Terrain Height Research Articles

State estimation and traversability map construction method of a quadruped robot on soft uneven terrain

AbstractQuadruped robots show excellent application prospects in complex environment detection and rescue. At present, scholars mainly focus on quadruped walking in rigid environments. However, quadruped robots often need to pass through uneven and soft unconstructed terrains, prone to slip and impact. The mismatch between the planned foothold position and the real one resulting from environmental uncertainties makes the robot unstable. In this paper, the state estimation and traversability map construction methods are proposed for quadruped robots to achieve stable walking in an unstructured environment, especially on soft terrains. First, the Error‐state Kalman Filter (ErKF) is extended by optimizing the leg odometry information to get an accurate robot state, especially in soft, uneven terrain. The ErKF method fuses the sensor data from the inertial measurement unit, laser, camera, and leg odometry. The leg odometry is optimized by considering the foot slippage, which easily occurs in soft uneven terrains. Then, the unstructured environment is parameterized and modeled by the terrain inclination, roughness, height, and stiffness. A traversability map, which is essential for robot path and foothold planning in autonomous movement, is constructed with the above parameters. Finally, the proposed method is verified by simulation and experiments. The results show that the quadruped robot can walk stably on different soft and uneven terrains.

Shooting Simulator «Inhibitor»: Mathematical Supportof Fire Range and Shielding

Mathematical support for constructing the relief of a shooting range from an image with support for the height and range of each point and taking into account the screening of targets by folds terrain for tactical optical-electronic simulator of the small arms 'Inhibitor' developed at Institute of mechanics UdmFRC UB RAS and at Computer facilities department of Kalashnikov ISTU jointly with JSC «Kalashnikov Concern» was described. The tactical and technical task for the functional capabilities of implementing the relief of the shooting range is given: hills, ravines, mountains at a distance of up to 2 km in the sector up to 60°, shielding targets with folds of the area. A relief model is proposed in the form of a bilinear interpolated matrix of heights with a pitch of 10 m, and an editor is developed that allows interactively specifying range boundaries and terrain height along the front on the image of the shooting range. Screening the visibility of targets by folds of the terrain is implemented using the central projection of all relief profiles for a single workplace of shooters, and for the implementation of trenches, a mechanism for the height of the target above the surface of the shooting range is proposed, which optimizes the 'quality-speed' ratio. Studies of the «projector-screen-observer» system showed the presence of factors of errors in the virtual shooting range, some of which are of a fundamental nature, and the rest can be compensated for by the regression model of the target point recorder and taking into account its cumulative error by reducing the tabular ballistic dispersion of simulated shots. Based on the prospects for further research and development of electronic shooting simulators and thanks to the improvement of computing tools and the development of software graphics libraries, it is necessary to expand the functionality of the simulators and reduce the cost, which means to increase competitiveness, for example, improving the realism of simulating the terrain of the shooting range with screening of targets by folds of the terrain.

ICESat-2 data classification and estimation of terrain height and canopy height

ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) was launched in 2018 with a photon-counting LiDAR (Light Detection and Ranging) system, ATLAS (Advanced Topographic Laser Altimeter System). It is collecting massive earth elevation data all over the world, which has shown the potential of large-scale forest monitoring. However, the energy emitted by the LiDAR system is low, and the received signals are easily affected by noise. Accurate classification of photons is an important step for forest parameter retrieval. Given the limitations of existing photon classification algorithms in areas with complex terrain, we proposed an improved local outlier factor algorithm with a rotating search area (LOFR). First, photons are transformed to the along-track direction, and noise photons are preliminarily filtered out by using the elevation histogram and elevation statistical methods. Next, ground photons are extracted by using the LOF (Local Outlier Factor) with the horizontal ellipse search area algorithm (LOFE) during an initial classification stage to filter photons that are far away from the ground. During the refined classification stage, which is the core of the algorithm, the terrain slope is calculated according to the ground photons extracted during initial classification. The elliptic search area is then rotated to align its long axis with the terrain slope. Finally, the LOFR scores of the photons are calculated to remove noise photons and signal photons are classified into top-of-canopy photons, canopy photons, and ground photons. The results show that the algorithm can effectively classify photons. Both estimated terrain height and canopy height derived using the classified photons are in good agreement with airborne LiDAR data. The mean absolute error (MAE) of the estimated terrain height relative to airborne data was 1.45 m and the root mean square error (RMSE) was 2.82 m. For canopy height validation, the correlation coefficient (R2), MAE, and RMSE at the best study scale (80 m) were 0.86, 1.82 m, and 2.72 m, respectively. These results demonstrated that the proposed LOFR algorithm can improve photon classification over complex terrain without prior knowledge of the terrain. Therefore, it could provide a robust approach for large-scale ATLAS data processing.

Typhoon field construction and wind-induced wave model optimization based on topographic parameters

In recent years, most of the research on typhoon in Fujian Province of China has stayed in the typhoon wave simulation under the influence of wind fields. In order to study the influence of land terrain on typhoon wave fields, the terrain height parameters are introduced into the wind field simulation through the third-generation typhoon wave numerical model. The numerical simulation results of the model are verified and analyzed by referring to the data of significant wave height and wind speed of typhoon Fung-wong (200808) and typhoon Jangmi (200815) when it is across Taiwan Island. The results show that the relative error of the maximum wind speed and the significant wave height is reduced after the wind field optimization, which is more consistent with the observed data. The model accuracy is improved after optimization; Typhoon Fung-Wong, which crossed Taiwan, showed the most significant reduction in wind speed. Significant wave height is positively correlated with wind speed. The wave height distribution shows a gradually decreasing trend from the cyclone center to the coast, and the significant wave heights of the typhoon moving direction show pronounced asymmetry. Affected by the land topography, the distribution of significant wave height generally shows a downward trend, and the area of height water level area decreases.

Assessment of Oak Groves Conservation Statuses in Natura 2000 Sacs with Single Photon Lidar and Sentinel-2 Data

Among the main objectives of Natura 2000 Network sites management plans is monitoring their conservation status under a reasonable cost and with high temporal frequency. The aim of this study is to assess the ability of single-photon light detection and ranging (LiDAR) technology (14 points per m2) and Sentinel-2 data to classify the conservation status of oak forests in four special areas of conservation in Navarra Province (Spain) that comprise three habitats. To capture the variability of conservation status within the three habitats, we first performed a random stratified sampling based on conservation status measured in the field, canopy cover, and terrain slope and height. Thereafter, we compared two metric selection approaches, namely Kruskal–Wallis and Dunn tests, and two machine learning classification methods, random forest (RF) and support vector machine (SVM), to classify the conservation statuses using LiDAR and Sentinel-2 data. The best-fit classification model, which included only LiDAR metrics, was obtained using the random forest method, with an overall classification accuracy after validation of 83.01%, 75.51%, and 88.25% for Quercus robur (9160), Quercus pyrenaica (9230), and Quercus faginea (9240) habitats, respectively. The models include three to six LiDAR metrics, with the structural diversity indices (LiDAR height evenness index, LHEI, and LiDAR height diversity index, LHDI) and canopy cover (FCC) being the most relevant ones. The inclusion of the NDVI index from the Sentinel-2 image did not improve the classification accuracy significantly. This approach demonstrates its value for classifying and subsequently mapping conservation statuses in oak groves and other Natura 2000 Network habitat sites at a regional scale, which could serve for more effective monitoring and management of high biodiversity habitats.

Terrain Height Assessment of Satellite Laser Altimetry Standard Products for Natural Resources

Terrain Height Assessment of Satellite Laser Altimetry Standard Products for Natural Resources

Research and Revisions in AS/NZS 1170.2 - 2021

This paper summarises activities in the area of Wind-Structural Engineering in Australia between 2012 and 2021, including the work, carried out for Standards Australia and research at Universities in Australia. The main changes are; the redefinition of wind regions and the recalculation of wind direction multipliers in Section 3, refinement of terrain height, shielding and topographic multipliers in Section 4, inclusion of an open area volume factor for internal pressure and changes to the local pressure factor and area reduction factors in Section 5, new data for along-wind and cross-wind response of slender structures in Section 6, and revised data for curved roofs and new data for open conical roofs and ground mounted solar panel arrays in the Appendices. These revision are based on research carried out in this period.

A deep learning method for predicting lower troposphere temperature using surface reanalysis

Planetary boundary layer (PBL) is formed by the interaction of the atmosphere and the surface and surface conditions have an important influence on the PBL state. This study developed a method to retrieve temperatures in the lower troposphere using temperature reanalysis at 2 m (T2) with a U-net based deep learning model (briefly, lower troposphere retrieval with U-net, LTRUnet). High-resolution four-dimensional data assimilation (FDDA) reanalysis data over Southern China was selected to demonstrate the ability of the deep-learning network. The normalization method and the hyper-parameters of LTRUnet were determined through experiments. The results show that LTRUnet can project the temperatures in the layers below about 1.3 km with good accuracy, with a Mean Absolute Error (MAE) <0.5 K on the test set compared to the reanalysis. The performance of LTRUnet deteriorates with height and has a strong diurnal variation. By utilizing auxiliary physical information of the terrain height and the first-guess temperature (Tb) as additional channels of the LTRUnet input, the error (MAE) of the projected temperature was further reduced by 19.5%. Using the first guess background fields also allows LTRUnet to achieve the same prediction performance with much less training data. Sensitivity tests of the input variables indicate that the topographic information helps the model to better predict the topography-induced temperature distribution, and Tb plays a more significant role in higher levels than T2. Finally, by training LTRUnet independently for different phases of PBL with a physics-guided data grouping strategy, the model results are further improved.

Improved Estimation of O-B Bias and Standard Deviation by an RFI Restoration Method for AMSR-2 C-Band Observations over North America

Spaceborne microwave radiometer observations play vital roles in surface parameter retrievals and data assimilation, but widespread radio-frequency interference (RFI) signals in the C-band channel result in a lack of valuable data over large areas. Establishing repaired data based on existing observation information is crucial. In this study, Advanced Microwave Scanning Radiometer (AMSR)-2 C-band data affected by RFI were accurately repaired through the iterative principal component analysis (PCA) method in 2016 over the U.S. land area. The standard deviation (STD) and bias characteristics of the brightness temperature in the C-band vertical polarization channel were compared and analyzed before and after the restoration to verify the assimilation application prospect of the repaired data. Not only was the spatial continuity of the microwave imager observations significantly improved following restoration; the STD and bias of the observation minus background (OMB) of the restored data were basically consistent with those of the RFI-free data. The STD of OMB exhibited obvious seasonal variations, which were approximately 4.0 K from January to May and 3.0 K from June to December, whereas the biases were near zero in winter but negative (approximately −2.0 K) in summer. The surface type and terrain height also critically affected the STD and bias. The STD decreased with increasing terrain height, whereas the bias exhibited the opposite trend. The STD was largest in low-vegetation areas (4.0 K) but only approximately 2.0–3.0 K in pine forest and brush areas. These results show that the restored data have a high prospect for retrieval application and assimilation, and the STD and bias estimation results also provide a reference for land-based AMSR-2 data assimilation.

A Demonstration of Three-Satellite Stereo Winds

Stereo tracking of clouds from multiple satellites permits the simultaneous retrieval of an atmospheric motion vector (“wind”) and its height in the atmosphere. The direct measurement of height is a major advantage of stereo methods over observations made from a single satellite where the height must be inferred from infrared brightness temperatures. A pair of operational geostationary satellites over the Americas provides stereo coverage where their two fields of view intersect. Stereo coverage can be extended to nearly a full hemisphere with a third satellite. We demonstrate this configuration with the operational GOES-R constellation of GOES-16 (east) and GOES-17 (west) augmented by GOES-18 in its central test slot and use the 500-m resolution Advanced Baseline Imager Band 2. We examine the consistency of the pairwise products created from GOES-18 and -16 versus GOES-18 and -17 and create a fused triple-GOES product that spans nearly the full hemisphere seen from GOES-18. We also examine the retrieval of ground points observed under clear skies and compare their retrievals to zero speed and known terrain heights. The results are compatible with a wind accuracy about 0.1 m/s with height assignment uncertainty of 175 m.

Combined SBAS-InSAR and PSO-RF Algorithm for Evaluating the Susceptibility Prediction of Landslide in Complex Mountainous Area: A Case Study of Ludian County, China.

In complex mountainous areas where earthquakes are frequent, landslide hazards pose a significant threat to human life and property due to their high degree of concealment, complex development mechanism, and abrupt nature. In view of the problems of the existing landslide hazard susceptibility evaluation model, such as poor effectiveness and inaccuracy of landslide hazard data and the need for experts to participate in the calculation of a large number of evaluation factor weight classification statistics. In this paper, a combined SBAS-InSAR (Small Baseline Subsets-Interferometric Synthetic Aperture Radar) and PSO-RF (Particle Swarm Optimization-Random Forest) algorithm was proposed to evaluate the susceptibility of landslide hazards in complex mountainous regions characterized by frequent earthquakes, deep river valleys, and large terrain height differences. First, the SBAS-InSAR technique was used to invert the surface deformation rates of the study area and identified potential landslide hazards. Second, the study area was divided into 412,585 grid cells, and the 16 selected environmental factors were analyzed comprehensively to identify the most effective evaluation factors. Last, 2722 landslide (1361 grid cells) and non-landslide (1361 grid cells) grid cells in the study area were randomly divided into a training dataset (70%) and a test dataset (30%). By analyzing real landslide and non-landslide data, the performances of the PSO-RF algorithm and three other machine learning algorithms, BP (back propagation), SVM (support vector machines), and RF (random forest) algorithms were compared. The results showed that 329 potential landslide hazards were updated using the surface deformation rates and existing landslide cataloguing data. Furthermore, the area under the curve (AUC) value and the accuracy (ACC) of the PSO-RF algorithm were 0.9567 and 0.8874, which were higher than those of the BP (0.8823 and 0.8274), SVM (0.8910 and 0.8311), and RF (0.9293 and 0.8531), respectively. In conclusion, the method put forth in this paper can be effectively updated landslide data sources and implemented a susceptibility prediction assessment of landslide disasters in intricate mountainous areas. The findings can serve as a strong reference for the prevention of landslide hazards and decision-making mitigation by government departments.

Heat flux assumptions contribute to overestimation of wildfire smoke injection into the free troposphere

Injections of wildfire smoke plumes into the free troposphere impact air quality, yet model forecasts of injections are poor. Here, we use aircraft observations obtained during the 2019 western US wildfires (FIREX-AQ) to evaluate a commonly used smoke plume rise parameterization in two atmospheric chemistry-transport models (WRF-Chem and HRRR-Smoke). Observations show that smoke injections into the free troposphere occur in 35% of plumes, whereas the models forecast 59–95% indicating false injections in the simulations. False injections were associated with both models overestimating fire heat flux and terrain height, and with WRF-Chem underestimating planetary boundary layer height. We estimate that the radiant fraction of heat flux is 0.5 to 25 times larger in models than in observations, depending on fuel type. Model performance was substantially improved by using observed heat flux and boundary layer heights, confirming that models need accurate heat fluxes and boundary layer heights to correctly forecast plume injections.

Deep Learning for InSAR Phase Filtering: An Optimized Framework for Phase Unwrapping

Interferometric Synthetic Aperture Radar (InSAR) data processing applications, such as deformation monitoring and topographic mapping, require an interferometric phase filtering step. Indeed, the filtering quality significantly impacts the deformation and terrain height estimation accuracy. However, the existing classical and deep learning-based phase filtering methods provide artefacts in the filtered areas where a large amount of noise prevents retrieving the original signal. In this way, we can no longer distinguish the underlying informative signal for the next processing step. This paper proposes a deep convolutional neural network filtering method, developing a novel learning strategy to preserve the initial phase noise input into these crucial areas. Thanks to the encoder–decoder powerful phase feature extraction ability, the network can predict an accurate coherence and filtered interferometric phase, ensuring reliable final results. Furthermore, we also address a novel Synthetic Aperture Radar (SAR) interferograms simulation strategy that, using initial parameters estimated from real SAR images, considers physical behaviors typical of a real acquisition. According to the results achieved on simulated and real InSAR data, the proposed filtering method significantly outperforms the classical and deep learning-based ones.

A Reduced-Order-Model-Based Motion Selection Strategy in a Leg-Wheel Transformable Robot

This article reports on a model-based methodology for a leg-wheel transformable robot to autonomously determine the use of the wheeled or legged mode based on environmental RGB-Depth information. A concentric and reduced-order dual-leg-wheel model was developed to explore the dynamic interactions between the leg-wheel and terrain. The simulation results revealed that terrain height variation acts as the key factor in determining the model's terrain negotiability; therefore, it was utilized as the decision index for leg-wheel motion selection. To facilitate real-time motion selection on the empirical robot, a two-step selection strategy utilizing RGB-Depth information was proposed. The first step utilized RGB images to classify seven types of terrain. If the classified terrain was potentially rough, the second step utilized depth images to compute the terrain height variation, which was then compared to the index derived by the dual-leg-wheel model to select the final operation mode. The strategy was implemented on a leg-wheel transformable robot and experimentally validated on various types of terrain. The results confirm that the robot's behavior closely resembled the model's prediction, and using the proposed strategy to select leg-wheel motion in the robot yielded the most energy efficient locomotion.

Using Geographic Information to Analyze Wildland Firefighter Situational Awareness: Impacts of Spatial Resolution on Visibility Assessment

Wildland firefighters must be able to maintain situational awareness to ensure their safety. Crew members, including lookouts and crew building handlines, rely on visibility to assess risk and communicate changing conditions. Geographic information systems and remote sensing offer potential solutions for characterizing visibility using models incorporating terrain and vegetation height. Visibility can be assessed using viewshed algorithms, and while previous research has demonstrated the utility of these algorithms across multiple fields, their use in wildland firefighter safety has yet to be explored. The goals of this study were to develop an approach for assessing visibility at the handline level, quantify the effects of spatial resolution on a lidar-driven visibility analysis, and demonstrate a set of spatial metrics that can be used to inform handline safety. Comparisons were made between elevation models derived from airborne lidar at varying spatial resolutions and those derived from LANDFIRE, a US-wide 30 m product. Coarser resolution inputs overestimated visibility by as much as 223%, while the finest-scale resolution input was not practical due to extreme processing times. Canopy cover and slope had strong linear relationships with visibility, with R2 values of 0.806 and 0.718, respectively. Visibility analyses, when conducted at an appropriate spatial resolution, can provide useful information to inform situational awareness in a wildland fire context. Evaluating situational awareness at the handline level prior to engaging a fire may help firefighters evaluate potential safety risks and more effectively plan handlines.

Comparative Analysis of the Observation Bias and Error Characteristics of AGRI and AHI Data for Land Areas in East Asia

Observation bias and error characteristics are the preconditions for the effective assimilation of observation data. In this paper, the bias and error characteristics of the AHI (Advanced Himawari Imager (AHI) and AGRI (Multi-Channel Advanced Geostationary Orbit Radiation Imager (AGRI) data are compared and analyzed, with an emphasis on the observations obtained from land areas. The results show that the observation errors of the two instruments for the ocean area have a good channel consistency over ocean areas, which are all with errors of about 0.6 K; however, the bias and error are significantly affected by the land-surface types and terrain heights over land. For most of the AHI channels, the bias in urban-area bias is smaller than that of those of other surface types, while that of the AGRI data exhibits just the opposite trend, with obviously larger biases in urban areas. However, the observation errors of these two instruments in urban areas are significantly smaller than those of other surface types. The biases of the two instruments do not extensively change much with the terrain height, only slightly decreasing when the height is above 1000 m; however, the observation errors increase obviously with the increase of terrain heights. The difference between the two instruments is that the observation error of the AHI data tends to be stable and stabilizes above 1000 m, while that of the AGRI data is relatively stable below 500 m. The observation errors of the CO2-sensitive channels of the two instruments over the land areas are obviously smaller than those of other near-surface channels, which may indicate that the data obtained in these two CO2 channels have good application prospects for assimilation over land.

A Physically Based Statistical Model With the Parameterized Topographic Effect for Predicting the Weakening of Tropical Cyclones After Landfall Over China

AbstractAccurate prediction of tropical cyclone (TC) intensity is important but challenging. In this study, a physically based algebraic decay model for predicting TC weakening after landfall over China is introduced, which assumes the TC weakening rate is proportional to the square of the TC maximum near‐surface wind speed. In this algebraic decay model, a decay parameter including the topographic effect by modifying the surface drag coefficient with the normalized terrain height is determined by minimizing the forecast errors for all landfalling TCs over mainland China during 1980–2020. Results show that the algebraic decay model with topographic effect considered performs better than the commonly used exponential decay model for TCs after landfall over mainland China, especially when TCs move further inland. This new model has a time‐dependent decay parameter along the TC track due to the topographic variation, which is different from the previous exponential decay model.

A vibration-powered self-contained node by profiling mechanism and its application in cleaner agricultural production

The vibration-based electromagnetic energy harvester offers a cost-effective and promising renewable energy approach. With the rapid development of autonomous agricultural production, advanced agricultural machinery equipped with multiple sensors is of significant importance. In the hilly areas of southwest China, the driver of the pennisetum sinese roxb harvester must frequently adjust the height of the stripping header to adapt to the terrain height variation and it leads to high labor intensity. Because of the harness environment of the working condition, the arrangement of wires for sensors is almost impossible. Thus, a self-powered sensor node by profiling mechanism with a vibration energy harvester is proposed. This paper presents theoretical and experimental research on a profiling mechanism using a vibration energy harvester, with which a detailed theoretical model is established to perform the dynamic simulation study. Diverse excitation conditions such as sinusoidal sweeping vibration tests, fixed frequency vibration and stochastic road spectrum tests of agricultural machinery were implemented to verify the power generation capacity. The simulation results agree well with the experimental data. Experimental results show the peak-peak voltage and power of the proposed device reach about 44 V and 113 mW under low frequency and large displacement excitations. The vibration test justifies the feasibility of the proposed profiling mechanism in the application scene for powering the range sensor applied in agricultural production.

Measuring Vegetation Heights and Their Seasonal Changes in the Western Namibian Savanna Using Spaceborne Lidars

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with its land and vegetation height data product (ATL08), and Global Ecosystem Dynamics Investigation (GEDI) with its terrain elevation and height metrics data product (GEDI Level 2A) missions have great potential to globally map ground and canopy heights. Canopy height is a key factor in estimating above-ground biomass and its seasonal changes; these satellite missions can also improve estimated above-ground carbon stocks. This study presents a novel Sparse Vegetation Detection Algorithm (SVDA) which uses ICESat-2 (ATL03, geolocated photons) data to map tree and vegetation heights in a sparsely vegetated savanna ecosystem. The SVDA consists of three main steps: First, noise photons are filtered using the signal confidence flag from ATL03 data and local point statistics. Second, we classify ground photons based on photon height percentiles. Third, tree and grass photons are classified based on the number of neighbors. We validated tree heights with field measurements (n = 55), finding a root-mean-square error (RMSE) of 1.82 m using SVDA, GEDI Level 2A (Geolocated Elevation and Height Metrics product): 1.33 m, and ATL08: 5.59 m. Our results indicate that the SVDA is effective in identifying canopy photons in savanna ecosystems, where ATL08 performs poorly. We further identify seasonal vegetation height changes with an emphasis on vegetation below 3 m; widespread height changes in this class from two wet-dry cycles show maximum seasonal changes of 1 m, possibly related to seasonal grass-height differences. Our study shows the difficulties of vegetation measurements in savanna ecosystems but provides the first estimates of seasonal biomass changes.

Research on Terrain Sensing Method and Model Prediction for Height Adjustment of Sugarcane Harvester Base Cutter

The sugarcane plantations in China occupy large areas of undulated terrain. During the operation of a sugarcane harvester, the operator’s vision is severely obstructed, and the height of the cutter needs to be adjusted frequently, resulting in low sugarcane harvesting efficiency and difficulty in ensuring harvesting quality. However, the current diversified non-contact methods are unable to distinguish or penetrate the interference well to provide feedback regarding the change in terrain height. Based on the proposed contact sensing method, a contact terrain sensing device is designed for application to the sugarcane harvester, and the orthogonal test is carried out with the standard orthogonal table L9(34) to study the influence of the working parameters of the device on the terrain height sensing performance. Through the analysis of the working principle of the device, three factors (moving speed, rotational torque, and preload) are tested in three levels, and index systems of the Fréchet distance and residual standard deviation are proposed. The range and variance analysis methods are used to statistically analyze the test results to determine the primary and secondary orders of operation parameters. Then, the regression model was obtained by regression analysis on the influencing factors. After analysis, the influences of moving speed and rotating torque were found to be the most significant, and the influence of preload was the smallest. The results show that the average relative errors between the experimental values and predicted model values of the Fréchet distance and residual standard deviation were 3.46% and 2.48%. According to the practical applications of the sugarcane harvester and the data of the two indexes, the optimal value ranges of moving speed (km/h) and rotating torque (Nm) are determined as (1.57, 1.75) and (0.73, 0.86), respectively, which provide a certain reference for the subsequent application of the terrain sensing device to sugarcane harvesters.