Search Results (295)

To improve the harvesting efficiency of mechanized cabbage harvesting and reduce damage, the structural configuration of a cabbage harvester was designed based on the cabbage cultivation pattern, physical morphological parameters, and mechanical harvesting characteristics. The harvester consists of a crawler power chassis, pulling device, crop guiding device, clamping and conveying device, profiling device, root-cutting device, and leaf-stripping and collecting device, which enables simultaneous pulling, conveying, root cutting, outer leaf separation, and collection for two rows of cabbages in a single pass, thereby enhancing harvesting efficiency. The sources of cabbage damage during the harvesting process were analyzed, and dynamic analyses of the key components were performed to determine their structural parameters. Through single-factor experiments and response surface methodology optimization tests, the effects of forward speed, pulling roller rotational speed, clamping and conveying speed, and cutter rotational speed on the harvest qualification rate were evaluated. The optimal working parameter combination of these factors was determined and validated through field harvesting performance tests. The results showed that, under the operating conditions of forward speed 0.4 m/s, pulling roller rotational speed 114 r/min, clamping and conveying speed 0.51 m/s, and cutter rotational speed 338 r/min, the average harvest qualification rate reached 96.4%, and the average damage rate was 3.6%, which is close to the maximum theoretical harvest qualification rate of 96.78% predicted by the optimization model. The field validation tests demonstrated good performance, with all indicators meeting the design requirements and relevant standards, providing theoretical support and reference for the development and improvement of cabbage harvesting machinery. Full article

Aiming at the problem of the insufficient passability and stability of the tracked chassis of silage harvesters caused by complex hilly and mountainous areas and a severe working environment, the crawler chassis of self-propelled silage harvesters was taken as the research object, the straight-line driving, longitudinal climbing, and lateral climbing processes of the chassis were theoretically analyzed, and the critical parameters that affect the normal climbing of the chassis were calculated. Meanwhile, the multi-body dynamics model of the tracked chassis was established by using the software SolidWorks 2020 and RecurDyn 2023, and its climbing and obstacle crossing performance were analyzed. The relevant motion parameters of the tracked chassis suitable for longitudinal and transverse slopes in hilly and mountainous areas were obtained, and field tests were conducted on the tracked chassis to verify the reliability of the simulation model. According to the simulation results, the tracked chassis achieves ultimate slope angles of 28° longitudinally and 23° laterally. It demonstrates the capability to navigate 140 mm high ridges and 250 mm wide trenches smoothly, while its straight-line driving offset rate conforms to prevailing agricultural machinery industry standards. Field test results indicated that the tracked chassis achieved a maximum longitudinal climbing angle of 26°. The relative error of less than 8% between the experimental and simulated data confirms a strong correlation. The maximum offset rate for straight-line travel is 1.95%, meeting the requirements of the agricultural machinery industry standards. The test verified the feasibility of the dynamic model of the crawler chassis of the silage harvester, providing a theoretical basis and technical support for the optimal design of the crawler chassis of the self-propelled silage harvester in hilly and mountainous areas. Full article

In-pipe robots must navigate narrow, curved passages where rigid mechanisms often require bulky steering units. Soft crawlers offer better compliance but typically rely on multiple actuators or reconfigurable contacts to achieve multi-directional motion. Drawing inspiration from biological soft crawlers that exploit directional friction and coordinated anchor–slip patterns, this study focuses on locomotion principles observed in caterpillars, water boatmen, and whirligig beetles. Based on these bioinspired concepts, we present a tendon-driven soft in-pipe robot that combines continuum bending–twisting deformation with modular anisotropic friction pads (AFPs), enabling three locomotion modes using only two motors. AFP inclination, curvature, and ridge geometry were optimized through friction tests, constant-curvature modeling, and finite element analysis to enhance directional adhesion on flat and curved surfaces. A deformation-based locomotion framework was developed to couple tendon actuation with friction orientation, achieving longitudinal crawling, transverse translation, in-place rotation, and smooth transitions via programmed twisting. Driving experiments demonstrated repeatable anchor–slip locomotion with average speeds of 28.6 mm/s, 15.7 mm/s, and 11.5°/s for the three modes. Pipe tests in straight, curved, and T-junction sections further validated stable contact and reliable gait transitions. These findings highlight the potential of friction-programmed continuum robots as compact, bioinspired platforms for advanced in-pipe inspection and diagnostic tasks. Full article

Although extensive research has been conducted on peristaltic robots, early designs are often constrained by mechanical configurations and material constraints, which restrict kinematic capability, particularly steering control. In contrast, snails steer by modulating mucus secretion to redistribute ventral friction along the foot. Inspired by this strategy, we propose a friction-differential steering mechanism and develop a novel crawler that implements it. The crawler is integrated with a peristaltic robot, and three experiments are conducted to evaluate steering performance. We further establish a physical model of friction-differential steering, including cases identified from the experiments. The proposed model captures the experimentally observed trend that the steering response increases with the friction differential and provides a qualitative physical interpretation of the steering mechanism. Finally, the method is generalized by analyzing its limiting behavior, thereby clarifying the operating bounds of the proposed approach. This work provides a principled framework for steering control in peristaltic robots and offers a promising direction for improving their motion controllability. Full article

The rapid expansion of crypto-asset markets and the introduction of the Markets in Crypto-Assets Regulation (MiCAR) pose novel supervisory challenges. Existing blockchain intelligence platforms focus predominantly on on-chain surveillance, leaving gaps in off-chain documentary due diligence automation. This paper presents a Multi-Agent System (MAS) integrating Large Language Model (LLM) capabilities with rule-based compliance frameworks. The architecture comprises seven specialized agents: a Coordinator Agent for orchestration; data acquisition agents (Searcher, Crawler); three parallel analytical agents—Heuristic Agent (LLM-powered qualitative risk assessment), Compliance Agent (hybrid-AI MiCAR asset classification and regulatory requirement verification), and On-Chain Agent (machine learning-based fraud detection); and a Reconciliator Agent synthesizing findings into unified alerts. Component-level empirical validation on 150 projects indicates 95% output reproducibility (identical alert tier and score deviation $\le 0.05$ across five reruns) and 210 s mean latency, providing proof-of-concept evidence for the integrated pipeline. A pilot user evaluation (six researchers/master students and two experts from regulatory authorities) provides preliminary usability evidence and surfaces domain-specific feedback from regulatory-authority experts. The architecture advances proactive regulatory technology by enabling scalable analysis combining off-chain documentary evidence with on-chain forensics. Full article

In the construction industry, struck-by accidents involving heavy equipment such as crawler excavators are a leading cause of worker fatalities and injuries. Existing vision-based hazard detection methods are limited by approximate evaluations, reliance on specific references, and neglect of spatial relationships between equipment and workers, making them inadequate for complex dynamic construction environments. This study aims to address these limitations by proposing a precise and adaptable struck-by hazard detection method. The method integrates four core modules: object tracking via the YOLOv5-DeepSORT model to detect workers, excavators, and their key components; activity recognition to identify the operational states of excavators, working or static, and workers, driver or field worker; proximity estimation based on stereo vision using the BGNet model and camera calibration to calculate 3D spatial distances; and safety identification to assess worker safety status in real time. Validated through three virtual construction scenarios, flat ground, rugged terrain, slope, the method achieved high safety status identification accuracies of 92.71%, 90.04%, and 94.25% respectively. The results demonstrate its robustness in adapting to diverse construction environments and accurately capturing equipment–worker spatial interactions. This research expands the application scope of hazard monitoring in complex settings, enhances safety identification efficiency, and provides a reliable technical solution for improving construction site safety management. Full article

Crawler cranes suffer from power mismatch in their electro-mechanical–hydraulic main drive system under sudden load variations and multi-action coupling conditions, which results in low energy utilization efficiency. This study establishes an electro-hydraulic closed-loop transfer function model and proposes a synthetic deviation-driven proportional–integral–derivative power matching control strategy to regulate hydraulic pump displacement. Experiments on a 1250 t crawler crane show that the model is reliable, with the coefficient of determination R² between simulation and experimental data exceeding 0.94. The strategy improves power matching by 27.33% for the engine–pump link and 18.9% for the full link in single-action conditions, and 12.67% and 8.7% in compound-action conditions. After control, the engine–pump power matching coefficient stabilizes around 1.04 and the full-link coefficient stabilizes within 1.05 to 1.21 under single-action conditions, while under compound-action conditions, the engine–pump coefficient stabilizes within 1.05 to 1.15 and the full-link coefficient stabilizes within 1.0 to 1.25. The control strategy suppresses power mismatch and optimizes system stability and energy efficiency, which provides a technical reference for the optimization of electro-mechanical–hydraulic drive systems in heavy machinery. Full article

The harvesting of Camellia oleifera fruit in hilly areas faces core problems such as low manual efficiency, poor terrain adaptability of existing machinery, and severe emissions and noise from traditional equipment. This study designed a crawler chassis utilizing a permanent magnet synchronous motor-driven hydraulic system. The research integrated kinematic modeling and resistance calculations for parameter matching, followed by AMESim dynamic simulations and motor calibration experiments. Finally, comprehensive field tests were conducted to evaluate the prototype. The results indicate the chassis achieves a maximum travel speed >1.5 m∙s⁻¹, a climbing angle of 41.4°, and a turning radius of 0.72 m, with noise levels strictly below 80 dB(A). Significantly, dynamic power characteristic tests under actual vibration harvesting conditions revealed that the 45 kW motor maintains a rapid response with ample power reserve. The input power exhibited a distinct square-wave pattern synchronized with hydraulic valve commands, peaking at 18.1 kW during vibration bursts. These findings confirm the system’s stability under coupled driving and harvesting loads. This design offers a viable, low-noise solution for electrifying and intelligently upgrading Camellia oleifera harvesting equipment in complex terrains. Full article

In the complex operational environment of coal mine shafts, trajectory tracking control of crawler hydraulic drill-anchor robots is susceptible to track slippage and internal–external uncertain disturbances, leading to low tracking accuracy. This issue hinders the implementation of efficient and precise coal mine roadway support operations. To address these challenges, enhance the automation level of coal mine roadway support, and improve operational safety and reliability, research on high-precision trajectory tracking control for crawler hydraulic drill-anchor robots is imperative. Therefore, this paper takes crawler hydraulic drill-anchor robots as the research object and focuses on the trajectory tracking control of such robots. First, a kinematic model incorporating track slippage was established for the crawler hydraulic drill-anchor robot. Second, a cascade predictive control strategy is proposed. The upper-layer trajectory tracking control adopts an adaptive model predictive controller, which adjusts controller weights according to tracking error variations and provides reference rotational speeds for the lower-layer predictive controller. Simulation results of linear and sinusoidal trajectory tracking show that the proposed strategy can effectively compensate for the effects of track slippage and improve trajectory tracking accuracy. Finally, a friction-compensated predictive control method was designed to regulate the rotational speeds of the left and right track drive wheels, and the proposed method achieves optimal control performance with a minimum MEAE of 0.12292 rpm, SDAE of 0.44366 rpm, ITAE of 4.9168, MEACI of 3.0607 mA, SDACI of 1.2497 mA, and ITACI of 122.4283. This performance is significantly superior to that of the conventional PID, ADRC, and MPC methods, thereby realizing high-precision track speed control. Full article

Urban flood information enables managers to rapidly synthesize comprehensive flood event profiles, serving as critical evidence for flood control decision making. Compared with traditional methods, public data offer unprecedented spatiotemporal granularity due to its high volume, multidimensionality, and real-time nature. In this paper, we investigated public data’s usefulness and generalizability of spatial feature differences using multi-source social media data as an entry point. We selected rainstorm events that occurred in three cities located in the North China Plain, the Southeast Coastal Region, and the Western Region of China, with vastly different developmental statuses in 2023. Then, multi-platform data from the events were collected and analyzed through crawling and topic mining. The results indicate that: (1) social media data from different sources are complementary to each other and can collectively extract plenty of neglected waterlogging points to supplement official data, with a supplementary rate reaching 171% on average; and (2) social media data has significant value in spatial characterization, which means that its availability remains constant despite geographical differences and can self-adapt to local geography, inhabitant profiles and social development levels. To address the issues of limited available data and essential information lacking during the analysis process, we propose recommendations for data processing and city managers to enhance the scientific value of social media data utilized in practice. Full article

In this study, a three-dimensional (3D) model is developed based on an actual small forestry crawler tractor, to analyze its overturning and rollover behaviors, and a corresponding simulation model is constructed. The accuracy of the 3D model is validated by comparing its dimensions and center of gravity with those of the physical tractor, and the fidelity of the simulation model is verified using static sidelong falling angle, minimum turning radius, and driving tests. The developed simulation framework was employed to investigate the dynamic behavior of the small forestry crawler tractor, focusing on roll and pitch angular velocities across different obstacle heights, slope angles, and driving speeds. Backward rollover was not observed within the tractor’s realistic operating speed range, indicating that backward rollover is not the dominant risk mode. In contrast, lateral overturning occurs under all driving scenarios, and increases in driving speed and obstacle height lead to higher roll angular velocities, increasing the risk of lateral overturning. Across all conditions, the likelihood of lateral overturning surges when the roll angular velocity enters the 80–100°/s range, with obstacle height exerting the greatest influence. In conclusion, the small forestry crawler tractor is more prone to lateral overturning than backward rollover when driving on inclined surfaces. A distinct threshold roll angular velocity is identified as the onset point of lateral overturning, which will vary according to the tractor’s specifications. This study is a quantitative study of a small forestry crawler tractor and does not correlate with a full-scale tractor. While angular velocity values vary during lateral overturning and backward rollover, this study was conducted to identify trends under various driving conditions. Further work is required to apply the proposed analysis methodology to full-scale agricultural and forestry machinery and validate it with real-world operational data. Full article

With the growing global popularity of Android apps, ensuring their quality and reliability has become increasingly important, as low-quality apps can lead to poor user experiences and potential business losses. A common approach to testing Android apps involves automatically generating event sequences that interact with the app’s graphical user interface (GUI) to detect crashes. To support this, we developed ACE (Android Crawler), a tool that systematically generates events to test Android apps by automatically exploring their GUIs. However, ACE’s original heuristic-driven exploration can be inefficient in complex application states. To address this, we extend ACE with a deep reinforcement learning-based crawling strategy, called Reinforcement Learning Strategy (RLS), which tightly integrates with ACE’s GUI exploration process by learning to intelligently select GUI components and interaction actions. RLS leverages the Proximal Policy Optimization (PPO) algorithm for stable and efficient learning and incorporates an action mask to filter invalid actions, thereby reducing training time. We evaluate RLS on 15 real-world Android apps and compare its performance against the original ACE and three state-of-the-art Android testing tools. Results show that RLS improves code coverage by an average of 2.1% over ACE’s Nearest unvisited event First Search (NFS) strategy and outperforms all three baseline tools in terms of code coverage. Paired t-test analyses further confirm that these improvements are statistically significant, demonstrating its effectiveness in enhancing automated Android GUI testing. Full article

The high accessibility and interconnectedness of social media platforms have led to their increasing exploitation as tools for criminal activity. A notable example of such digital sexual offenses is the “Nth Room” case, in which sexually exploitative content and illegal recordings were unlawfully distributed on platforms such as X, Telegram, and Discord. Despite amendments to legislations, including the Sexual Violence Punishment Act and Youth Protection Act, aimed at preventing the recurrence of incidents, these crimes continue to persist. Perpetrators employ tactics such as the repeated creation and deletion of accounts, which complicate efforts to track and apprehend them. Consequently, there is an urgent need to develop advanced cyber investigation technologies capable of effectively monitoring sexual crimes posted on social media. This study aimed to propose a novel cyber investigation technology designed to trace criminal organizations by collecting tweets related to sexual crimes from X, which is the most frequently used social media platform for such content in Korea, and subsequently constructing a knowledge graph. Slang terms commonly associated with sexual crimes on X were employed as search keywords to collect relevant tweets. The knowledge graph is then generated based on three key elements extracted from the tweets: hashtags, words, and URL/invite codes. This graph serves as a tool for tracking the criminal networks involved in the distribution of sexually exploitative content and unauthorized recordings. Furthermore, to enhance tracking efficiency, an optimization model was developed to generate knowledge graphs from various analytical perspectives. In this study, to evaluate the performance of the proposed technology, a dataset of 3387 tweets was collected using an X crawler. Knowledge graphs were generated and optimized through both single and combined analyses of the three key elements, demonstrating the effectiveness of the proposed technology in tracking criminal organizations engaged in sexual crimes. Full article

This study explores the core competencies required for sales and marketing roles in the rapidly evolving NEV sector. Adopting an exploratory sequential mixed-methods design, it employs a big data-driven approach to construct a competency framework: web crawlers collected NEV-related recruitment data across over 20 major Chinese cities, the Latent Dirichlet Allocation (LDA) model identified core competency items, and a multi-dimensional consensus scoring process via the Nominal Group Technique (NGT) refined the framework. The resulting validated model comprises nine thematic clusters, reflecting a shift from internal combustion engine (ICE) vehicle sales’ traditional skill set. Beyond enriching conventional competencies (customer reception, sales service, CRM, sales support), it highlights emerging capabilities: live-streaming/short-video marketing, digital media operations, and ecosystem-oriented resource collaboration. Further, NGT-based multi-dimensional evaluations (frequency, importance, difficulty) generated a four-quadrant matrix, offering actionable guidance for vocational education and corporate training (VET) curriculum design. Theoretically, this study redefines digital-era automotive sales roles: not mere product sellers, but core actors in user experience co-creation and ecological value integration, which enriches discourse on sales role evolution. Full article

Crawler-type combine harvesters feature labor-intensive operation, tough steering and complex environments in paddy fields, necessitating reliable automatic operation to ensure efficient and complete harvesting. An error threshold-based autonomous navigation system for crawler-type combine harvesters was developed by using right-angle turning according to unilateral brake steering. Based on the chassis structure and working principles, a moving control system was designed to achieve automatic control of steering, speed and throttle. A global path planning method was proposed to generate a spiral path by giving reference points and operation directions. A path tracking method based on the error threshold was developed to calculate both lateral and heading errors in real-time, and we executed the adjustment strategy to ensure rapid alignment and high-precision tracking. A right-angle turning method was implemented to prevent missed cutting and crop damage by giving an adjustment distance. Field tests showed that the maximum lateral and heading errors for straight-line path tracking were 10.25 cm and 1.94°, respectively. The maximum lateral and heading errors for right-angle turning were 17.64 cm and −14.46°, respectively. It was concluded that the newly developed autonomous navigation system showed adequate path tracking accuracy and stability, meeting working requirements in crop harvesting. Full article