245 results about "Trafficability" patented technology

The invention belongs to the technical field of path planning, and discloses a path planning method and system fusing multi-modal terrain perception and vehicle dynamics, and the method comprises the steps: obtaining and fusing multi-modal geographic data to generate a terrain physical representation, and calculating the terrain complexity based on the terrain physical representation; predicting an overturning index and a stability margin of the vehicle based on the terrain physical representation, and generating a trafficability cost function; taking the trafficability cost function as a constraint condition, selecting a motion element according to terrain complexity to perform trajectory fitting, and generating a vehicle path; executing the vehicle path to obtain a vehicle state, correcting the trafficability cost function by using the vehicle state, and optimizing the vehicle path; and constructing a loss function, training the path planning model, and performing path planning by using the trained model. According to the invention, by establishing a terrain vehicle bidirectional coupling mechanism, safe and adaptive local path planning under a complex unstructured terrain is realized, and the traffic capacity and safety of the system in a dynamic environment are significantly improved.

The invention discloses a vehicle trajectory planning method and device and a vehicle, and relates to the technical field of intelligent driving. The method comprises the following steps: obtaining vehicle state information of a target vehicle, a reference trajectory and environment perception data containing available traffic width, and performing trajectory optimization processing according to the vehicle state information, the reference trajectory and the environment perception data to obtain a trajectory optimization result of the reference trajectory; in the trajectory optimization processing process, taking a first passing cost required for minimizing a trajectory optimization result as a target; a passing width cost item in the first passing cost is a power function taking the residual passing width as an independent variable, and the passing width cost item is increased along with the reduction of the residual passing width; the remaining passing width is the difference between the available passing width and the vehicle width of the target vehicle. Therefore, in a narrow traffic scene, the traffic width cost item is increased in a super-linear manner, the constraint strength on the transverse position of the track is enhanced, the generation of a high-risk track excessively close to an obstacle is effectively avoided, the planning interruption frequency is reduced, and the trafficability of the vehicle in the narrow scene is remarkably improved.

The invention relates to the technical field of intelligent traffic, in particular to an intelligent networked PHEV multi-driving-mode autonomous decision control method based on deep learning. The method comprises the steps of designing a state space and an action space based on a deterministic strategy gradient algorithm by constructing three driving modes of a cruise scene, a car-following scene and a passing scene; a multi-target award function is constructed for driving safety, riding comfort and energy consumption economy, a Markov decision process is utilized to construct a multi-target driving mode autonomous decision control model, and it is ensured that when the intelligent network connection PHEV is driven in a complex environment, the optimal target vehicle speed can be planned by autonomously deciding different driving modes. The comprehensive optimization of safety, trafficability and energy consumption economy of the intelligent networked PHEV in the driving process is realized, and the decision-making ability of the intelligent vehicle in a complex traffic environment is further improved.

The invention discloses an intelligent road surface recognition method based on force perception and image analysis. The method comprises the following steps: S1, acquiring image data and contact force data in the advancing process of a mobile robot; s2, inputting the image into a two-channel feature network, and extracting structural features and fuzzy region information; s3, processing through an unwrapping model to form an image semantic vector; s4, constructing a contact time sequence, extracting behavior characteristics, and generating a contact event code; s5, performing alignment and weighted fusion on the image semantic vector and the contact code, and outputting a combined pavement state vector; s6, inputting a trafficability scoring model, identifying a ground type and generating a trafficability score; and S7, transmitting the result to a navigation module for path planning and motion control. Visual and force sense information is combined, and the complex terrain recognition accuracy and the robot traffic capacity are improved.

The invention provides a method and device for calculating the trafficability of a line under the operation of a heavy haul railway train group, and relates to the technical field of train group analysis, and the method comprises the steps: calculating an interval time parameter under the operation of the train group; drawing a train interval working diagram; constructing a single-line working diagram cycle model based on the train interval working diagram and the interval time parameters; constructing a train group tracking model of the double-track heavy haul railway under train group operation based on the interval time parameters; linearizing and then solving the single-line working diagram cycle model to obtain a minimum limit interval working diagram cycle, and calculating the trafficability of the single-line heavy haul railway line under the operation of the train group based on the minimum limit interval working diagram cycle; and according to the train group tracking model, calculating the trafficability of the double-track heavy haul railway at different stages under the train group operation. The method solves the problem that in an existing line trafficability calculation method, the particularity of a group operation organization method is not considered, and the method is not suitable for capacity calculation under group operation.

The invention discloses a gravity center self-adjustment control system and method based on an electric balance car, and relates to the technical field of electric balance car control, and the gravity center self-adjustment control system comprises a multi-source sensing module, a control decision module, a multi-mode execution module and a safety control module. According to the invention, attitude, pressure, motion and environment data are fused in real time through the multi-source sensing module, three types of instability risks of sudden lateral offset, continuous slope offset and load distribution abnormity are accurately identified, and the control decision module intelligently starts differential compensation, wheel track adjustment or counterweight adjustment according to risk types. The multi-mode executing mechanism can cope with composite unbalance in a cooperative mode, the safety control module adopts a dynamic threshold value to adjust intervention sensitivity, multiple guarantees are formed in combination with a fault diagnosis and speed reduction parking mechanism, and finally the control burden of a user is relieved while the trafficability of complex road conditions is improved.

The invention discloses a low-speed unmanned vehicle-oriented obstacle avoidance detouring method, which comprises the following steps of: inputting and acquiring vehicle surrounding sensing environment information, static map information, vehicle real-time pose information and vehicle real-time state information in real time, and preprocessing to generate a passable cost map; generating a triggering detour logic, and entering a detour planning logic when a condition is satisfied according to a preset detour logic; generating a target bypassing distance; acquiring a bypassing target point according to the target bypassing distance; and according to the detour target point and the passable cost map, planning a detour track according to a preset planning algorithm. According to the invention, a passable area cost map is generated according to input information processing, and finally an obstacle avoidance detouring track is planned. Through the above steps, the unmanned vehicle can generate and execute a safe and effective bypassing track so as to avoid obstacles and continue to execute tasks, and the scene trafficability of the unmanned vehicle is improved.

The invention relates to a road understanding method and device under the conditions of satellite denial and no prior map. The method comprises the following steps: generating global guide information by using a spline curve according to satellite map marking points; fusing the laser radar point cloud data to construct a three-dimensional terrain model, extracting roughness and a normal vector to obtain a corresponding graph, and constructing an environment trafficability graph; then, defining a credibility value for each graph, fusing a roughness graph and a normal vector graph evidence, and carrying out basic probability distribution fusion with a trafficability graph to obtain final data; determining a passable area based on the global guidance information and the final data, estimating the width of the passable area, and calculating the current position, angle expectation and standard deviation in combination with the local guidance information; and finally, calculating posteriori estimation by taking the distance as a measured value, and analyzing a boundary, so as to obtain a corrected guide path and a passable area. By adopting the method, the path generation accuracy can be improved.

The invention discloses a vehicle and a control method thereof, and relates to the technical field of vehicles. According to the method, when the vehicle needs to climb the steps, the height of the suspension of each wheel can be increased, so that the trafficability of the vehicle during climbing the steps is improved, and damage to a chassis of the vehicle is reduced to a certain extent. In the step climbing process, the method can reduce the suspension height of the target wheel along with the increase of the climbing height of the target wheel. After the suspension height of the target wheel is reduced, the difficulty of the target wheel climbing steps can be reduced, and it can be ensured that a vehicle body of the vehicle is stable. Therefore, the vehicle can pass through the steps safely and efficiently.

In this application, when an intent of the object is indeterminate, the intent probability of the intent-indeterminate object is inferred by using observed information, and indeterminate interactive game decision-making is performed based on the intent probability, to work out an optimal action of the ego vehicle. In this way, the ego vehicle can properly respond to a jump of an intent of a dynamic object, to reduce improper pulsating brakes and unwanted grab, thereby improving comfort, safety, and trafficability of the ego vehicle.

The invention relates to the technical field of railway intelligent transportation, in particular to a railway station trafficability calculation and bottleneck identification method and system, and the method comprises the steps: obtaining various travelling equipment data of a target railway station in real time; constructing a mathematical model for station trafficability calculation based on a space-time network theory, converting the selection of a train on a route and an arrival-departure line into the selection of a segmental arc and a path of the train in a space-time network, and calculating the station trafficability; the utilization rate of each device in the arrival and departure site and the throat area is calculated, and the station trafficability bottleneck is identified, that is, the main device limiting the station trafficability is searched; a mathematical model for optimizing a train route distribution scheme is constructed with the purpose that the utilization rates of equipment such as arrival and departure lines and turnouts are balanced as much as possible, so that a train operation route scheme under the equipment balanced application condition is determined, and the station capacity bottleneck is relieved. According to the method, intelligent measurement and calculation of the overall trafficability of the arrival and departure site, the throat area and the station can be realized, and meanwhile, a train route scheme is optimized.

The invention discloses an unmanned vehicle and unmanned aerial vehicle cooperative distribution method in a complex field environment. The method comprises the following steps: acquiring data of ground terrain parameters; based on the initial obstacle density evaluation, high-density obstacle areas which are not suitable for vehicle passing are eliminated; obstacle recognition constraint is carried out by using a Transform model, obstacle features are confirmed, and constraint calculation is carried out; calculating a trafficability score of each region; calculating the trafficability score and the passing time of the path by using a bilevel programming model, and finding out an optimal path; when the path of the unmanned vehicle is not feasible, the unmanned aerial vehicle takes over the task and generates an optimal path according to the surrounding environment and the distribution distance; the map generation module generates a map for displaying the optimal path; and the unmanned aerial vehicle automatically takes goods and completes a distribution task according to the map. According to the invention, by combining the cooperative capability of the unmanned vehicle and the unmanned aerial vehicle, the limitation of terrains and obstacles on transportation path selection in a complex environment is effectively solved, and the transportation efficiency and the traffic safety are improved.

The invention discloses an unstructured environment motion planning method, device and equipment, and relates to the field of automation technology and vehicle engineering, and the method comprises the steps: inputting a to-be-tested unstructured environment pavement topographic image into a neural network model, determining the probability of any terrain type to which each pixel in the to-be-measured unstructured environment pavement topographic image belongs; adopting a Bayesian regression model to determine comprehensive passable risks under different road surfaces according to the probability of any terrain type to which each pixel belongs; an entropy risk measurement method is adopted to convert the comprehensive passable risks under different road surfaces into quantitative risks, and a passable map is generated; according to the trafficability map, determining trafficability parameters of the motion planner based on the cross entropy; according to the method, a motion planner based on cross entropy is utilized to perform motion planning on a vehicle in a to-be-detected unstructured environment according to the trafficability parameters, so that comprehensive probabilistic evaluation on terrain risks is realized, and the defects of uncertainty of terrain perception and terrain adaptability are overcome.

The invention relates to the technical field of unmanned vehicle scheduling, in particular to a park networking unmanned vehicle dynamic scheduling planning method and system, and the method comprises the steps: collecting dynamic obstacle data through a multi-source sensor and V2X equipment, generating a four-dimensional trafficability map, formulating an avoidance strategy weight, optimizing a multi-vehicle track, and carrying out the optimization of a multi-vehicle track. And evaluating a threat value to trigger the reconfiguration of the resident area by combining the edge node and the cloud adjusting path. The system comprises a sensing module, a decision-making module, a storage module, an allocation module, a regulation module and a verification module, and an exception handling module is linked with a threat value change rate to optimize resource allocation. According to the method, the avoidance strategy can be dynamically adjusted according to the threat value, the scheduling accuracy and the resource allocation accuracy are improved, the vehicle safety is guaranteed, and the task failure rate is reduced.

The invention discloses a rugged environment improved A * algorithm path planning method based on an elevation map, and relates to the technical field of path planning. The method comprises the following steps: establishing a ground trafficability model and a ground rugged model which conform to the characteristics of a rugged environment terrain based on an elevation map, further identifying the passing difficulty and wave degree of the ground, and then designing an elevation cost function capable of obtaining an optimal path according to the two types of models in combination with the main process of an A * algorithm. And finally, a continuous cost function is established in combination with an original distance cost function, the path optimality is improved, and an optimal route is selected. The improved algorithm enhances the stability and optimality of the planned path of the mobile robot in the rugged environment, and improves the autonomous navigation performance of the mobile robot in the rugged environment.

The invention belongs to the field of agricultural mechanization equipment, and provides greening auxiliary planting equipment based on different mountainous area site conditions, the greening auxiliary planting equipment comprises a hand-held device and a walking device, the walking device comprises a front wheel mechanism, a rear wheel mechanism and a bottom bridge, and a pair of ground gripping devices is arranged at the tail of the bottom bridge; an intestine-shaped vehicle body is arranged above the front wheel mechanism and the rear wheel mechanism, a rotary clamping device is arranged at the front end of the intestine-shaped vehicle body and comprises a plurality of lower clamping openings distributed at intervals, a tail hoop is arranged at the position, close to the rear end, of the intestine-shaped vehicle body, and the two sides of the tail hoop are each provided with a telescopic device. The telescopic device comprises a fixed cantilever, a movable cantilever, a plurality of upper clamping pieces and a centralizing clamping piece, and a control device used for controlling the telescopic device to stretch out and draw back is arranged on the movable cantilever and the handheld device. The device is reasonable in design, good in moving flexibility, good in mountainous region trafficability and beneficial to auxiliary planting and improvement of operation efficiency.

The invention provides a vehicle path planning method and device. The method comprises the following steps: acquiring vehicle parameters, environment sensing data, a path starting point and a path ending point; generating an upper-layer obstacle grid map and a lower-layer obstacle grid map based on the vehicle parameters and the environment perception data; performing collision detection based on the vehicle parameters in the upper-layer obstacle grid map and the lower-layer obstacle grid map to obtain an upper-layer collision detection result and a lower-layer collision detection result; combining the upper-layer collision detection result with the lower-layer collision detection result to generate a planned path from the path starting point to the path ending point; the planned path comprises obstacle-avoiding nodes for bypassing the upper-layer obstacle and obstacle-crossing nodes for crossing the lower-layer obstacle. Therefore, efficient path searching in the multi-obstacle complex scene is realized, and the vehicle has better trafficability in the multi-obstacle complex scene.

The invention relates to the technical field of vehicle control, and provides a vehicle tire pressure adjusting method and a vehicle. Vehicle running data and a road topographic image in front of the vehicle are obtained, so that the vehicle trapping risk of the vehicle is predicted, and vehicle trapping probability information of the vehicle is obtained; according to the method, the vehicle trapping risk of the vehicle can be predicted in advance, the accuracy of vehicle trapping risk prediction is ensured through environmental perception and operation data, and the vehicle trapping risk prediction accuracy is improved. The tire pressure adjusting quantity is determined in time according to the vehicle sinking probability information and the vehicle operation data, so that the tire pressure adjusting accuracy is ensured, the purpose of timely intervention before the vehicle sinking is achieved, the vehicle sinking caused by mismatching of the tire pressure under the complex road conditions such as sand and mud is avoided, the vehicle escaping capability is improved, and the vehicle safety is improved. And the trafficability of the vehicle under various complex road conditions is improved.

The invention relates to an unmanned vehicle trafficability analysis method based on online self-supervised learning. The method comprises the following steps: generating BEV data of an environment according to related data obtained by each sensor in real time and subjected to time synchronization processing; a ResNet18-UNet model is constructed, and a loss function is set; performing deep feature extraction on BEV data according to a trained ResNet18-UNet model, periodically recording historical feature vectors corresponding to a left wheel and a right wheel by taking a certain distance as an interval in the driving process of the unmanned vehicle at a historical moment, performing unsupervised clustering on the historical feature vectors, and obtaining a clustering result; and calculating the cosine similarity between each feature vector in the road surface related feature vector diagram and all feature points in the high-level feature vector diagram at the current moment, mapping the obtained similarity value to a gray scale range from 0 to 255, generating a similarity image, and predicting the trafficability of the unmanned vehicle according to the similarity image. By adopting the method, the accuracy of unmanned vehicle trafficability analysis can be improved.

The invention relates to the technical field of geographic information systems, in particular to an incomplete road network repairing and reconstructing method based on AI recognition. Comprising the following steps: S1, road surface data geometric restoration and road width calculation; s2, reconstructing a target road network topology; s3, road attribute matching; and S4, topology simplification and output. According to the method, through the dynamic distance threshold value and topology verification, broken roads are intelligently connected, the disconnection phenomenon is eliminated, a topology connected road network is constructed, and a reliable data basis is provided for path planning. The width is calculated through correlation between the road surface and the center line, and the external road network attributes are fused, so that the output road network has key information such as the road width and grade, and the trafficability evaluation requirement in mechanical construction is met.

The invention discloses a visual analysis method and equipment for the trafficability of a grain transport vehicle tractor track and a storage medium. According to the method, images are collected through a vehicle-mounted binocular vision system, and multi-dimensional geometric attributes of the terrain and obstacles are automatically extracted through semantic segmentation, three-dimensional reconstruction and voxel modeling. And on the basis of the extracted multi-dimensional attributes, constructing a hierarchical evaluation system containing terrain and obstacle criteria. A local weight of each attribute index is determined by adopting an analytic hierarchy process, and a fuzzy membership degree vector of each index on different passage evaluation grades is constructed according to dynamic judgment parameters adaptively set with the current grain transport vehicle model number and the terrain, so that a membership degree matrix is formed. And finally, through hierarchical fuzzy comprehensive evaluation, calculating to obtain a comprehensive trafficability score. The accuracy, reliability and automation level of tractor track trafficability evaluation are improved.

The invention relates to the technical field of agricultural machinery intelligent control, in particular to a combine harvester track trafficability control method based on visual inspection, which comprises the following steps: S1, predicting a motion track based on the real-time traveling speed and steering angle of a harvester; s2, extracting a real-time terrain slope angle of the trajectory region; s3, determining a target grounding pressure distribution mode required by the track in the predicted track area; s4, calculating a target hydraulic flow distribution proportion used for controlling a walking motor; and S5, the hydraulic valve group is controlled to adjust the oil supply amount according to the target flow distribution proportion. Through linkage of visual perception and hydraulic control, segmented self-adaptive adjustment of the track grounding pressure is achieved, and the trafficability and operation stability of the combine harvester under complex terrains are effectively improved.

The invention provides a vehicle remote control driving system and method integrating AR virtual path projection, and relates to the technical field of vehicle remote control. The system constructs a high-precision three-dimensional environment model through cooperative work of an environment sensing module and a data processing and decision module; the AR path generation unit generates a collision-free virtual path and a vehicle dynamic rehearsal picture, and the collision-free virtual path and the vehicle dynamic rehearsal picture are visually presented in an augmented reality mode through the remote control terminal outside the vehicle, so that a driver can obtain comprehensive situation awareness beyond a physical visual field outside the vehicle; and after the virtual path is confirmed or adjusted in combination with the interaction control, the vehicle control strategy unit automatically generates a cooperative control command adapting to the road condition, and an execution mechanism is driven to perform accurate execution. According to the system, the safety and operation confidence of remote control driving are improved, the blind area collision risk is effectively avoided, the trafficability of the vehicle in a complex environment is enhanced, meanwhile, the operation difficulty is greatly reduced through AR visual interaction, and intelligent and user-friendly vehicle exterior remote control driving experience is achieved.

The invention discloses a turning trajectory optimization method for a trailer, which comprises the following steps: firstly, judging whether a vehicle needs trajectory optimization, secondly, obtaining an initial input boundary of trajectory optimization based on the current position information of the vehicle, and obtaining an input boundary after an obstacle is processed by combining the longitudinal distance of the center of the obstacle in a frenet coordinate system; based on the input boundary, the turning radius of the trailer during turning and the leftmost end face and the rightmost end face of the trailer are combined, the input boundary considering trailer movement is obtained, and the safety distance of the inner side of the trailer and the safety distance of the outer side of the trailer are obtained through calculation; searching the safe distance between the inner side and the outer side calculated in the previous step on the basis of the curvature, and adding the safe distance into an input boundary after the obstacle is processed; and according to the finally obtained boundary, inputting the boundary into a solver to obtain a final collision-free track considering the trailer motion during turning. The stability, safety and trafficability of the automatic driving system can be greatly improved.

Disclosed is an in-situ detection robot for loess geological information, including a housing, an active tensioning system, an auxiliary tensioning system, a control system, a drive system, and a detection system. The active tensioning system is configured to provide the robot with a tensioning force in an aperture direction of a loess hole for the robot to travel in the loess hole. The auxiliary tensioning system is configured to provide the robot with a pre-tensioning force for the robot to travel in the loess hole. The control system is configured to control a movement mode of the robot according to different travelling environments of the robot in the loess hole. The driving system is configured to provide the robot with a travelling power in the loess hole. The detection system is configured to detect related parameters of the loess geological information. The robot according to the present disclosure has a simple structure, can adapt to an aperture change of 100-150 mm, can effectively solve the problem in which the crawler pipe robot has a large radial size, has strong obstacle crossing ability and trafficability performance, and is applicable to the detection of geological information in the loess hole, which fills the existing technical gap.

The invention relates to the technical field of logistics management, in particular to a logistics order transportation management and control system based on digital driving, and the system comprises a data collection module which is used for collecting order data of a logistics order, environment data of an initial path and traffic data, carrying out the preprocessing of the collected data, and carrying out the fusion construction of a basic data set after the processing; and the path evaluation module is used for carrying out trafficability risk evaluation on the initial path based on the basic data set and judging whether to adjust the initial path based on an evaluation result. According to the invention, through cooperative work of the data acquisition module, the path evaluation module and the path planning module, full-process digital management of logistics transportation is realized; by utilizing dynamic path planning and multi-dimensional risk assessment, the influence of extreme weather on logistics transportation is effectively solved, and the transportation timeliness is guaranteed, especially under the conditions of road closing, icing and the like in snowy weather.

According to the suspension system self-adaptive adjustment method, device and equipment and the readable storage medium, data from a plurality of sensors are obtained in real time, the sensors monitor the driving state of a vehicle, and on the basis of the sensor data obtained in real time, by means of a pre-trained suspension parameter prediction algorithm model, the self-adaptive adjustment of the suspension system can be achieved according to the current driving state. According to the method, the optimal matching suspension parameter is predicted, and the current setting of the suspension system is automatically adjusted according to the predicted suspension parameter so as to ensure that the suspension system can adapt to the running state of the vehicle in real time, and the optimal controllability, comfort and trafficability are provided. By adjusting suspension parameters in real time, the system can ensure that the suspension system is always in an optimal working state, so that the controllability, comfort and trafficability of a vehicle are remarkably improved; therefore, a driver can concentrate on driving without worrying whether the suspension system adapts to the current driving state or not, and more stable and comfortable driving experience is provided for the driver.

The invention discloses a front-wheel steering vehicle autonomous exploration method and system suitable for a complex terrain, and the method comprises the steps: 1, obtaining a global rasterized terrain plane map according to original point cloud data; step 2, carrying out trafficability cost evaluation on the global rasterized terrain plane map to obtain a global trafficability cost map and a leading edge point; step 3, dividing a set direction area according to the orientation and steering capability of the vehicle, calculating leading edge gathering points in the set direction by using a geometric analysis algorithm, and obtaining a sparse topological graph of the leading edge points; step 4, local planning: selecting a frontier aggregation point with the lowest navigation comprehensive cost from local frontier aggregation points in the frontier point sparse topological graph as a local planning target point of the current period, and obtaining an initial path by using an improved A * algorithm; and 5, performing global planning. According to the method, the interaction between the vehicle and the terrain can be fully considered, a real local terrain traversal map is generated, and the vehicle can completely and autonomously explore a large outdoor unknown area.

The invention belongs to the technical field of data processing, and discloses a Mars rover non-contact trafficability estimation method and system based on thermal inertia, and the method comprises the steps: obtaining a ground surface temperature time sequence and environment parameters of a target region; constructing a thermal inertia estimation model, and inputting the earth surface temperature time sequence and the environmental parameters into the thermal inertia estimation model to obtain a thermal inertia estimation value of the fire soil; constructing a thermal inertia and trafficability index relation model, inputting the thermal inertia estimation value of the fire soil into the thermal inertia and trafficability index relation model, and outputting a predicted trafficability index; and determining a trafficability state grade according to the predicted trafficability index, and generating a navigation control suggestion, so that the Mars rover passes through the target area according to the Mars rover navigation control suggestion. According to the method, the trafficability index of the Mars rover is predicted by extracting the thermal inertia estimation value of the fire soil in the target area, the evaluation precision of the Mars rover passing through the target area can be effectively improved, and the driving safety of the Mars rover is ensured.