and normal direction errors of less than 0.65°. Full article

This paper presents an intelligent robotic system for in-pipe inspection that integrates a novel mechanical design, deep learning-based defect detection, and high-fidelity simulation for real-time validation. Unlike existing solutions, the proposed system combines a Mecanum wheel-based mobile platform with a modular arm and advanced pan-tilt camera, enabling navigation and inspection of pipes ranging from 100 mm to 500 mm in diameter. A comprehensive dataset of 53,486 images, including 27,000 annotated defect instances across six critical classes, was used to train a YOLOv11-based detection framework. The model achieved high accuracy with a precision of 0.9, recall of 0.8, mAP@0.5 of 0.9, and mAP@0.5:0.95 of 0.6, outperforming previous YOLO versions, SSD, RCNN, and DinoV2 by 26% in mAP. Real-time testing on a Raspberry Pi Camera 3 Wide IR module validated the robust detection under realistic conditions. This work contributes a mechanically adaptable robot, an optimized deep learning inspection framework, and an integrated simulation-to-deployment workflow, providing a scalable and autonomous solution for industrial pipeline inspection. Full article

The problem of poor coupling and wheel breakage is a critical issue in the rapid inspection of rails using contact piezoelectric ultrasonic technology for trolleys and vehicles. To overcome this shortcoming, a non-contact unidirectional Shear Vertical Wave EMAT (USV-EMAT) for rapid rail flaw detection with a larger emission angle is proposed and optimized. First, the core characteristics of the USV-EMAT and the Unidirectional Line-Focusing Shear Vertical Wave EMAT (ULSV-EMAT) are compared and analyzed, including emission angle, directivity, intensity, and detection scan distance. The results confirmed that the USV-EMAT is more suitable for rapid rail flaw detection. Secondly, the orthogonal experimental analysis method was used to optimize the structural parameters of the probe. This study systematically identified the key factors influencing the directivity and intensity of acoustic waves excited by the probe, as well as the detection blind zones. Finally, the structural parameters of the integrated 37° USV-EMAT probe were determined by comparing and analyzing the received signal characteristics of the transmit–receive racetrack coil and the self-transmitting–receiving meander coil. The results show that the optimized probe achieves a 14.3 dB SNR for detecting a 5 mm diameter, 50 mm deep transverse hole in the rail, and a 14.0 dB SNR for a 3 mm diameter, 25 mm long, 50 mm deep flat-bottomed hole. Additionally, this study reveals that as the burial depth of the transverse holes increases, the detection scan distance for such defects exhibits an “N”-shaped trend, with the minimum occurring at a depth of 90 mm. Full article

This study has developed a novel measurement-based computational method that accurately determines the vertical and lateral wheel–rail contact forces transmitted from railway vehicles to the rails. A major contribution—and the first in the literature—is the analytical distribution of the total lateral wheelset force into its outer-wheel and inner-wheel components, thereby enabling precise individual evaluation of derailment risk on each wheel in curved tracks. Analytical equations derived from Newton’s second law were first formulated to express both vertical forces and total axle lateral force directly from bogie/axle-box accelerations and suspension reactions. To eliminate the deviations caused by conventional simplifying assumptions (neglect of creep effects, wheel diameter variation, and constant contact geometry), surrogate functions and distribution equations sensitive to curve radius, vehicle speed, and cant deficiency were introduced for the first time and seamlessly integrated into the equations. Validation was performed using the Istanbul Tramway multibody model in SIMPACK 2024x.2, with the equations implemented in MATLAB/Simulink R2024b. Excellent agreement with SIMPACK reference results was achieved on straight tracks and curves, after regression-based calibration of the surrogate functions. Although the method requires an initial regression calibration within a simulation environment, it relies exclusively on measurable parameters, ensuring low cost, full compatibility with existing vehicle sensors, and genuine suitability for real-time monitoring. Consequently, it supports predictive maintenance and proactive safety management while overcoming the practical limitations of instrumented wheelsets and offering a robust, fleet-scalable alternative for the railway industry. Full article

Surface defects on stay cables are primary contributors to wire corrosion and breakage. Traditional manual inspection methods are inefficient, inaccurate, and pose safety risks. Recently, cable-climbing robots have shown significant potential for surface defect detection, but existing designs are constrained by large size, limited operational speed, and complex installation, restricting their field applicability. This study presents an intelligent robotic system for detecting cable surface defects. The system features a dual-wheel driving mechanism, and a computer vision–based defect recognition framework is proposed. Image preprocessing techniques, including histogram equalization, Gaussian filtering, and Sobel edge detection, are applied. Interfering information, such as sheath edges and rain lines, is removed using the Hough Line Detection Algorithm and template matching. The geometry of identified defects is automatically calculated using connected component analysis and contour extraction. The system’s performance is validated through laboratory and field tests. The results demonstrate easy installation, adaptability to cable diameters from 70 mm to 270 mm and inclination angles from 0° to 90°, and a maximum speed of 26 m/min. The proposed defect recognition framework accurately identifies typical defects and captures their morphological characteristics, achieving an average precision of 92.37%. Full article

This paper presents the design of a seed-pressing mechanism for a high-speed suction-type precision peanut planter to address the issue of poor seeding performance at high travel speeds and to reduce seed bounce within furrows. To clarify the working principle of the mechanism, a force analysis of peanut seeds in the furrow and a numerical study using discrete element analysis were conducted under high-speed operating conditions. Simulation results show that when the distance between the center of the seed-pressing wheel and the seeding-tube outlet (DCSPW-STO) is 146.11 mm, the seed-pressing wheel diameter is 198.13 mm, and the machine operating velocity is 6.45 km h⁻¹, the plant spacing qualification index and seeding depth compliance index for peanuts planted after rolling reach their maximum values. The corresponding germination rates of 93.78% and 90.65% indicate satisfactory sowing performance. Field validation trials demonstrate that when DCSPW-STO (l_fz) is 146 mm, the seed-pressing wheel diameter (d_fz) is 198 mm, and the machine operating velocity (v) is 6.45 km h⁻¹, the post-seeding plant-spacing qualification index and the seeding-depth compliance index reach 90.31% and 89.18%, respectively. Although slightly lower than the simulation results, these values meet the operational requirements for peanut seeding. Field performance comparisons with non-pressure seeding units further confirm that units equipped with the seed-pressing and soil-covering mechanisms significantly improve both the plant-spacing qualification index and the seeding-depth compliance index, satisfying agronomic requirements for high-speed peanut cultivation. Full article

Axial Flux Permanent Magnet Synchronous Motor (AFPMSM) offers high power density in structures with short axial lengths and large radial dimensions, making it attractive for applications that require thin structures, such as in-wheel motors. This study proposes an AFPMSM design process applicable under fixed inner/outer diameter and axial length constraints. The proposed process is presented as step-by-step procedures: selection of pole/slot combinations, adjustment of slot depth, determination of stator/rotor dimensional ratios, and slot structure design. It is universally applicable to both bobbin-type rectangular wire windings and shoe-type round wire windings. The validity of the proposed process was verified through finite element method (FEM) analysis, and the differences between the two winding structures were examined through post-processing of the results. By presenting an AFPMSM design methodology that can be consistently applied under constrained spatial conditions, this study provides practical design guidelines for the development of in-wheel motors for next-generation mobility applications. Full article