287 results about "Ground vehicles" patented technology

The invention discloses an emergency material intelligent scheduling system and method based on an ant colony algorithm. A demand prediction module of the emergency material intelligent scheduling system dynamically predicts material demands by using a time-space diagram neural network, and constructs a hierarchical network model integrating multi-modal transportation of unmanned aerial vehicles, ground vehicles and the like. And the optimization calculation module adopts an improved ant colony algorithm, introduces a demand urgency degree weight factor and a green weight factor to construct a multi-objective fitness function, and optimizes a transportation path in combination with a dynamic pheromone updating mechanism and a multi-ant colony collaborative strategy. The system is equipped with an edge computing driven dynamic adjustment module to realize 30-second fast path re-planning, a psychological assistance priority model is innovatively integrated, and a psychological crisis index optimization scheduling strategy is extracted through sentiment analysis. According to the method, the problems of response lag and insufficient multi-objective optimization of a traditional scheduling system are effectively solved, the transportation efficiency is remarkably improved by 25%-35%, carbon emission is reduced, and high efficiency, fairness and humanity care of emergency scheduling are guaranteed.

The invention discloses a ground vehicle inertial positioning enhancement method considering motion constraints. The method comprises the following steps: carrying out ground vehicle pose recursion calculation based on acceleration and angular velocity measured by a micro-electro-mechanical inertial measurement unit (MEMS-IMU); the method comprises the following steps: training a deep neural network by using data under two driving working conditions of straight running and turning of a vehicle, so that the deep neural network can predict the transverse and vertical speeds of the vehicle in real time according to MEMS-IMU measurement, wheel speed meter measurement and angle sensor measurement, and give the prediction confidence in the form of variance; and the transverse and vertical speeds predicted by the deep neural network, the rotating speed of the rear axle wheel of the vehicle and the rotating angle of the front wheel of the vehicle jointly form motion constraint, and an accumulated error of MEMS-IMU pose calculation is corrected through an error state Kalman filtering algorithm to obtain an accurate vehicle pose. According to the invention, the accumulated positioning error of ground vehicle inertial positioning can be corrected, and the precision of ground vehicle inertial positioning is improved.

The invention provides a modular and multifunctional karst collapse physical simulation experiment platform. Comprising a rigid frame, a bearing device, a moving device, a loading device, a lifting device, a pile foundation construction dynamic load simulation device, a ground traveling dynamic load simulation device, a rainfall simulation device, a computer vision monitoring device, an underground water pumping and draining simulation device and an underground space excavation disturbance simulation device. The moving device is arranged on the bearing device, the lifting device, the loading device and the pile foundation construction dynamic load simulation device are arranged on the moving device, the travelling crane dynamic load simulation device is connected with a traction device of the moving trolley, and the camera shooting monitoring device and the rainfall simulation device are arranged on the rigid frame through a support. According to the method, key disaster-causing conditions in the urban environment are fully considered, the working condition scene coverage is wide, and the applicability is high; modularized design is adopted as a whole, all the modules can be flexibly loaded according to requirements, the real environment can be simulated more truly, and the catastrophe evolution process of surface collapse can be reproduced.

The invention discloses an aerial unmanned aerial vehicle configuration number prediction method based on ground vehicle trajectory data, and belongs to the technical field of unmanned aerial vehicle control. In order to solve the problem of predicting the configuration number of aerial unmanned aerial vehicles based on a ground traffic state, the method comprises the steps of constructing a vehicle trajectory set; constructing a traffic density thermal field; constructing an average speed trend vector field; constructing a direction consistency index; a traffic density thermal field is used as a basic load, the modulus length of an average speed trend vector field represents mobility, a direction consistency index represents a traffic order, weighted combination is carried out to construct distribution pressure intensity, the distribution pressure intensity is converted into the demand intensity of air resources, and a space vector from a vehicle position to a nearest unmanned aerial vehicle take-off and landing point and a terrain complexity factor are considered. Constructing a preliminary unmanned aerial vehicle demand estimation field; and introducing a task urgency weighting function, the current task backlog amount and the weather flight risk to construct a final unmanned aerial vehicle deployment suggestion function, and completing prediction of the air unmanned aerial vehicle configuration number based on the ground vehicle trajectory data.

The invention discloses an expressway intelligent scheduling system based on air-ground collaborative data, and relates to the technical field of intelligent traffic, and the system comprises a low-altitude aircraft module which is used for monitoring the traffic state of an expressway in real time through a sensor, and generating a multi-sensor fusion data set through a data fusion algorithm; the ground vehicle module is used for communicating with the management and control center module through a vehicle-mounted terminal and receiving traffic guidance information, accident early warning and navigation instructions; the road side unit module is used for monitoring data of lanes and traffic nodes, generating a traffic flow initial data set and carrying out relay transmission on communication data among different modules; and the management and control center module is used for generating traffic guidance information, accident early warning and navigation instructions by using an artificial intelligence algorithm, and issuing the traffic guidance information, accident early warning and navigation instructions to ground vehicles by using a communication network. According to the invention, through cooperation of the low-altitude aircraft, the ground vehicle, the road side unit and the management and control center module, fusion analysis and intelligent scheduling of multi-source data are realized.

Autonomous ground vehicles that are outfitted with radar sensors and inertial measurement units accurately determine states of the autonomous ground vehicles, e.g., estimates of the vehicles' positions, orientations, or velocities or accelerations along or about one or more axes, based on data captured by the radar sensors and the inertial measurement units. Where objects are detected in radar scans, the objects are determined to be static (or fixed), or dynamic (or moving), and landmarks representing static objects are identified. Constraints on estimates of states may be calculated based on doppler effects, inertial measurement unit effects, or locations of landmarks, and the states may be determined as solutions to optimization problems based on the data and the calculated constraints. Odometry messages representing the determined states may be generated and stored or utilized for any purpose.

The invention relates to the technical field of rail traffic, in particular to a virtual coupling station track departure scene marshalling method, device and equipment and a medium. After each train in the virtual coupling group train crosses the transponder group, periodically sending a message packet 1 containing train distance information to the ground equipment; after the front train of the virtual coupling group train leaves the station, the front train receives a message packet 2 which is sent by the ground equipment and contains the converted distance information; wherein the converted distance information and the train distance information of the next train are based on the same reference object; and the front vehicle sends the converted distance information to the rear vehicle, and throat area grouping is completed. Through a train-ground-train communication mode, the ground equipment converts the position of the rear train into the position which can be identified by the front train and forwards the position to the front train, so that marshalling of the two trains is accelerated. Marshalling can be carried out in advance in the throat area, after marshalling succeeds, the rear vehicle can be controlled according to the driving permission of the following vehicle, and the running speed and the running efficiency are improved.

The invention discloses an air-ground cooperative path planning method for multi-type target reconnaissance, and the method comprises the steps: obtaining the position information of a base and all targets, carrying out the clustering, obtaining a plurality of target sub-regions, taking each clustering center as a virtual point corresponding to the target sub-region, and enabling each target sub-region to have at least one target; generating a ground vehicle path based on the coordinates of the base and each virtual point, and optimizing the ground vehicle path; feature points of various types of reconnaissance targets are obtained, unmanned aerial vehicle paths of various target sub-regions are obtained based on improved adaptive large neighborhood search, each unmanned aerial vehicle path takes a virtual point of the target sub-region as a starting point and an ending point of the unmanned aerial vehicle path, and the feature points of the targets are connected; and integrating the ground vehicle path and the unmanned aerial vehicle path of each target sub-region to complete air-ground cooperative path planning. The method is applied to the field of path planning, full-coverage reconnaissance of various types of targets such as points, lines and planes can be efficiently completed, and the charging frequency of the unmanned aerial vehicle is reduced.

The invention relates to the technical field of artificial intelligence security, in particular to a physical domain robust adversarial attack method oriented to unmanned aerial vehicle aerial photography target detection, and aims at challenges of various environmental factors, the method comprises the following steps: initializing a learnable pixel tensor to generate an adversarial patch; inputting the training samples, the adversarial patches and label files of the adversarial patches into a patch conversion module to generate adversarial samples, inputting the adversarial samples into a YOLO target detector, performing weighted summation on each loss item based on the performance of the adversarial patches on each target detection network, constructing a unified optimization target, solving the overall loss for a detection model, and constructing an optimal target; according to the method, the adversarial patch is installed on the top of the vehicle or around the vehicle, so that the ground vehicle can avoid the unmanned aerial vehicle target detection system, and therefore, robustness detection of the aerial photography target detection neural network can be realized.

The invention discloses a multi-agent dynamic access and cooperation method for an air-space-ground Internet of Vehicles integrated architecture, provides a dynamic access decision scheme based on the air-space-ground Internet of Vehicles integrated network architecture, and aims to solve the problems of vehicle sensing range limitation and communication delay. Through multi-agent deep reinforcement learning, channel quality and load conditions of a ground network and a satellite network are evaluated in real time, and an optimal access mode is selected for a vehicle node. Meanwhile, in order to solve the problem of data asynchronization caused by communication delay between vehicles, clock inconsistency and transmission delay difference between a ground network and a satellite network, a Transform-based multi-agent cooperation mechanism is provided on the basis of a dynamic access architecture, time sequence coding is performed on asynchronous delay, sparse features of the vehicles are extracted, and the data asynchronization problem is solved. Effective processing of asynchronous data and accurate reconstruction of a global environment are realized at a core network end. According to the invention, more flexible and efficient dynamic access management and cooperation are realized.

A system for controlling unmanned vehicles such as drones, ground vehicles and robots includes multiple human-to-machine interface components to manage interactions efficiently and effectively between humans and robots with respect to control mechanisms (i.e., inputs such as voice-commands or joystick use) and monitoring (i.e., outputs such as camera or other sensor feeds, geolocation overlay, or audio feedbacks). A system according to the principles of the invention includes numerous modalities for control (e.g., voice-control, gesture-control, touch screen, joystick or other hand-held controllers) as well as monitoring (visual screen display, heads-up-display, audio feedbacks, geolocation overlay, haptic feedback, wearable sensor devices). The system is preferably expandable and may include additional modalities in the future, such as eye-tracking as compared to voice or gesture control.

The invention discloses a ground-air collision early warning method and system based on multi-dimensional risk assessment, and a storage medium. The method comprises the steps of obtaining a vehicle envelope box size parameter, a driving state vector, an air object landing state vector and a position covariance matrix; calculating the three-dimensional space-time envelope volume distribution of the vehicle according to the envelope box size parameter and the driving state vector, and calculating the position probability distribution of the air object according to the landing state vector and the position covariance matrix; determining a multi-dimensional collision risk quantitative index according to the three-dimensional space-time envelope volume distribution and the position probability distribution; and calculating a ground-air collision risk evaluation value through a multi-dimensional risk evaluation function based on the multi-dimensional collision risk quantitative index so as to carry out ground-air collision early warning prompt. According to the invention, through the multi-dimensional collision risk quantitative index, the complexity of collision calculation is reduced, the risk value is discretized to generate the progressive early warning instruction, and more accurate and reliable safety early warning information is provided for the ground vehicle in the ground-air heterogeneous scene.

The invention provides a multi-unmanned aerial vehicle cooperative reasoning optimization method based on DAG topology generation and dynamic DNN model partitioning, and the method comprises the steps: dynamically adjusting the position of an unmanned aerial vehicle based on a potential field method when a task request of a ground vehicle is received, and constructing a topology network in combination with greedy clustering and a DAG topology generation strategy; performing dynamic DNN model partitioning based on a discontinuous layer allocation mechanism and a dynamic programming algorithm DPA, and dynamically allocating DNN discontinuous layers to different unmanned aerial vehicles; performing DNN reasoning according to the topology network and the partitioning scheme; performing cache updating based on the priority and the energy perception strategy; and calculating time delay and overhead. Task allocation and load balancing can be intelligently performed according to the complexity of tasks and the computing power of each unmanned aerial vehicle, so that the overall efficiency of the system is improved, and the processing time delay is reduced; the task and data allocation strategy can be flexibly adjusted according to the network state and the environment change, and the real-time performance and the high efficiency of the task are ensured.

The invention discloses an air-ground task intersection prediction method for emergency rescue, and belongs to the technical field of unmanned aerial vehicle control. In order to solve the problem of accurately predicting an air-ground task intersection with reachability and energy efficiency requirements, the method comprises the following steps: predicting an unmanned aerial vehicle path; predicting a ground vehicle path; generating candidate intersection points; calculating the time cost of the rescue task, the energy consumption cost of flight and the environmental risk cost; constructing a comprehensive cost function; constructing a confidence factor through the terrain height difference, the path node complexity and the communication shielding score, and carrying out risk correction on the comprehensive cost function to obtain a corrected comprehensive cost function; and introducing factors such as a ground slope angle, a wind speed value, a ground trafficability score and an unmanned aerial vehicle flight distance to carry out punishment correction, and extracting an optimal air-ground task intersection point coordinate for emergency rescue. The method is suitable for various emergency rescue, multi-platform cooperation and intelligent task scheduling, and has wide application prospect and popularization value.

The invention discloses a low-altitude aircraft route setting method and equipment based on height layering, and the method comprises the steps: carrying out the layering of a low-altitude airspace model based on height, and obtaining a plurality of height layers; importing a digital three-dimensional base map into the layered low-altitude airspace model; respectively generating a corresponding two-dimensional connected graph for each height layer; carrying out route planning on the two-dimensional connected graph, and respectively generating corresponding two-dimensional routes; and generating a three-dimensional route according to the height layer and the two-dimensional route. According to the method, the complexity and difficulty of route design are reduced, the experience of ground vehicle automatic driving route planning can be conveniently applied to the field of route planning and route planning of low-altitude aircrafts, all height layers do not interfere with one another, and layered monitoring, layered scheduling and layered management are facilitated.

The invention discloses an autonomous parcel delivery system. The present disclosure relates to systems and methods for enabling unmanned and optionally manned delivery of goods to ground persons. For example, an aircraft may be used to provide fast response cargo delivery to widely separated small units under harsh and unpredictable conditions that cause unacceptable risks to both ground rereplenishment personnel and crew personnel. Among ground vehicles, parcels from the aircraft may be deployed to ground personnel at interspersed combat locations without exposing the ground personnel to an open landing zone of the aircraft.

A vehicle motion management, VMM, system for a heavy-duty vehicle has at least one wheel speed sensor outputs a wheel speed signal indicative of a rotation speed of a respective wheel on the vehicle, at least one inertial measurement unit, IMU, outputs an IMU signal indicative of an acceleration of the vehicle, a motion estimation function estimates a vehicle motion state comprising vehicle speed over ground, SOG, based on the at least one wheel speed signal and on the at least one IMU signal. The motion estimation function estimates a respective SOG error associated with the wheel speed signal as an increasing function of an applied torque to the wheel, and a respective SOG error associated with the IMU signal as an increasing function of a time duration elapsed after calibration of an integrator of the IMU signal. The motion estimation function estimates the vehicle SOG, based on a weighted combination of the at least one wheel speed signal and the at least one IMU signal, where the weights of the weighted combination are determined based on the SOG error associated with the wheel speed signal and on the SOG error associated with the IMU signal.

A method for speed control of a ground-based vehicle, a computer-implemented method for speed control of a ground-based vehicle, and a control device for speed control of a ground-based vehicle. A speed planner is provided and includes a macro-planner for rapid forward-backward speed planning and a meso-planner for optimizing driving time and energy consumption using an optimization solver. A distance planner is provided and includes a micro-planner for distance control of distance to a vehicle driving ahead. A speed setpoint value is initially calculated by the macro-planner or the meso-planner, the speed setpoint value then optimized by the micro-planner to generate a final speed setpoint value. The ground-based vehicle is then controlled by operating it using the final speed setpoint value.

Systems and methods for causing a first autonomous vehicle (e.g., a drone or other aerial vehicle) to utilize a LIDAR pattern being emitted by a LIDAR device of a second autonomous vehicle (e.g., a ground vehicle) to determine one or more characteristics of the second vehicle. The one or more characteristics may comprise, for example, (i) a make and / or model of a manufacturer of the LIDAR device and / or the second vehicle; and (ii) a particular unit of the LIDAR device and / or the second vehicle. The first vehicle may further utilize the LIDAR pattern of the second vehicle to perform other operations, such as mapping a 3-D model of the environment being traversed by the second vehicle.

The present disclosure describes an autonomous warehouse package delivery system. The system includes a warehouse, a plurality of unmanned ground vehicles (UGV), a plurality of UAVs, and a plurality of servers. The warehouse includes a U-shaped conveyor belt, a plurality of shelving units, a plurality of package stacks, a first plurality of robots, and a second plurality of robots. Each UGV autonomously pick-ups a package from a shelf, navigates to the UAV landing pads, and places the package on the UAV landing pad. The plurality of UAVs travel to the UAV landing pad and pick up a package, autonomously fly to a package delivery address and release the package at the package delivery address. A real-time data distribution middleware network is connected to the U-shaped conveyor belt, the first plurality of robots, the second plurality of robots, the plurality of UGVs, the plurality of UAVs and a plurality of servers.

The invention relates to a vehicle illegal parking management and control method and system based on an unmanned aerial vehicle. The method comprises the following steps: obtaining road information of a target controlled road section; arranging a flight plan of the unmanned aerial vehicle based on the road information; acquiring image information acquired when the unmanned aerial vehicle cruises according to the flight plan; and according to the image information, analyzing whether a ground vehicle of the target management and control road section violates parking, if yes, enabling the unmanned aerial vehicle to perform snapshot, then enabling the unmanned aerial vehicle to pause the current route and adjusting the pan-tilt camera to perform snapshot again so as to obtain a clear license plate image. According to the invention, the vehicle illegal parking event of the target road section can be accurately and efficiently identified, and vehicle illegal parking intelligent control based on unmanned aerial vehicle automatic cruise is realized.

A method for navigating an unmanned ground vehicle (UGV) includes obtaining, by a sensor spaced apart from the UGV and at least temporarily fixed relative to a stretch of land on which the UGV is to operate, a three-dimensional map of the stretch of land. A navigation signal is generated based on the three-dimensional map and a user-specified task. The navigation signal is transmitted to a controller operatively coupled to the UGV, and configured to receive a navigation signal and operate the UGV in accordance with the navigation signal.

The invention discloses an unmanned aerial vehicle assisted Internet of Vehicles trajectory planning method and system based on traffic flow prediction, and the method comprises the steps: S1, employing multi-source historical traffic data collected by a distributed sensor network as input, and building a traffic flow space-time prediction model based on a deep learning algorithm; s2, a vehicle networking model composed of a plurality of vehicles and a plurality of unmanned aerial vehicles is constructed, the unmanned aerial vehicles fly at a fixed height and serve as air base stations to provide services for ground vehicles, and only one unmanned aerial vehicle provides services for each vehicle; and S3, constructing a multi-unmanned aerial vehicle assisted Internet of Vehicles communication system model, clustering ground vehicles by using a clustering algorithm in combination with traffic flow prediction data, and further jointly optimizing trajectory planning and resource allocation strategies of the unmanned aerial vehicles by taking a clustering center as a pre-deployment position of the unmanned aerial vehicles for each vehicle cluster. The corresponding system comprises the sensor network, the cloud platform and the unmanned aerial vehicle cluster carrying the base station, and the communication quality and the energy efficiency of the Internet of Vehicles can be effectively improved.

The invention discloses an unmanned aerial vehicle dual-light aerial image over-the-ground vehicle target detection algorithm. The algorithm comprises the steps that an unmanned aerial vehicle pod collects ground visible light and thermal imaging data in real time; preprocessing and enhancing the sample; detecting a ground vehicle target; respectively extracting the features of the two images by using a trunk feature extraction network, and fusing the features at different depths, so that the vehicle target is more significant in the feature map; the feature extraction network is enhanced to further process fusion features; and inputting the processed feature map into a target detection head, predicting the type and the position of the vehicle, and transmitting a video with a mark back to a ground computer. According to the invention, for the aerial image of the unmanned aerial vehicle, the accuracy and efficiency of vehicle target detection are effectively improved, and powerful information support is provided for aerial observation and traffic management.

The invention relates to an optimal data transmission method for an unmanned ground vehicle, belongs to the technical field of unmanned vehicle control, and solves the problems of limitation of a static defense strategy and insufficient resource optimization of the unmanned ground vehicle in the aspect of safety protection. The method comprises the steps that a Stackelberg dynamic game model with an unmanned ground vehicle as a leader and an attacker as a follower is constructed, a defense target function and an attack target function are constructed respectively, iterative optimization is carried out, and when Stackelberg equilibrium is achieved, an optimal attack and defense strategy structural form is obtained; based on the optimal attack and defense strategy structural form, after the unmanned ground vehicle executes the optimal defense strategy at the current moment for data transmission, an attacker obtains the optimal attack strategy at the current moment based on the optimal defense strategy; and the unmanned ground vehicle adjusts the defense strategy of the unmanned ground vehicle according to the optimal attack strategy at the current moment, and obtains the optimal defense strategy at the next moment for data transmission.

A agricultural vision system including an aerial and a land vehicle equipped with imaging and geolocation units to generate lateral and overhead maps of crops. These maps fused into a multidimensional model for object tracking, yield estimation, and growth analysis. AI-driven algorithms provide actionable insights and enable automation, including pruning, harvesting, and augmented reality visualization for precision agriculture.

A system for 3D surveying of an environment by an unmanned ground vehicle (UGV) and an unmanned aerial vehicle (UAV) has two lidar devices. A reference unit has a first and a second marker in a spatially fixed arrangement. An automatic detection of the first marker is carried out for a coordinative measurement by the first lidar device to determine relative position data for providing relative position information of the first marker with respect to the first lidar device. The relative position data and spatial 3D information is used for an automatic detection and a coordinative measurement of the second marker by the second lidar device. The coordinative measurements are used for a referencing of lidar data of the UGV lidar device and lidar data of the UAV lidar device with respect to a common coordinate system.

The invention discloses a ground vehicle adaptive path planning method and device based on fuzzy reasoning, and relates to the field of path planning, and the method comprises the steps: constructing a terrain grid map based on a satellite image map and satellite elevation data; calculating the ground type coefficient, gradient and flatness of the target position; taking the cross-country performance of the target vehicle as an input variable, taking the gradient weight and the flatness weight of the target position as output variables, fuzzifying the input variable and the output variable, establishing a fuzzy rule by using a membership function, performing fuzzy reasoning, and converting a fuzzy quantity after reasoning into an accurate quantity for output; and using an A * algorithm to take the lowest comprehensive passing cost as a target, performing adaptive path retrieval on the surrounding position of the target position, updating the target position, and continuing path retrieval until the target position arrives at the final position to obtain an optimal path. According to the invention, the adaptability of the vehicle cross-country performance to the path is improved, the rationality and adaptability of path planning are enhanced, and the driving efficiency is improved.

The invention discloses a satellite-ground vehicle networking task unloading and resource allocation joint optimization method and system, and relates to the technical field of resource allocation, and the specific steps are as follows: constructing a three-layer cooperative computing network composed of a vehicle layer, an RSU layer and an LEO satellite layer; constructing a dynamic link available time constraint model based on a three-layer cooperative computing network; if the tasks are unloaded to the LEO satellites, whether migration triggering conditions are met or not is judged based on a task migration mechanism, and if yes, the tasks are unloaded to the adjacent LEO satellites through inter-satellite links; an ATPER-MATD3 algorithm is adopted, optimization is carried out by taking minimization of task execution total time delay and energy consumption as an optimization target based on a three-layer cooperative computing network, a dynamic link available time constraint model and a task migration mechanism, and an unloading strategy and a resource allocation strategy are generated. According to the invention, through dynamic unloading decision and multi-agent collaborative optimization, the task processing time delay is significantly reduced, the resource allocation efficiency is improved, and the system robustness and expandability under the scene of fusion of the Internet of Vehicles and the satellite network are enhanced.

The invention discloses a combined optimization method and system for vehicle formation stability and communication reliability. The method comprises the following steps: firstly, analyzing a coupling relationship between an air unmanned aerial vehicle and a ground vehicle queue in a motion control and communication process, and constructing a formation control and communication reliability dual-target joint optimization model in an air-ground collaborative network vehicle environment; and then, designing an auxiliary function to deduce a closed upper bound expression of a communication target, and converting the double-target model into an equivalent single-target optimization problem by adopting a constraint optimization strategy based on the expression, thereby ensuring the reliability of air-ground communication while realizing stable formation of a motorcade. According to the method, the coupling characteristic between the formation control system and the communication system is systematically considered, a joint optimization scheme with universality and practical value is provided, and the overall performance of the air-ground collaborative formation system can be effectively improved.