200 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 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 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 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.

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 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 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 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 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.

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.

The invention discloses an emergency rescue path planning method, system and device based on building earthquake damage simulation and a medium, and relates to the field of rescue path planning, and the method comprises the steps: building a building three-dimensional structure based on a two-dimensional plane graph of a building with an earthquake disaster; processing the three-dimensional structure of the building by adopting a physical engine to obtain a building damage and pavement accumulation result under an earthquake; performing indoor and outdoor integrated road network extraction on building damage and pavement accumulation results under an earthquake by adopting a navigation grid method to obtain a road network extraction result; and performing path planning based on the road network extraction result by adopting an optimized A * algorithm to obtain an emergency rescue path planning result. According to the method, the requirements of evacuation safety, trafficability and traffic capacity constraint and strain can be met, and indoor and outdoor integrated path planning service is provided for search and rescue personnel so as to respond to the optimal rescue path requirement of emergency.

The invention relates to the technical field of unmanned vehicle path planning, and provides a path planning method suitable for an unmanned vehicle in a field scene, and the method comprises the steps: carrying out the evaluation of a trafficable terrain, and obtaining the trafficability of the current ground; a global reference path is generated in combination with the trafficability and an improved Inform-RRT * algorithm; performing trajectory fitting on the global reference path by adopting a Bezier curve to generate a smooth path; and local planning and tracking control are performed based on a kinematic model in combination with the smooth path and obstacle information. According to the path planning framework provided by the invention, the trafficability of the terrain and the dynamic characteristics of the robot are considered, so that the method better meets the actual application requirements, and the operation efficiency and reliability of the unmanned vehicle in the complex terrain are improved.

The invention provides an air-ground collaborative navigation method based on a hierarchical visual language action model, which comprises the following steps that: a ground robot and an air unmanned aerial vehicle respectively acquire relevant data and send the relevant data to a central processing system for processing; a global path planning module of the central processing system generates a coarse-grained global path key point sequence, namely a sparse path point sequence, based on the processed and aligned aerial top view image, and then a local path planning module of the central processing system generates dense local path points between adjacent sparse path points based on the ground view image; and a capability perception evaluation module of the central processing system evaluates the trafficability of each output candidate path according to the physical capability of the ground robot so as to provide a basis for final path selection. The method effectively solves the problems that a traditional air-ground collaborative framework lacks quantitative evaluation and closed-loop feedback mechanisms for physical capabilities of ground robots and does not make full use of global environment information to carry out robot passage planning and the like.

The invention discloses an unmanned engineering machinery steer-by-wire system and a control method in the technical field of vehicle chassis and steering systems. The unmanned engineering machinery steer-by-wire system comprises four wheels and steering modules, wherein the steering modules are in one-to-one correspondence with the four wheels, are completely independent and have the same structure; the corner deflection of the four wheels is cooperatively controlled through a distributed electronic control system; and each wheel is driven by a hub motor. Through four-wheel steering, the minimum turning radius can be 0 m, the trafficability in narrow roadways, sidewalks and obstacle dense areas is improved, meanwhile, four-wheel steering is independently controlled, special movement forms such as transverse translation and oblique advancing are supported, and the four-wheel steering robot is particularly suitable for high-precision operation requirements such as urban road edge stone attaching and sweeping; four-wheel independent main pin steering is combined with a hub motor and distributed redundancy control, so that the reliability, the function safety and the maintainability of the system are remarkably improved, and the system is particularly suitable for unmanned engineering machinery to work for a long time in narrow, complex and severe environments.

The utility model relates to an aerial work platform which comprises a main platform unit, the main platform unit comprises a main work platform and a first guardrail assembly, the first guardrail assembly is arranged on the edge of the main work platform in a surrounding mode, and the first guardrail assembly extends in the height direction; the anti-collision unit comprises an anti-collision rod assembly, and the anti-collision rod assembly is assembled on the first guardrail assembly; the anti-collision rod assembly comprises an upper structure, a lower structure and an induction piece, one end of the upper structure is rotationally connected with the lower structure, and the induction piece is installed at the other end of the upper structure. During working, the induction piece is driven by the upper structure to move to a high point. According to the anti-collision structure, the lifting action of the working platform can be stopped in time after a top obstacle is detected, safety accidents are effectively avoided, good top anti-collision protection measures are provided for operators, the anti-collision structure can be folded and stored conveniently in a non-working state, and therefore the trafficability of the whole machine in a stored state cannot be affected.

This invention provides a vision-based self-supervised off-road terrain accessibility estimation method. This method utilizes measurement data from an onboard inertial measurement unit to generate self-supervised access cost labels, enabling the predicted terrain cost to reflect the dynamic response characteristics of the vehicle during driving. Furthermore, these labels do not require manual annotation, saving the cost of training data labeling. Images containing rich terrain features are used as input perceptual information for the prediction network, and a contrastive learning method is used to pre-train the backbone network that extracts multi-scale features from the images, improving the reliability and accuracy of off-road terrain accessibility estimation.

The invention provides a lane center line generation method and device, electronic equipment and a vehicle, and relates to the technical field of intelligent driving, and the method comprises the steps: obtaining a single communication path according to a lane topology network, carrying out the aggregation of segmented lanes in the single communication path, obtaining at least one aggregated lane in the single communication path, and carrying out the aggregation of the at least one aggregated lane in the single communication path; determining passing points of each aggregation lane, connecting the passing points of each aggregation lane, generating a lane center line of each aggregation lane, and splicing and fitting the lane center line of each aggregation lane to obtain a target lane center line; according to the method, the lane center line of each aggregation lane is spliced and fitted to obtain the target lane center line, so that the continuity, smoothness and trafficability of the finally generated target lane center line are ensured, and the intelligent driving safety and the user driving experience are improved.

The invention discloses a path planning method based on a city engine and an unreal engine. The method comprises the following steps: completing basic data preparation, rule definition, model generation and visual presentation and analysis, and establishing a city model; secondly, constructing a vehicle motion model in an unreal engine, and simulating weather; the three-dimensional static model and a dynamic model in an unreal engine are fused to generate an urban environment; carrying out geographical gridding on the urban area; introducing a trafficability map and physical restrictions of the vehicle, and setting the trafficability map of the vehicle; an improved A * algorithm based on geographic gridding is introduced, path planning is conducted, the improved A * algorithm is used, and path optimization is achieved through a heuristic function and a penalty function; and outputting the generated path plan.

The utility model relates to the technical field of security and protection equipment, in particular to all-weather security and protection patrol equipment which can avoid obstacles in the moving process, push and clean sundries such as stones on a walking road, avoid influence on equipment walking, improve trafficability and safety, facilitate adjustment of monitoring directions and improve the safety of the equipment. The patrol area is increased; comprising an equipment body, an equipment box, two patrol cameras, a face recognition camera, an adjusting assembly, an avoiding assembly, a pushing assembly and a control assembly, walking wheels are arranged at the bottom of the equipment body, the equipment box is installed at the right end of the top of the equipment body, the adjusting assembly is installed in the equipment box, and the two patrol cameras are installed at the output end of the top of the adjusting assembly; an avoiding assembly is installed on the outer wall of the equipment body, pushing assemblies are installed at the left and right ends of the bottom of the equipment body, a control assembly is installed in the equipment body, and the face recognition camera is installed at the left end of the top of the equipment body.

The invention discloses a small general intelligent control chassis for hilly and mountainous areas, which belongs to the technical field of agricultural mechanical equipment and comprises a telescopic support, a lifting walking system, a central transfer steering system and a control and navigation system. The telescopic support adjusts the wheel track of the whole vehicle through a front frame body and a rear frame body which are connected in a sliding mode. The four lifting walking systems are independently installed below the four corners of the support. Each lifting walking system comprises wheels, an electric push cylinder driving the wheels to ascend and descend and a pressure sensor monitoring grounding pressure. The central transfer steering system is installed on the support and used for controlling synchronous steering of four wheels. The control and navigation system receives signals of the sensors and controls the systems to act cooperatively, so that self-adaptive driving and operation of the chassis under complex terrains are achieved. The trafficability, stability and operation safety of the agricultural machine in hilly and mountainous areas are improved.

The invention discloses an amphibious unmanned vehicle walking mechanism with a wheel-track interchange function, and mainly relates to the field of amphibious vehicles. The traveling mechanism adopts a modular design relatively independent of a vehicle body, and mainly consists of a turnover lifting piece and an interchangeable tire / crawler traveling piece. The overturning and lifting part drives the walking part to be wholly overturned upwards or downwards through the cooperative action of the overturning oil cylinder and the lifting oil cylinder, so that when the amphibious vehicle runs in water, the walking mechanism can be overturned out of the water surface, the streamline of the vehicle body is kept, and the water resistance is reduced; and the ground can be reliably grounded when the vehicle runs on land. The hydraulic motor is used for driving and providing power, and the pin shaft is used for quick connection and separation, so that the tire walking part and the crawler walking part can be integrally and quickly interchanged to adapt to different application scenes such as hard road surfaces or sand beaches, sludge and the like. According to the amphibious vehicle, the overwater high speed performance and the land complex terrain trafficability of the amphibious vehicle are effectively considered, the application range of the amphibious vehicle is widened, and the use efficiency is improved.