597 results about "Environmental perception" patented technology

The invention provides an acoustic array adaptive calibration correction system applied to an underwater moving target, which relates to the technical field of marine equipment and comprises a multi-source excitation and environment perception module, an intelligent array perception and diagnosis module, an adaptive position inversion and uncertainty quantification module and a closed-loop calibration execution and self-learning optimization module. The multi-source excitation and environment perception module is responsible for providing reference signals required by calibration and establishing a correlation model of environment and formation distortion, and the intelligent array perception and diagnosis module realizes multi-modal data acquisition, array element health state monitoring and formation geometry self-perception. The adaptive position inversion and uncertainty quantization module completes signal processing, position estimation and error quantization propagation, and the closed-loop calibration execution and self-learning optimization module executes a compensation strategy, verifies a calibration effect and continuously optimizes system performance through online learning; the system solves the problem that a traditional method cannot process environment time varying, multi-sensor conflicts and uncertainty quantization.

The invention discloses an information processing method and system based on the Internet of Things, and relates to the technical field of Internet of Things services, and the method comprises the steps: dynamically obtaining the computing power, energy consumption and network state of each node through an environment perception module, and constructing a node capability vector and a link performance vector; the task analysis module is used for carrying out structured modeling on tasks and identifying candidate segmentation points and unloading targets; the cost modeling module establishes a joint cost function of time delay and energy consumption; the strategy generation module generates an optimal unloading strategy based on multi-agent reinforcement learning; and the scheduling communication module executes task unloading and result integration according to the strategy, and feeds back execution data to update the system state. According to the method, intelligent, low-delay and energy-consumption optimization processing of task segmentation unloading is realized, and the method is suitable for a dynamic heterogeneous Internet of Things computing environment.

The invention discloses an electricity consumption anomaly detection method, system and device based on an environmental perception graph convolutional network and a medium, and belongs to the technical field of smart power grids. The method comprises the steps that historical electricity consumption of a power grid area unit and external environmental factors are acquired to construct a multi-dimensional space-time association graph; a periodic trend component and environment-related residual fluctuation are separated through the multi-dimensional space-time correlation diagram, and a two-channel diagram convolution feature is obtained; in combination with the convolution features of the two-channel graph, feature space decoupling of a periodic channel and a residual channel is realized, and decoupling features are obtained; and extracting a historical residual fluctuation sequence based on the decoupling features, and constructing an environmental state sensitive probability density function. According to the method, the historical electricity consumption and the external environment factors such as temperature, humidity or weather events are closely fused by constructing the multi-dimensional space-time association diagram, so that the space-time dependence of the electricity consumption behavior is effectively captured, and misjudgment caused by ignoring environment dynamics in a traditional method is avoided.

The invention discloses a feed additive ratio control method and system based on environmental perception. The method comprises the steps that breeding environment, feed characteristics, equipment operation and animal physiological parameters are collected, and a high-quality parameter set is generated after preprocessing; calculating an environment stress index based on a multi-factor data fusion algorithm and analyzing a change trend; combining historical and current data, using time sequence prediction and a machine learning algorithm to predict additive requirements and generating an optimal proportioning strategy; a control instruction is generated according to the strategy, and the additive conveying and spraying process is regulated and controlled; and a matching strategy and control parameters are corrected in real time through closed-loop feedback, so that accurate regulation and control are realized. The device can dynamically adapt to environmental changes, reduce animal stress, and improve mixing uniformity and nutrition retention effect.

The invention relates to the technical field of beacon light control, in particular to a beacon light anti-interference control method and system based on environmental perception and dynamic modulation. The method comprises the following steps: extracting an ambient light reference based on adaptive weight sliding filtering; in combination with target detection and trajectory prediction, interference pre-judgment features are obtained; on the basis of an evidence theory, analyzing comprehensive interference evaluation characteristics through the ambient light reference, the interference pre-judgment characteristics and the electromagnetic interference characteristics; fusing meteorological environment perception and influence features and self health state features, and evaluating equipment operation risks; and based on modulation signal optimization characteristics, the comprehensive interference evaluation characteristics and the equipment operation risk, fuzzy control and rule reasoning are utilized to realize dynamic output of anti-interference control parameters. In order to solve the problem that the signal recognition rate is reduced due to the fact that a beacon light is easily affected by multiple factors in a complex ocean navigation channel environment, data are obtained through multi-dimensional environment perception, and dynamic modulation control is achieved after feature extraction and fusion.

The invention discloses an intelligent robot navigation system with a body based on a multi-modal large model, and the system comprises an RGB-D camera module which captures a color image and a depth image of an environment in real time, and carries out the preprocessing of the color image and the depth image, and outputs the preprocessed image; the laser radar SLAM module provides a basis for subsequent coordinate calculation and navigation path planning; the multi-modal VLM module is used for calculating the three-dimensional coordinates of the target location in the map; and the navigation control module controls the robot to move. The beneficial effects of the invention lie in that the system realizes efficient environmental perception and natural language understanding through multi-modal fusion, and can accurately identify a target location, calculate a three-dimensional coordinate and plan a safe path, thereby improving the autonomous navigation precision and reliability of the robot, and being suitable for intelligent movement control in a complex scene.

The invention provides an underwater positioning system based on effective sound velocity compensation and multipath equalization, which relates to the technical field of marine equipment and comprises an environment sensing and modeling module, a signal propagation characteristic analysis module, an intelligent signal processing and distance measuring module and a fusion positioning and quality evaluation module. The environment sensing and modeling module is used for constructing a dynamic three-dimensional sound velocity field and evaluating the reliability of the environment, the signal propagation characteristic analysis module is used for establishing a multi-path propagation model and extracting path characteristic parameters, and the intelligent signal processing and distance measuring module is used for suppressing multi-path interference and realizing high-precision distance measuring calculation. The fusion positioning and quality evaluation module is used for multi-source information fusion positioning and forming a quality feedback closed loop; the system can effectively cope with sound velocity temporal and spatial change and multipath interference in a complex marine environment, remarkably improves the precision, stability and robustness of underwater positioning of the underwater vehicle, and is suitable for the fields of deep sea detection, ocean engineering and the like.

A motion control method for an adaptive self-reconfigurable pipeline robot based on environmental perception includes: acquiring internal images of the pipeline for scene recognition, segmenting planar surfaces and curved surfaces in the images according to recognition results, and extracting boundary lines of the pipeline; calculating a straight-pipe width, a bent-pipe curvature, a slope angle, and a step height to analyze passability of the robot; designing a path planner and a swing-arm planner to generate a reference trajectory and a swing-arm angle sequence of the robot, performing smoothing, and inputting the reference trajectory and the swing-arm angle sequence into a model predictive control (MPC) motion controller; estimating a position and state of the robot through an Error State Kalman Filter (ESKF) algorithm, and inputting estimation results and collision warning signals into the MPC motion controller; and finally outputting a signal for control of a motor and a swing-arm motor.

The utility model discloses an intelligent communication antenna system capable of self-adapting to signal intensity, which is characterized by comprising an environment sensing module, a signal processing module and a fuzzy calculation adaptation module, and the environment sensing module, the signal processing module and the fuzzy calculation adaptation module are electrically connected. The environment sensing module comprises a signal state monitoring unit and a physical environment sensing unit; according to the utility model, through the arrangement of the environment sensing module and the fuzzy calculation adaptation module, if the attitude of the unmanned aerial vehicle changes in the driving process, the system can realize the dynamic prediction of the unmanned aerial vehicle according to fuzzy calculation in combination with the effects of multiple sensors such as a radar and a sensor, and can adjust the antenna parameters in time; according to the device, dynamic adaptation is realized through closed-loop feedback and sensing-processing-adjustment, so that the antenna signal strength can be adaptively switched when the attitude of the unmanned aerial vehicle changes quickly.

The invention relates to the technical field of dexterous hand motion planning, in particular to a dexterous hand motion planning method and system based on deep learning, and the method comprises the steps: obtaining the state information of a dexterous hand, carrying out the multi-modal feature fusion to generate environment perception information, carrying out the dynamic obstacle detection and cooperative signal analysis, generating an obstacle avoidance path, and optimizing the motion path planning. Real-time data are collected through the sensor module, multi-modal features are fused, and environment sensing information is generated to support motion planning of the dexterous hand; collaborative decision-making among multiple dexterous hands is realized by utilizing an internal communication module, and the adaptive ability and task execution efficiency in a complex dynamic environment are remarkably improved. In addition, dynamic obstacle information is shared by prompting a cooperation signal, conflicts are avoided, and the response speed is increased. The method is suitable for efficient motion planning of the dexterous hand in a complex dynamic scene.

According to the intelligent navigation method and device for the inspection robot, a map fusion system is innovatively designed, and the environment is effectively expressed through multi-source data and rasterization processing. And an environment perception mechanism is constructed, and a reliable obstacle recognition model is established in combination with semantic analysis and trajectory prediction. Dynamic planning is introduced, and the accuracy of obstacle avoidance is ensured through interference analysis and state optimization. According to the method, the defects of the traditional technology in the aspects of map fusion, environment perception, dynamic planning and the like are effectively overcome, and technical guarantee is provided for the inspection robot.

The invention discloses a real-time collaborative cross-scene visual assistance and environment perception system for visually impaired people based on head-mounted equipment. According to the invention, through fusion of a multi-mode perception technology and a neural feedback mechanism, all-around environmental cognition support is provided for visually impaired people. Key targets such as curbs, steps and traffic signals in a complex scene can be accurately recognized, and a safe navigation scheme is generated in real time in combination with a dynamic path planning and obstacle avoidance algorithm. Through personalized feedback modes such as voice and vibration, the system can adjust the interaction rhythm according to the behavior habit and cognitive state of the user, ensure efficient and natural information transmission, effectively reduce the cognitive load of the user, help the visually impaired people to travel autonomously in different environments, improve the independent living ability, and improve the user experience. Real-time response and calculation efficiency are balanced through a collaborative architecture, so that a high-performance auxiliary function is not limited by heavy hardware equipment any more. The universal design reduces the use threshold, and is helpful for more visually impaired people to enjoy the convenience brought by science and technology.

The invention discloses an autonomous exploration method based on multi-modal data fusion and a large language model, solves the problems of low autonomous exploration efficiency, insufficient decision intelligence, poor environmental adaptability and the like in the prior art, and belongs to the technical field of robots. The autonomous exploration method comprises the following steps: S1, deeply fusing laser radar geometric information and visual semantic information through cooperative work of a three-dimensional laser and a visual sensor, and constructing an accurate semantic map; s2, performing code conversion on the semantic map to enable a large language model to understand and perform reasoning; and S3, based on an output result of the large language model in the S2, selecting semantic-oriented exploration or geometric frontier exploration until the task is completed or all regions are explored. According to the method, accurate and detailed environment perception is realized, the intelligence and task correlation of exploration decision making are improved, and the method has wide application potential.

The invention discloses an intelligent assembly guide and error correction system based on man-machine cooperation, and relates to the field of man-machine cooperation intelligent assembly, and the system comprises a task instantiation module which generates an assembly task instance aligned with a physical environment according to an assembly order and in combination with environment perception information; the cognitive intention state module is used for collecting and processing multi-mode physiological and behavior signals of an operator in real time and identifying a cognitive intention state related to a current assembly task; the man-machine cooperation strategy module is used for dynamically generating an adaptive man-machine cooperation strategy based on the recognized cognitive intention state and the process requirement of the current assembly task instance; and the operation mode pre-configuration module is used for generating operator-oriented guide information according to the man-machine cooperation strategy and carrying out corresponding operation mode pre-configuration on the robot. According to the method, upgrading from one-way programming to two-way self-adaptive collaboration is realized, and the intelligent level and collaboration efficiency of the assembly process are effectively improved.

The invention discloses an environment perception method and system based on multiple laser radars and edge computing, and belongs to the field of intelligent traffic and vehicle infrastructure collaboration.The method comprises the steps that firstly, space-time alignment and edge preprocessing are conducted on collected point cloud data, multi-source voxel point cloud is generated, coarse semantic segmentation is conducted, and then the multi-source voxel point cloud is obtained; a target registration network is used for estimating a posture and fusing point clouds, reverse denoising and dense reconstruction are carried out through a diffusion model, a static background model is constructed based on a sliding time window, dynamic point clouds and a passable area graph are output through voxel difference and aerial view analysis, and finally three-dimensional target detection and cross-radar fusion and tracking are carried out in combination with the passable area. The edge side is responsible for data compression and abnormal rollback, incremental information is only issued to the vehicle end, a transmission strategy is adjusted in a self-adaptive mode, and real-time performance and reliability are guaranteed.

The invention relates to the technical field of vehicles, in particular to a multi-mode out-of-vehicle interaction method and related equipment based on environment self-adaptation, and the method comprises the steps: obtaining perception information in real time through an environment perception module, the perception information comprising environment conditions, interaction object information and scene semantic understanding information; based on the perception information, utilizing an adaptive decision engine to dynamically analyze an optimized interaction mode and parameters, and generating interaction content; outputting interaction information according to the interaction content generated by the self-adaptive decision engine through a multi-modal output module; the interaction strategy is dynamically adjusted by sensing the environment state in real time, and the safety and the interaction efficiency can be improved.

The invention discloses a robot obstacle avoidance method and device for a dynamic unstructured environment, equipment and a medium, is applied to a target robot, and relates to the technical field of computers, and the method comprises the steps: carrying out the feature fusion of collected multi-modal environment data of the dynamic unstructured environment, so as to obtain an environment perception data set; analyzing the environment perception data set through a pre-training semantic model to generate an environment evaluation report corresponding to the environment perception data set; generating an initial path reaching the target point based on the current pose information of the target robot and the environment evaluation report, and performing obstacle avoidance optimization on the initial path through the environment evaluation report to obtain an optimized path; and the optimized path is converted into a robot motion instruction, and a target point is reached by executing the robot motion instruction. Therefore, synergy of semantic-level obstacle understanding and path optimization in a dynamic unstructured environment can be realized, and the scene adaptation limitation of the traditional technology is broken through.

The invention discloses an intelligent illegal behavior identification method and system based on multi-algorithm fusion, and relates to the technical field of image analysis. The method comprises the steps of collecting a region image sequence, identifying real-time personnel density information, adjusting the scale of a laser curtain wall region based on the personnel density information, forming a self-adaptive monitoring boundary, when a laser sensor detects an intrusion event, recording the intrusion moment and intrusion characteristics, calling an intrusion region image set, and obtaining the intrusion region image sequence; and analyzing the intrusion area image set by adopting an ensemble learning-based violation behavior recognition network group to obtain an interactive violation behavior recognition result, fusing the interactive violation behavior recognition result with the intrusion characteristics, introducing personnel density information to carry out environment adaptive compensation calculation, and outputting a final violation behavior recognition result. According to the invention, through fusion of image analysis, laser triggering and environment perception, high-robustness and high-accuracy intelligent identification of illegal behaviors in scenes such as subway security check is realized.

The invention discloses an inspection robot path planning method and system based on environmental perception, and relates to the technical field of path planning, and the method comprises the steps: traversing an inspection target region to determine a plurality of environmental perception point locations; obtaining a plurality of environmental perception data sets; performing data fusion processing on the plurality of environment perception data sets to generate a three-dimensional environment model; determining an initial planning path of the inspection robot; performing environment adaptability analysis on the initial planning path, and determining a target planning path; and controlling the inspection robot to execute an inspection task in the inspection target area according to the target planning path, and feeding back inspection state data in real time. According to the method and the device, the technical problems of low task execution efficiency and poor reliability of the inspection robot caused by lack of dynamic optimization of the inspection robot path planning in the prior art are solved, and the technical effects of realizing accurate planning and dynamic environment adaptation of the inspection robot path and improving the task execution efficiency and reliability of the inspection robot are achieved.

The invention discloses a real-time processing method and system for robot dog environment perception and decision, and relates to the field of robot dogs. Carrying out multi-source data fusion and semantic segmentation based on deep learning; carrying out semantic enhanced 3D point cloud construction and target detection; real-time path planning and obstacle avoidance decision making in a dynamic environment; and carrying out adaptive motion control and system fault-tolerant processing. According to the invention, through four core technologies of multi-modal fusion, semantic enhancement, real-time calculation optimization and intelligent decision making, the robustness and real-time performance of multi-modal sensor fusion are significantly improved, and the precision of environmental perception is improved; calculation delay is greatly reduced, edge computing power distribution is optimized, and high-real-time decision making is achieved; the intelligent obstacle avoidance and path planning capability in the dynamic environment is enhanced, the intelligent decision-making capability in the dynamic environment is improved, the reliability and practicability of the system are improved, and an efficient, stable and low-cost solution is provided for large-scale application of the system in the fields of industry, security and logistics.

The invention provides an industrial robot anti-collision early warning method and system. The method comprises the following steps: environment perception and multi-source data fusion; predicting a dynamic obstacle track; robot posture real-time modeling is carried out; carrying out collision risk joint assessment; dynamically dividing early warning levels; intelligent planning of an obstacle avoidance path; dynamically compensating joint torque; multi-stage early warning collaborative response is carried out; performing iterative evaluation on the early warning effect; and self-evolving the anti-collision knowledge base. The three key technical problems in the industrial robot anti-collision field are solved, dependence on high-cost physical sensors (such as a torque sensor and a laser radar) is eliminated, collision detection of any position of the whole robot is achieved, and the cost is reduced by 60% or above. The limitation of a static obstacle model is overcome, and real-time track prediction and risk grading of dynamic targets such as moving personnel and conveying belt materials are achieved. Through multi-source data fusion and a risk probability model, the false alarm rate is reduced to 5% or below, and meanwhile it is guaranteed that the missing report rate is 0.

The invention discloses a robot grabbed target recognition and positioning method and system based on a knowledge graph, and the method comprises the steps: constructing a triple knowledge graph containing object appearance features and grabbed point pose parameters through multi-source data integration, and achieving a structured node network; environment perception data of a robot vision module is obtained, vector representation of a target object is determined through processing, if the similarity exceeds a threshold value, a matching node is inquired through the entity linking technology, recognition ambiguity is solved, and an accurate result is obtained; based on a result, retrieving pose parameters in the knowledge graph, performing coordinate conversion to generate a fine-grained grabbing sequence, combining real-time visual feedback, adopting a dynamic adjustment algorithm to optimize a path, and updating the knowledge graph through simulation evaluation to adapt to a new scene. According to the method, the core function of the knowledge graph in grabbing optimization is highlighted, the accuracy, robustness and adaptive capacity of robot operation are improved, and the method is suitable for the fields of industrial automation and the like.

The invention belongs to the technical field of aerial work robots, and discloses an automatic wall-climbing cleaning and overhauling robot and a control method thereof. The robot comprises a machine body frame, a multi-mode movement mechanism, an operation mechanism, an environment sensing module, a multi-level depth estimation module and a control module. According to the multi-mode movement mechanism, switching between a flying mode and a wall climbing mode is achieved through at least four sets of tiltable duct propelling assemblies; the operation mechanism comprises a pneumatic adsorption type mechanical arm driven by the rotating platform and an integrated transmission cleaning mechanism and is used for executing adsorption and operation tasks. The multi-level depth estimation module generates a high-precision environment map, and the control module adopts neural reachability analysis, predicts a robot state reachability set after different control instructions are executed through a neural network, and performs collision detection with the environment map, so that a safety instruction or a performance instruction is selected prospectively. The problems of high aerial work risk, single function, poor environmental adaptability and low intelligent degree in the prior art are solved.

The invention discloses a forehead flexible sensing method and system, and relates to the technical field of flexible sensing, and the method comprises the following steps: S1, obtaining microenvironment sensing data; s2, generating an original data frame; s3, performing invalid frame inducement classification on the original data frames; s4, distinguishing valid frames from invalid frames; s5, performing directional remining and parameter correction of remining-mining linkage on the judged invalid frames; and S6, dynamically adjusting the regulation and control parameters. According to the invention, through multi-dimensional microenvironment perception grading, invalid frame inducement classification and discrimination and threshold value weighting dynamic linkage fine tuning, in combination with a steaming-sampling linkage directional resampling mode, microenvironment dynamic change characteristics such as conductivity, temperature, contact impedance and the like caused by skin sweat secretion, environment temperature and humidity conduction and head actions in a wearing process can be captured; and adaptive acquisition parameters are dynamically matched according to an actual grading result of the microenvironment, so that the problem of gradual distortion of core physiological signals such as electroencephalogram and PPG caused by microenvironment change is avoided.

The invention discloses a comprehensive traffic low-altitude global intelligent sensing method based on autonomous evolution, and belongs to the crossing field of intelligent traffic and computer vision. The method comprises the following steps: collecting multi-source sensing data and extracting a structured causal variable; a dynamic causal graph is constructed, and attribution analysis of perception errors is realized through intervention learning; realizing cross-scene model migration and few-sample self-adaption based on the knowledge graph; carrying out adaptive reasoning by adopting an environment-aware dynamic graph neural network; and realizing a causal-driven autonomous evolution closed loop based on multi-source feedback. According to the method, the crossing of the perception system from'perception-optimization 'to'understanding-evolution' in a low-altitude complex traffic environment is realized, the perception precision, the scene adaptability and the system interpretability are remarkably improved, and the manual operation and maintenance cost is reduced.

The invention discloses a complex soft obstacle environment-oriented quadruped robot crossing advancing method, which comprises the following steps of S1, actively interacting with the terrain under the condition of no prior environment perception, and obtaining terrain mechanical property information; s2, based on the obtained terrain mechanical property information, deeply analyzing the terrain, evaluating the risk of the robot advancing in the terrain, and adjusting the gait according to the risk; and S3, comprehensively evaluating the traveling safety of the robot on the current terrain, and deciding whether to pass safely or take recovery measures according to the risk level. The method gets rid of the dependence of the traditional technology on geometric contour perception of an optical sensor, and can directly obtain key mechanical parameters of subsidence, shearing and the like of soft terrains such as sludge and thick snow on line; meanwhile, in combination with a parameter estimation method for simplifying a Bekker model, the problem of perception deviation caused by large mechanical property difference of visual similar areas is solved, and reliable environmental data support is provided for subsequent planning and control.

The invention discloses an adaptive cruise function test method and device, electronic equipment, a medium and a product. The method comprises the following steps: in a pre-constructed test scene, acquiring surrounding environment information of a test vehicle to obtain environment sensing data; constructing a vehicle surrounding environment model according to the environment perception data; according to the vehicle surrounding environment model and a preset adaptive cruise control strategy, adaptive cruise control is carried out; acquiring operation data generated when adaptive cruise control is carried out in the test scene; and analyzing the operation data according to a preset performance index to obtain a test evaluation result for evaluating the performance of the adaptive cruise control system. The method can solve the problem that a traditional adaptive cruise test method is insufficient in test coverage in a complex dynamic scene.

The invention relates to an autonomous exploration method based on hierarchical reinforcement learning target driving in a dense dynamic scene, and belongs to the field of robot autonomous navigation and environmental perception. The method comprises the steps of obtaining a pedestrian position vector based on a pedestrian frame in an RGBD image, and constructing a dynamic database in combination with radar scanning data; performing similarity calculation based on an RK radar descriptor of radar scanning data to obtain a high-level strategy switching result; a plurality of interest points are generated, and the interest point with the minimum composite cost function value is selected as a target point needing to be explored in the next step; according to a high-level strategy switching result, outputting high-level multi-modal fusion features by using the trained HRL model, and obtaining a final navigation skill in combination with a target point needing to be explored in the next step; and outputting a speed control instruction to realize autonomous exploration based on the bottom-layer multi-modal fusion features and final navigation skills output by the trained HRL model. The objective of the invention is to solve the technical problem that exploration efficiency and safety are difficult to balance in a complex environment in the prior art.

The invention relates to an environment sensing method based on target detection and stereo matching. According to the environment perception method based on target detection and stereo matching, targets in an environment image can be recognized through model training, and category information and position information of each target can be recognized, so that a target detection model can recognize the category information and the position information of the targets in the environment image in real time; through a local matching strategy of a target area delineated by focusing on the position information of the target, huge calculation overhead caused by full-image matching is avoided; pixel-level parallax information in the two-dimensional image is accurately converted into a physical depth distance in a three-dimensional space in combination with calibration parameters of a binocular camera in an environment sensing system; and finally outputting an environment perception image capable of displaying a detailed perception result of each detected target through information fusion. According to the environment sensing method, the efficiency and robustness of the whole sensing process are remarkably improved while the accuracy and integrity of environment sensing are ensured.

The invention provides a reference path generation method and device. The method comprises the following steps: acquiring environment sensing data and vehicle real-time state data, wherein the environment sensing data comprises road information, obstacle information and a road adhesion coefficient; generating an initial path set according to the road information and the obstacle information; vehicle state adaptive dynamic constraints are constructed, the vehicle state adaptive dynamic constraints comprise vehicle dynamic parameters, and the vehicle state adaptive dynamic constraints associate values of the vehicle dynamic parameters with vehicle real-time states and road adhesion coefficients; the value of the vehicle dynamics constraint index is adaptively adjusted according to the change of the vehicle real-time state or the road adhesion coefficient; and according to the vehicle real-time state data and the environmental perception data, optimizing each initial path in the initial path set under the vehicle state adaptive dynamics constraint to generate a reference path which simultaneously meets the geometric feasibility and the physical performability.