Search Results (2,474)

Rapid and accurate assessment of tracked-vehicle trafficability on off-road terrain is essential for vehicle mobility evaluation and path planning. Traditional empirical formulas for tracked-vehicle terramechanics are computationally complex and require numerous measured parameters. To address this issue, this study proposes a vehicle trafficability inversion method that combines a multilayer perceptron with numerical simulation. First, a parametric tracked vehicle–terrain trafficability model was constructed using numerical calculations, and a vehicle trafficability dataset was generated from typical influencing parameters. Next, an MLP surrogate model was trained using these data. The model takes terrain slope, basic mechanical properties of the ground soil, track geometric parameters, vehicle weight, and vehicle speed as inputs and outputs vehicle trafficability information. The optimal model architecture was then identified through Bayesian optimization, and the results show that the surrogate model achieves high accuracy and real-time performance. In addition, an engineering case study demonstrates the potential application of this method in path planning. Full article

To address the safety, efficiency, and adaptability challenges of autonomous path planning for lunar rovers in complex and unknown terrain, this study proposes a path planning method that integrates deep reinforcement learning with Hybrid A*. Multi-level obstacle maps are generated from lunar digital elevation models and local terrain reconstruction, and an environment model integrating terrain features and rover states is constructed. A Deep Deterministic Policy Gradient framework is introduced, including a state encoder, a policy network, and dual Q-networks, to dynamically adapt the search parameters of Hybrid A* and optimize the path-cost function online. Simulation experiments are conducted under simple, moderate, and complex scenarios. The results show that the proposed method achieves a 100% path planning success rate in the evaluated baseline scenarios, with comprehensive accuracy scores of 0.931, 0.907, and 0.886, respectively. It outperforms conventional A*, RRT, and Hybrid A* algorithms in path length, smoothness, and safety. The method can support safe and efficient autonomous exploration by lunar rovers under the tested dynamic-illumination and multi-obstacle conditions and provides technical support for future deep-space exploration missions such as Chang’e and Tianwen. Full article

Accurate characterization of multi-band solar radiation is essential for optimizing photovoltaic (PV) site selection and supporting carbon neutrality targets. Shandong Province, a major economic and energy-consuming province in eastern China, possesses abundant solar resources but exhibits pronounced spatiotemporal heterogeneity driven by complex terrain, rapid urbanization, and variable cloud cover. Based on high-resolution CARE (Cloud Remote Sensing, Atmospheric Radiation and Renewable Energy Application) satellite products (0.1°, hourly, 2016–2020) combined with SRTM DEM, CLCD land use, and ERA5 cloud data, this study systematically analyzes the spatiotemporal distribution and driving factors of four solar radiation components—shortwave radiation (SWR), photosynthetically active radiation (PAR), UVA, and UVB—across Shandong Province. Key findings are as follows: (1) All four radiation components exhibit a consistent spatial pattern characterized by higher radiation intensities in the eastern coastal and northern plain regions, which gradually decrease toward the western inland and southern mountainous areas. Provincial five-year means are SWR 186.6 W/m², PAR 86.3 W/m², UVA 11.4 W/m², and UVB 0.3 W/m², with high-value zones concentrated in the Jiaodong Peninsula coast and the North Shandong Plain. (2) During 2016–2020, short-term increasing tendencies were observed across 80.4% (SWR), 78.0% (PAR), 85.1% (UVA), and 91.3% (UVB) of the province, while all components declined in winter. (3) STL decomposition reveals a “down-up-down” multi-year trend, a unimodal annual seasonal cycle peaking in May, and residuals closely associated with extreme weather events. (4) Geodetector analysis identifies cloud cover as the dominant factor (q = 0.332), followed by elevation (q = 0.100); and nonlinear enhancement characterizes all factor interactions, especially cloud cover × elevation (q = 0.393) and cloud cover × land-use (q = 0.347), revealing a “climate–topography–human activity” multi-level coupling mechanism. Built-up land records the lowest SWR (172.4 W/m²) and spatially coincides with radiation low-value zones. These results provide a scientific basis for PV site optimization and the realization of carbon neutrality goals in Shandong Province. Full article

Agricultural machinery operates under complex field conditions involving uneven terrain, crop flow impacts, variable speed and load, dust, moisture, and multi-source structural excitation. These factors make vibration-based fault diagnosis more challenging than that of conventional rotating machinery because weak fault features are often masked by non-stationary background vibration and operating condition disturbances. This review provides a structured synthesis of vibration-based fault diagnosis for agricultural machinery, focusing on tractors, combine harvesters, harvesting machinery, and key components such as bearings, gearboxes, transmission systems, headers, threshing drums, cleaning sieves, vibrating screens, chassis, frames, and cab systems. The review first analyzes vibration sources, fault mechanisms, and signal degradation under field conditions. It then summarizes vibration sensors, data acquisition, preprocessing, time–frequency analysis, feature representation, machine learning, deep learning, transfer learning, and multi-source information fusion. Applications are reviewed from component-level diagnosis to whole-machine monitoring. Key challenges include field data scarcity, variable conditions, sensor reliability, data leakage, model generalization, edge deployment, standardization, and long-term validation. Future research should emphasise high-quality field datasets, physics-informed and explainable models, robust cross-condition diagnosis, multimodal sensing, edge intelligence, digital twins, and predictive maintenance. This review highlights the need to connect vibration mechanisms, diagnostic models, and engineering deployment requirements for reliable agricultural machinery health monitoring. Rather than treating sensors, components, algorithms, and deployment issues as separate topics, this review organizes the literature around field-specific vibration disturbances, validation evidence, deployable diagnostic requirements, and future implementation priorities. Full article

Aboveground biomass (AGB) in grasslands is a key biophysical indicator for evaluating grassland productivity, ecosystem functioning, and carbon storage. However, accurate regional-scale AGB estimation in complex mountainous terrain remains challenging because of fragmented topography, strong environmental gradients, and heterogeneous grassland patches. This study evaluated the applicability of satellite embedding features for grassland AGB estimation in the Three Parallel Rivers region of Yunnan Province, China. Based on 135 field plots surveyed during the 2022 growing season, 64-dimensional annual satellite embedding features were extracted, and a conventional feature system derived from Sentinel-1, Sentinel-2, and DEM data was constructed for comparison. Four feature systems, namely Traditional-9, Traditional-40, Emb-9-PCA, and Emb-64, were evaluated using six regression models, including RF, SVR, GPR, XGBoost, LightGBM, and Elastic Net. Random five-fold cross-validation was used to compare feature systems and model combinations, while spatial cross-validation was further applied to assess model robustness under spatially independent conditions. The results showed that satellite embedding features outperformed conventional remote sensing features. Under random five-fold cross-validation, the Emb-64-based XGBoost model achieved the best performance, with an R² of 0.7949 ± 0.0405, an RMSE of 0.1388 ± 0.0191 t/ha, and an MAE of 0.1141 ± 0.0203 t/ha. Under spatial cross-validation, XGBoost retained the highest mean performance, with an R² of 0.7660 ± 0.1003, an RMSE of 0.1417 ± 0.0111 t/ha, and an MAE of 0.1165 ± 0.0124 t/ha. SHAP and Spearman correlation analyses further indicated that important embedding dimensions were associated with AGB and selected conventional environmental variables. Regional mapping showed that predicted grassland AGB ranged from 0.17 to 1.26 t/ha, with a mean value of 0.79 t/ha, and exhibited significant positive spatial autocorrelation. Bootstrap-based uncertainty analysis indicated that higher uncertainty mainly occurred in fragmented mountainous areas, grassland edges, and transition zones. These findings suggest that satellite embedding features provide a promising high-dimensional representation for grassland AGB estimation in complex mountainous landscapes, while their ecological interpretability and transferability still require further investigation. Full article

The decentralized deployment of photovoltaic (PV) systems in urbanized polluted mountainous basins faces unique challenges due to complex topography, persistent cloud cover and winter smog conditions. This study quantifies the atmospheric impact of localized winter haze/smog and Saharan dust intrusions on PV performance in the intermontane basin of Ioannina, NW Greece. By integrating a Digital Twin (DT) methodology with real energy production data, two PV plants were evaluated, a ground-based and a rooftop installation, to isolate the energy deficits caused by aerosol attenuation. The DT model demonstrated high accuracy (R² = 0.847) against actual power generation data for Koutselio and R² = 0.865 for Mpafra PV plants, while MBE was near zero for both sites (−0.008 kWh and −0.139 kWh, respectively). Error analysis revealed that the highest modeling discrepancies occurred during scattered clouds and intense winter haze conditions, primarily due to low spatial resolution of CAMS that fails to adequately capture localized biomass burning (BB) events. Despite the reduction in direct sunlight during extreme winter BB events, results indicate that the overall energy loss is mild. This operational stability is primarily due to the ability of c-Si modules to effectively utilize near-infrared radiation, which penetrates the low-level haze layer, alongside the thermal efficiency gains provided by low early-morning temperatures. Crucially, the installation geometry may influence system vulnerability. Direct comparisons revealed a minor power deviation of −4.8% for the ground-based Koutselio plant, while for the Mpafra site, there was a +3.2% production surplus likely linked to the high sky-view factor the rooftop installation has, which manages to capture isotropic diffuse irradiance. However, the low CAMS resolution may misclassify the haze events within the basin, further contributing to these discrepancies. On the contrary, Saharan dust intrusions caused broadband light attenuation, dropping the power production significantly on both installations. Ultimately, this research provides critical insights into the resilience of solar systems under strong air pollution events within polluted valleys in Southern Balkans, highlighting the connection between panel design and atmospheric attenuation. Full article

The climate sensitivity of high-altitude amphibians on the Qinghai–Tibet Plateau remains poorly quantified. We assessed habitat suitability for the plateau-endemic frog Nanorana parkeri across Xizang, China. Using 52 thinned occurrence records and nine predictors (spanning climate, terrain, and hydrological accessibility), we built a bias-corrected random forest (RF) model with VIF-screened variables, constrained complexity, and out-of-fold (OOF) evaluation, including spatial-block cross-validation. Gradient boosting (GBM) and a regularized GLM were fitted on identical data, and the RF was projected onto a two-GCM CMIP6 ensemble under four Shared Socioeconomic Pathways through 2100. The RF achieved OOF and spatial-block AUCs of 0.750 and 0.672 (GLM 0.734/0.691; GBM 0.692/0.584); all three converged on a current suitable area of 8.13 × 10⁵ km². Under future climate, net habitat expansion reached 117–126% of current area by 2081–2100. Range turnover was asymmetric: ~96% remained stable, 3–5% was lost, and 17–29% was gained upslope. Inter-GCM uncertainty was low (SD 0.016–0.022), but MESS extrapolation was substantial (55–67% of Xizang). Net upslope expansion represents a robust climatic opportunity, but realised gains depend on persistent breeding waters. Conservation actions should prioritise protecting stable core breeding waters in Southern and Southeastern Xizang and monitoring upslope colonisation fronts. Full article

Low-power electric all-terrain vehicles (ATVs) experience repeated acceleration, grade-driving, and regenerative-braking events that impose high transient current demand on the battery pack. This study presents a reduced-switch battery/supercapacitor hybrid energy storage system (HESS) as a battery-current-stress mitigation architecture for low-power electric ATVs. Converter-level hardware tests are used to verify the voltage-regulation capability of a 500 W reduced-switch prototype, whereas vehicle-level Simulink evaluations are used to compare battery-current-stress indicators under representative ATV-oriented cycles. The proposed mode-constrained Db4 allocation strategy assigns the smoother positive demand component to the battery and fast transient and braking-related power components to the supercapacitor. Under the ATV-oriented complex cycle, the proposed HESS limits the battery current to 15 A, reduces the RMS battery current from 19.31 A to 12.45 A, decreases the maximum DC-bus voltage sag from 1.528 V to 0.523 V, and recovers 1.738 Wh of regenerative braking energy in the evaluated model. These results indicate reduced battery-current-stress indicators and improved DC-bus regulation within the evaluated operating range; direct battery aging, thermal, and cycle-life validation are outside the scope of the present work. Full article

Previous studies on construction site layout often simplified the site as a rectangle, with little consideration of adaptability to complex terrain and multiple functional constraints. An optimization method was developed for irregular construction sites, based on Systematic Layout Planning (SLP) and an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II), to address the limited adaptability of traditional methods in multi-objective and multi-constraint scenarios. A mathematical model for site layout was constructed using a rasterization method, with transportation time, transportation cost, and noise level as the optimization objectives. High-quality initial populations were generated by quantifying logistics and non-logistics relationships using the SLP method. The NSGA-II algorithm was enhanced with an adaptive penalty function, two-point crossover encoding, dynamically adjusted crossover and mutation probabilities, and a population restart mechanism. This improved its global search efficiency and convergence performance in complex solution spaces. Case validation results indicate that SLP-INSGA-II outperforms NSGA-II and SLP-NSGA-II while maintaining comparable performance to INSGA-II on some indicators. Without degrading overall optimization performance, incorporating SLP-based engineering priors can enhance search guidance, leading to layout solutions that are both feasible and engineering-interpretable. This study provides a modeling and solution approach for layout optimization in irregular construction sites. Full article

This paper proposes a Multi-Strategy Improved Connected Banking System Optimizer, named MICBSO, for numerical optimization and three-dimensional UAV path planning. MICBSO enhances the original CBSO through three coordinated strategies. First, a chaos–opposition learning initialization strategy is introduced to improve initial population quality and search coverage. Second, a Gaussian perturbation-based multi-elite guidance mechanism is designed to reduce dependence on a single best solution and strengthen the balance between exploration and exploitation. Third, a hybrid boundary control strategy combining reflective correction and random reinitialization is developed to improve solution feasibility and maintain population diversity. The proposed algorithm is evaluated on the CEC2017 benchmark suite and compared with 11 representative algorithms. Experimental results show that MICBSO achieves competitive convergence accuracy, stability, and robustness across different dimensional settings. In addition, MICBSO is applied to three-dimensional UAV path planning in four complex terrain scenarios. The results demonstrate that MICBSO can generate feasible and safe flight paths with lower comprehensive cost. Overall, the proposed method provides an effective optimization framework for both benchmark optimization and constrained UAV path planning tasks. Full article

Autonomous mobile robots are playing an increasingly significant role in modern smart orchards by supporting precision agricultural operations such as target-oriented spraying and autonomous harvesting. Nevertheless, achieving high-precision trajectory tracking and stable motion in complex, unstructured orchard environments remains challenging, because tracking deviations induced by uneven terrain and low-traction soil can directly affect operational safety and efficiency. To address this challenge, the present study proposes an adaptive tracking controller which integrates model-driven and data-driven approaches. Firstly, a six-state planar dynamic model based on Newton–Euler equations is established to describe motion characteristics. Secondly, an improved Particle Swarm Optimization (PSO) algorithm is employed for offline parameter optimization under representative operating conditions. The process thus engenders a mapping dataset that relates the real-time motion states of the orchard mobile robot to the optimized horizon parameters and weights. Finally, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is trained using this dataset, enabling adaptive adjustment of MPC parameters according to the robot motion state. Simulation and experimental results demonstrate that, in Double-Lane-Change (DLC) and serpentine simulations, the proposed controller reduced lateral and heading Root-Mean-Square (RMS) errors to 0.0109 m/0.0081 rad and 0.0102 m/0.0117 rad, achieving reductions of 49.30–85.58% and 68.60–88.02% compared with Pure Pursuit, Stanley, Linear Quadratic Regulator (LQR), and traditional MPC, respectively. In orchard field tests with circular and Figure-8 trajectories at 0.3–0.6 m/s, the lateral RMS errors were recorded as 0.0112–0.0182 m and 0.0156–0.0262 m, respectively, corresponding to reductions of 46.94–61.52% relative to traditional MPC, while the heading RMS error remained below 0.0510 rad. These findings substantiate the efficacy of the proposed controller in enhancing the accuracy and adaptability of the system, thereby providing a resilient and precise control framework for operation within orchard environments. Full article

Robust and accurate lithological parameters are essential in engineering, geology, geophysics, geodesy, and resource exploration. Among these parameters, rock density plays a fundamental role in gravimetric geodesy and geophysics. However, the systematic collection, analysis, and categorization of rock density data remain insufficient in many countries around the world, including Ethiopia. Ethiopia is characterized by extreme topographic variations (exceeding 4500 m) and complex geology, dominated by Cenozoic volcanic formations associated with the East African Rift System. Consequently, the commonly adopted upper continental crustal density of 2670 kg/m³ is inadequate for precise geodetic applications (e.g., the definition and realization of the geodetic vertical datum) as well as for gravimetric modeling and interpretation (e.g., the compilation of Bouguer, isostatic, and mantle gravity maps) in the country. To address these limitations, we prepared the first comprehensive digital rock density model of Ethiopia, with a particular focus on its applications in gravimetric geodesy and geophysics. The rock density model has been prepared by integrating the Ethiopian geological database, comprising 88 lithological units, with established global rock-density databases to assign representative density values and their uncertainties to each geological unit. The height-weighted average densities, accounting for the mass contribution of elevated terrain, were computed from a 90-m-resolution digital elevation model. The rock density map shows significant density variations across Ethiopia, ranging from 1528 to 2892 kg/m³. The average height-weighted density of Ethiopia is 2430 ± 352 kg/m³, which is 9% lower than the standard density of 2670 kg/m³. We expect that the use of the rock density model instead of assuming only a constant density value for the whole country will improve the accuracy of gravimetric geoid modeling and orthometric height determination, both essential for the modernization of the geodetic vertical datum. This demonstrates the necessity of region-specific density models for countries in tectonically active and/or geologically complex settings. The study also provides a transferable methodological framework for developing similar products in other data-sparse regions. Full article

Lithological mapping is essential for geological studies, mineral exploration, and environmental assessment. Satellite remote sensing combined with machine learning provides a scalable, cost-effective approach for regional lithological discrimination. This study evaluates hyperspectral and multispectral satellite imagery for lithological mapping in a geologically complex region of northwestern Queensland, Australia. The study area, within the Mount Isa Inlier, comprises diverse sedimentary, volcanic, intrusive, and metamorphic lithologies. PRISMA hyperspectral and ASTER multispectral imagery were analyzed using supervised classification algorithms, including Support Vector Machine (SVM), Mahalanobis Distance (MaDC), Minimum Distance (MDC), and Maximum Likelihood (MLC). Image-derived endmembers from representative lithologies were used as training data. Classification accuracy was assessed using confusion matrices, Overall Accuracy (OA), and the Kappa coefficient. PRISMA imagery outperformed ASTER data. SVM achieved the highest performance for PRISMA (OA = 82.03%, Kappa = 0.81), whereas MLC achieved the highest performance for ASTER (OA = 33.29%, Kappa = 0.30). Classification accuracy was evaluated using an independent set of validation ROIs that were spatially separated from the training samples, providing a more reliable estimate of model performance. These results highlight the benefits of hyperspectral remote sensing with machine learning for lithological discrimination in complex terrain and emphasise the importance of spatially independent validation. The approach demonstrates strong potential for regional-scale applications and may support more efficient mineral exploration and geological mapping workflows. Full article

Accurate large-scale crop mapping is fundamental to agricultural management. However, in Liaoning Province, undulating terrain and fragmented fields make fine crop classification challenging. In particular, corn and soybean have overlapping phenologies, which can lead to spectral and structural confusion in conventional optical–SAR feature spaces and limit mapping accuracy. This study proposes a fine crop mapping framework integrating optical phenotypic, microwave structural, and meteorological time-series features. To overcome the curse of dimensionality caused by high-dimensional heterogeneous data, an adaptive feature truncation mechanism based on the transition pattern of the marginal-gain curve was designed. Additionally, a pyramid multi-scale sliding window algorithm was constructed to optimize meteorological features, achieving dimensionality reduction and precise identification of phenologically sensitive windows. The results indicate that: (1) The multi-scale feature selection strategy effectively eliminates redundant variables and maximizes the inter-class discriminability of core features, significantly improving computational efficiency and classification performance. (2) High-frequency meteorological features provide key physiological constraints. Specifically, mid-May shortwave radiation, early October precipitation, and early August growing degree days constitute the core environmental–physiological features for distinguishing confused crops, helping to mitigate the spectral confusion of dryland crops. (3) Driven by the multi-source features, the Support Vector Machine (SVM) exhibits the optimal generalization robustness for processing high-dimensional structured data, yielding an overall classification accuracy of 91.80% and a Kappa coefficient of 0.8905. This framework provides a reliable methodological reference for high-precision crop monitoring in large-scale complex planting areas. Full article

Autonomous navigation of harvesters in hilly and mountainous terrain faces two major challenges: sensor discrepancies among multiple sources and depth distortion caused by terrain slopes. This paper proposes a tightly coupled vision–inertial–depth navigation and control system to address these issues. The system fuses visual features with inertial data within an adaptive extended Kalman filter framework that dynamically adjusts sensor weights to resolve conflicts from illumination changes and inertial drift. It also incorporates a real-time depth compensation model based on vehicle attitude to correct spatial mapping distortions during slope operations. Additionally, a multi-controller coordination strategy integrates steering, speed, and header height to align state estimation with control execution. Field experiments show that the system achieves a lateral positioning error of 3.4 cm—48.5% and 81.5% lower than pure-vision and pure-inertial approaches, respectively-and remains within 9.5 cm even in degraded scenarios. These results demonstrate the system’s ability to deliver high-precision navigation and stable operation on complex terrain. Full article