277 results about "Motion prediction" patented technology

The invention discloses an unmanned aerial vehicle intrusion real-time alarm and countering guidance system based on RID information. The system comprises a multi-source information acquisition and fusion module, an intrusion detection and threat evaluation module, a real-time alarm and countering strategy generation module, a feedback evaluation and strategy iteration module, a signal loss prediction and compensation module, a comprehensive processing terminal module and the like. The system establishes a three-dimensional geofence model by analyzing remote identification information of the unmanned aerial vehicle and fusing radar, photoelectric, electromagnetic and other multi-source data, and performs dynamic judgment and multi-factor threat grading on a target intrusion state. Multi-mode alarm is automatically triggered according to the threat level, and a hierarchical countering scheme from broadcast alarm and electromagnetic interference to physical capture is generated; and when the RID signal is lost, the system realizes continuous tracking and state recovery of the target by using a motion prediction and probability association algorithm.

The invention discloses a car following control method and a related product. According to the scheme, multi-source data are acquired, and the multi-source data are fused to obtain a fusion result; based on the fusion result, performing multi-modal trajectory prediction on the driving behavior of the preceding vehicle to obtain a prediction result of the driving behavior of the preceding vehicle; the multi-modal trajectory prediction comprises longitudinal motion prediction and transverse motion prediction; quantifying a car following risk by using a multi-dimensional risk index to obtain a risk level; constructing a multi-target cost function based on the fusion result, the front vehicle driving behavior prediction result and a control vector, and solving the multi-target cost function by using a preset constraint condition to obtain an initial control parameter of the vehicle; and on the basis of the risk level, initial control parameters of the vehicle are adjusted, and target control parameters of the vehicle are obtained. Compared with the problem of response lag in adaptive cruise control in the prior art, the adaptive cruise control method has obvious advantages.

The invention discloses an unmanned aerial vehicle cluster area sealing control method and system in a dynamic game scene. The method comprises the following steps: in a region exploration stage, realizing coverage and detection search of a task region based on a collaborative search algorithm of multi-agent partition perception and path planning; in the collaborative interception stage, based on the established target motion prediction model and the self-adaptive allocation mechanism of the enemy target, dynamic allocation is realized, the optimal interception path of the unmanned ship is planned, and meanwhile, energy charging and secondary flying of the unmanned ship are carried out in time; in the confrontation game stage, based on a decision tree model, in combination with multi-dimensional battlefield evaluation data, decision execution and evaluation are carried out, attack, avoidance or other tactical instructions are generated, and an optimal instruction and global target distribution are waited and cyclically made; and after an attack instruction is sent out, performing a safe predefined tactical attack in combination with tactical action execution logic. According to the method, the problems of poor unmanned cluster cooperative perception, difficulty in resource scheduling and untimely decision response in a complex dynamic environment are effectively solved, and the interception efficiency and comprehensive combat capability of regional sealing and control are improved.

The invention relates to an unmanned aerial vehicle multi-dimensional environment perception obstacle avoidance method and system, and belongs to the field of unmanned aerial vehicle control, and the obstacle avoidance method comprises the steps: constructing a multi-dimensional environment perception data cube with an unmanned aerial vehicle body coordinate system as a reference, and carrying out the feature decoupling processing of the multi-dimensional environment perception data cube, static obstacle geometric features, dynamic obstacle kinematics features, environment disturbance features and unmanned aerial vehicle state features are separated; processing the four features to respectively obtain an obstacle space occupation probability graph, an obstacle future motion prediction envelope surface, a local airflow disturbance compensation factor and a maneuverability boundary function, inputting into a lightweight obstacle avoidance decision neural network, and outputting three candidate obstacle avoidance paths and corresponding path safety scores; and sorting the three candidate obstacle avoidance paths, and selecting the path with the highest score as a final obstacle avoidance instruction. The autonomous obstacle avoidance success rate and the task execution efficiency of the unmanned aerial vehicle in an unknown airspace are remarkably improved.

The invention provides a camera-based AI visual recognition and prediction control method and related equipment, and the method comprises the steps: obtaining continuous image frames of a moving object, and recognizing the skeleton key point coordinates and appearance feature information of the moving object; inputting the skeleton key point coordinates and the appearance feature information into a preset time sequence neural network model to obtain a motion prediction track; performing space mapping on the centroid coordinates of the moving object according to the motion prediction trajectory, and determining focal length control parameters of the camera; meanwhile, angle mapping is carried out on the position offset of the moving object, and pan-tilt control parameters are determined; and tracking and shooting the moving object according to the focal length control parameter and the holder control parameter. Through the mode, the camera can complete the pre-adjustment of the focusing mechanism and the composition direction before the target motion trend occurs, thereby effectively reducing the delay error in the focal length switching process, and improving the clear imaging rate and composition stability of a high-speed motion main body.

The invention discloses a mechanical arm dynamic tracking and obstacle avoidance system and method based on reinforcement learning. The mechanical arm dynamic tracking and obstacle avoidance system comprises a sensing module, a target motion prediction module, a reinforcement learning decision module, a safety control and constraint module, an execution module and a training and simulation module. According to the invention, a complete system including environment perception, target motion prediction, reinforcement learning decision and execution control is constructed, so that the mechanical arm can predictively track a moving target in an unknown or dynamic environment, and real-time obstacle avoidance is carried out on a static or dynamic obstacle; and the requirements of safety, robustness and real-time performance of an industrial site are met in the whole control process.

The invention provides a ship berthing and leaving early warning method and system, and relates to the field of port ship management, and the method comprises the steps: obtaining ship position data, converting the ship position data into distribution density, extracting time sequence features, building a motion prediction relation, calculating a congestion risk coefficient, constructing water area risk distribution, and carrying out the route crossing calculation to obtain an avoidance demand value. And determining an avoidance sequence, calculating an avoidance route and a navigation time window, and generating time-sharing avoidance early warning information containing an early warning level and an avoidance instruction. The ship congestion risk can be predicted in advance, the navigation time is reasonably planned, and the navigation safety and efficiency of the port water area are effectively improved.

The invention relates to the technical field of vehicle trajectory prediction, and provides a vehicle trajectory prediction method and related equipment, and the method comprises the steps: training a driving intention recognition model and a trajectory prediction model through employing an improved clustering algorithm, and obtaining a trained driving intention recognition model and a trained trajectory prediction model; based on the adaptive graph and all the vehicle information, utilizing a trained driving intention recognition model to recognize the driving intention of the to-be-predicted vehicle, and utilizing a trained trajectory prediction model to predict and obtain an initial prediction trajectory of the to-be-predicted vehicle based on the driving intention of the to-be-predicted vehicle; and performing kinematics prediction on the to-be-predicted vehicle to obtain a motion prediction track, and superposing the motion prediction track and the initial prediction track of the vehicle to obtain a prediction track of the to-be-predicted vehicle. According to the method, the accuracy of trajectory prediction in different driving scenes and different prediction tasks can be improved, and the problem of prediction precision reduction caused by forgetting is solved.

A tasked robot for a deep reinforcement learning system using asymmetric self-play for robust multi-robot flocking is provided. The tasked robot includes a reinforcement learning control module and an auxiliary training module. The control module dynamically adjusts the robot's behavior to optimize decisions, featuring a target navigation model, a cluster behavior maintenance model, and a collision avoidance model. The auxiliary training module enhances environmental perception, enabling the robot to predict dynamic changes. The auxiliary training module includes a local environment grid estimation model to generate small-scale maps and a motion prediction model to forecast robot and obstacle trajectories.

The invention provides a robot training system and a robot control system. The robot training system comprises a sensor data acquisition module, an upper limb joint angle prediction module, a lower limb controller, an upper limb controller and a training module, the system collects robot joint angle, base posture, speed and foot contact force information through sensors; the upper limb motion prediction model predicts an upper limb joint angle at the next moment according to a historical angle and an operator instruction; the lower limb controller synthesizes the base information, the prediction angle, the lower limb instruction and the historical torque, and generates a lower limb joint torque control instruction for gait adjustment through a lower limb action prediction model; the upper limb controller outputs upper limb joint torque according to the instruction and the physical parameters; the training module trains the upper limb motion prediction model based on the prediction angle. The system can effectively counteract angular momentum and linear momentum changes caused by upper limb actions, and the overall balance of the robot is maintained.

A method for decoder-side motion vector derivation utilizes a spatial correlation. A video coding method and apparatus minimize discontinuities at block boundaries, in order to overcome disadvantages of motion prediction that performs motion compensation on a per block basis. The video coding method and the apparatus derive, during decoder-side motion vector derivation, motion vectors by taking into account spatial correlation of the current block with surrounding blocks rather than deriving motion vectors by considering only the cost of a current and prediction block.

The application belongs to the technical field of unmanned aerial vehicle navigation. A pipeline inspection unmanned aerial vehicle navigation method and system based on reinforcement learning are provided. By collecting inertial measurement, visual inertial odometry, cross array ranging and electronic governor feedback data, a state vector is constructed and high-frequency motion prediction and measurement update are realized through extended Kalman filtering to output high-precision unmanned aerial vehicle body state. The best anti-interference height is solved by combining the pipeline wind disturbance and rotor airflow disturbance model, and the external disturbance torque is decoupled. A dual-frequency reinforcement learning state is constructed and control instructions are output, and safe speed is obtained through safety shield filtering. With safe speed as the target, the motor speed is directly output through the reinforcement learning network to control flight, and the three-dimensional topological mapping of pipeline defects is completed synchronously. The application can realize high-precision navigation, active disturbance rejection and safe inspection in the pipeline, and improve the flight stability and operation reliability in complex aerodynamic environment.

The invention discloses a language-based action representation robot control scale difference bridging method, and belongs to the technical field of artificial intelligence and robot control crossing. The method comprises the following steps of: 1, converting seven-dimensional continuous actions of a robot into discrete tokens; 2, generating a unified natural language motion description which comprises a motion representation definition, an adaptive threshold and a hierarchical time window; and step 3, based on the conversion in the step 1 and the description in the step 2, firstly learning universal motion and language alignment knowledge, and then finely adjusting specific motion prediction so as to realize that the robot control scale difference is bridged based on language-based motion representation to improve the migration ability. According to the method, the obvious limitation of the existing scheme is solved: part of methods depend on manually designed physical rules or exclusive modules, and the adaptability is limited; and the other method needs to be supported by additional annotation or an external module, so that the cost is high and the generalization is insufficient.

The application discloses a multi-agent collaborative perception feature enhancement method based on context aggregation, relates to the technical field of automatic driving and intelligent networked vehicles, and comprises the following steps: acquiring current frame features and at least one frame of historical features; adaptively aligning the historical features based on a motion prediction network and a deformable convolution offset; generating semantic consistency weights and identifying time sequence discontinuous regions based on a correlation prediction network; fusing the current and historical features according to the weights and carrying out state space selective scanning, pooling, denoising and aggregation; generating multi-scale features, combining scene complexity, quality evaluation and time sequence consistency constraints to determine scale weights and then fusing; and inputting an LSTM to perform time sequence modeling and output enhanced context features. Invalid alignment is avoided through motion compensation and semantic consistency constraints, noise and scale jitter are suppressed through position-level denoising and multi-scale adaptive smoothing, and the collaborative perception robustness and detection accuracy are improved.

The invention discloses a key agent discrimination method fusing future motion prediction information. The key agent discrimination method is characterized by comprising the following steps: selecting a true value of a key agent; constructing a self-vehicle multi-modal feature and an agent feature fused with future motion prediction information; and building a key agent selector model. The method can utilize the deep neural network model to adaptively learn and discriminate the key agent in a complex traffic environment, fully considers the possible interaction condition of a long time sequence in the future, breaks through the short-sight bottleneck of a traditional rule-based discrimination method, and improves the accuracy and rationality of an interaction determination result. According to the method, the key agent is judged by using the time window and the dynamically adjusted distance threshold value, a judgment method in a real driving process is highly simulated, and the causal reasoning capability of a key obstacle selector model is improved, so that powerful support is provided for subsequent trajectory planning.

This invention discloses a method for predicting robot motion under time-varying delay conditions. The method determines the number of secondary predictors and their gain parameters based on the maximum time delay, the robot's initial joint position, and the initial joint velocity. The time-varying prediction interval of each secondary predictor is determined by the time-varying delay magnitude. The robot's state from time-delayed motion to the current motion interval is divided into motion sub-states, the same number as the number of secondary predictors. Secondary predictors are constructed, the same number as the number of motion sub-states. The secondary predictors are connected in series, with the time-varying delay robot joint position and velocity signals as input to the first secondary predictor, and the predicted values ​​output by the previous secondary predictor as input to each subsequent secondary predictor. The last secondary predictor outputs the predicted values ​​of the robot's actual joint position and actual joint velocity. This invention offers high prediction accuracy, wide applicability, ease of engineering application and promotion, and contributes to the development of robotics technology.

The invention relates to a method (100) for determining an uncertainty in connection with motion trajectory predictions of a vehicle (1), comprising the following: - Providing (101) at least one trained machine learning model, wherein the at least one machine learning model is trained to predict motion patterns based on sensor data and assigns ratings that reflect a probability of each predicted motion pattern, - Providing (102) at least two predicted motion pattern distributions (3) by means of appropriate predictions based on the sensor data by the at least one trained machine learning model, - Using (103) a Monte Carlo sampling to estimate an entropy of a respective predicted motion trajectory distribution (3), thereby obtaining an estimate of an aleatory uncertainty, - Using (104) a Monte Carlo sampling to estimate an entropy of the at least two predicted motion trajectory distributions (3), thereby obtaining an estimate of an overall uncertainty, - Determining (105) an epistemic uncertainty by subtracting the aleatory uncertainty from the total uncertainty, - Providing (106) the definite epistemic uncertainty and the aleatory uncertainty for the predicted motion trajectories of the at least one machine learning model. Furthermore, the invention relates to a computer program, a device and a storage medium for this purpose.

This invention discloses a method, device, intelligent equipment, and storage medium for predicting human motion. The method includes: acquiring kinematic information of a target human body; performing a pre-defined simplified dynamic calculation on the kinematic information to obtain dynamic information of the target human body; inputting the kinematic and dynamic information into a neural network encoder to obtain kinematic spatiotemporal features and dynamic spatiotemporal features, respectively; and inputting the kinematic and dynamic spatiotemporal features into a neural network decoder to obtain a predicted human motion result for the target human body. Through the human motion prediction method of this invention, kinematic and dynamic information are complementary and coupled in their representation of human motion, comprehensively describing human motion from different perspectives, and enabling more accurate and longer-term predictions of human motion posture over a certain period of time in the future.

This application relates to a method and system for loading 3D models using oblique photogrammetry based on dynamic trajectory prediction. The method includes the following steps: real-time acquisition of camera motion parameters and identification of camera motion modes; acquisition of moving region information and generation of a first preloaded tile set using a fan-shaped region prediction algorithm; generation of predicted spatiotemporal position information using a motion prediction model and collision detection with the scene octree index to generate a second preloaded tile set; acquisition of current load information and weight allocation to generate a preload list; identification of the priority order in the preload list and loading the 3D model target tiles into video memory using a multi-level cache scheduling mechanism. In summary, this application, by combining dynamic trajectory prediction with a multi-level cache scheduling mechanism, achieves intelligent response and efficient data loading for complex camera motion modes, significantly improving data loading efficiency and rendering smoothness in complex motion scenes.

The invention relates to the technical field of intelligent traffic and vehicle active safety, and discloses a sampling vehicle active safety early warning method fusing multi-source data. The method comprises the following steps: acquiring a historical flight state, real-time environment sensing data and task planning information of a sampling vehicle; performing space-time alignment to form a fusion data set; constructing a vehicle dynamics constraint model and an obstacle motion prediction model; coupling the two to generate a vehicle reachable area and obstacle intrusion probability distribution in a future time window; and if the weighted collision risk index exceeds a threshold value, the graded early warning is triggered. The system integrates a GNSS, an IMU, an atmospheric sensor, a laser radar and a task planning interface, and supports generation of an avoidance track after early warning. Through multi-source data deep fusion and physical constraint modeling, the false alarm rate is significantly reduced, and the autonomous safe flight capability of the sampling vehicle in a complex airspace is improved.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a smart park target identification method and system based on artificial intelligence, and the method comprises the following steps: accessing a park monitoring video stream, analyzing a camera topological structure to determine a blind area channel, and carrying out the mapping and historical image analysis to obtain a target identification result; obtaining a physical path length and an illumination change intensity index of a blind area channel, and extracting a track point sequence of a target in a visible area; the residence time after the target enters the blind area is monitored, the confidence coefficient weight of the visual features is calculated through a preset feature effectiveness attenuation model according to the speed fluctuation condition before the target enters the blind area and the environment complex factors of the blind area channel, and the confidence coefficient weight is in nonlinear attenuation along with the increase of the residence time. According to the method, the problems of appearance failure and large motion prediction deviation caused by long-time retention are effectively solved, and the recognition accuracy is greatly improved.

This invention relates to a method for continuous motion prediction in patients with cerebral palsy. The method includes: acquiring sEMG data, IMU data, and ground truth joint angles of the subjects and constructing a dataset; dividing the dataset into training, validation, and test sets for each subject according to time sequence; standardizing the collected EMG and IMU data for each subject to obtain standardized sequence data; inputting the standardized sequence data of each subject into a trained prediction network, which includes parallel TCN and GAT modules. The TCN module extracts IMU temporal features, and the GAT module extracts sEMG spatial features. The two types of features are fused through a multimodal fusion module and then input into a BiLSTM network for bidirectional temporal modeling to capture the bidirectional temporal dependence of movements. Finally, a regression output module outputs the standardized predicted angles. Compared with existing technologies, this invention has advantages such as achieving continuous and accurate motion prediction for patients with cerebral palsy and enhancing cross-subject generalization ability.

The invention relates to the technical field of tour guide robots, and provides a temple tour guide robot path planning method and system.The temple tour guide robot path planning method comprises the steps that a robot scans environment information to generate three-dimensional point cloud data; global path planning is optimized through a dynamic weight improved A algorithm, a path is predicted in combination with a machine learning model, and an optimal path is obtained; and moving according to the optimal path, and avoiding obstacles in combination with a motion model and a dynamic window method. The machine learning module can enable the robot to avoid a tourist dense area and shorten the moving time, so that the navigation time is shortened; and the path can be adjusted according to weather conditions. By integrating the technologies of path planning, motion prediction, speed optimization, intelligent decision making and the like, autonomous navigation of the robot in a complex dynamic environment is realized. According to the innovative method combining reactive planning and strategic waiting, the robot can flexibly adapt to various dynamic scenes, the service quality is guaranteed, and the practicability is better.

This invention discloses a method and device for predicting the extreme values ​​of dynamic submarine cable response, integrating physical mechanisms and dynamic temporal characteristics. By deeply fusing the dynamic submarine cable control equations with data-driven training through a physical information neural network, the mechanical mechanisms of the dynamic submarine cable are transformed into residual constraint terms, overcoming the poor generalization of purely data-driven methods. An LSTM temporal prediction model extracts the amplitude features of the six-degree-of-freedom motion of the floating body, compressing long-term dynamic coupling effects into physically interpretable features. A structure type judgment module is designed, covering two mainstream submarine cable structures without model reconstruction, significantly enhancing engineering applicability. A time-varying weight strategy dynamically balances data fitting loss, physical residual loss, and motion prediction loss, accelerating convergence while ensuring the prediction results conform to physical laws. By outputting the extreme values ​​of key node responses and motion amplitude features in real time, dynamic submarine cable strength failure early warning is provided for offshore wind power, greatly reducing the operation and maintenance costs of offshore wind power.

The invention relates to a ship heaving motion prediction method and system based on a lightweight context-aware network, and belongs to the technical field of ship and ocean engineering motion control, and the method comprises the following steps: signal collection and preprocessing, heaving motion multi-step prediction based on an LCGNet network, model training and optimization, and quantitative compression of the trained LCGNet model. Deploying at a shipborne edge computing device; when the system runs, historical heave data are collected in real time and input into the model, the step S2 is executed circularly, continuous online prediction of future heave motion is achieved, high-precision and multi-step-length heave motion real-time prediction is achieved under the extremely low parameter and calculation cost through innovative lightweight network structure design, and the prediction efficiency is improved. Therefore, the engineering deployment requirement of the shipborne active heave compensation system is met.

A movement prediction apparatus including first and second light output devices, a light reception device, and a processor. The first light output device outputs output-light having a spectral component of a first optical frequency comb of which a frequency comb interval is a first interval. The second light output device outputs reference light having a spectral component of a second optical frequency comb of which a frequency comb interval is a second interval. The light reception device receives combination light that is a combination of the output-light, reflection light that is the output-light reflected by a flying object rotor wing, and the reference light, and measures a distance to the rotor wing based on the combination light. The processor calculates a rotation amount that represents a rotational speed of the rotor wing based on a change amount of the measured distance, and predicts a movement of the flying object.

The application relates to the technical field of image processing, and discloses an interventional navigation method and system based on multi-modal image fusion. The method comprises the following steps: CT angiography data is segmented to form a three-dimensional vascular skeleton model, MRI perfusion data is calculated to generate a vascular flow field, and a vascular geometric constraint matrix is constructed; the constraint matrix and instrument position information are diffused and reconstructed, and an instrument motion prediction trajectory is output; an X-ray fluoroscopy image instrument tip posture is estimated, and instrument-vascular spatial correlation features are formed; safe navigation constraint parameters are calculated and generated through a distance field; and a path planning algorithm is used to generate real-time navigation instructions containing a pushing distance and a rotation angle. The application solves the technical problems of insufficient multi-modal image data fusion, inaccurate instrument motion prediction and imperfect safety constraint mechanism in a traditional interventional navigation system, and improves the precision, safety and real-time performance of interventional surgery navigation.

The application discloses a ground-non-ground fusion network high-speed terminal seamless switching method and system based on trajectory prediction and resource reservation, and belongs to the technical field of space-ground integrated communication. The core of the method is: through bidirectional motion prediction of the terminal and the NTN node, the switching area is accurately judged; the terminal speed, network delay and other factors are combined to adaptively calculate the dynamic switching window; the AI-enabled link scoring model is used to intelligently select the optimal target link; and the resource is cooperatively pre-reserved and uplink and downlink synchronization is completed before the window. The application has the beneficial effects that: the failure problem of traditional RSRP judgment in the NTN dynamic environment is overcome, the switching success rate and prediction accuracy in the high-speed mobile scene are significantly improved, the service interruption time and signaling overhead are greatly reduced, the continuity and reliability of communication are effectively guaranteed, and the application is suitable for intelligent networked vehicles, unmanned aerial vehicles and other high-dynamic service scenarios in 6G networks.

This invention relates to a method for predicting ship maneuvering motions under high sea states, comprising the following steps: establishing a three-dimensional numerical model of ship-wave coupling under high sea states based on computational fluid dynamics; installing sensors on the ship to collect real-time motion data of the ship under high sea states, and preprocessing the collected data; extracting feature parameters related to the ship's six degrees of freedom motion based on the preprocessed data; using the feature parameters as input, constructing a ship maneuvering motion prediction model using a neural network model, the output of which is the prediction result of the ship's six degrees of freedom motion over a future period; training and validating the prediction model using historical data; inputting the real-time collected data into the trained prediction model to perform real-time prediction of the ship's six degrees of freedom motion; generating ship maneuvering suggestions based on the prediction results; and providing real-time feedback and correction to the model based on the comparison between actual operating data and the prediction results. This invention has the effects of improving prediction accuracy, ensuring ship safety, and improving prediction precision.

The invention discloses a double-arm robot control method based on single-arm trajectory prior guidance. The method comprises the following steps: acquiring a double-arm robot data set; skill primitives of different action types are obtained based on a single-arm action prediction model, the skill primitives of different action types are selected for a left arm and a right arm, and next action intentions of the left arm and the right arm are predicted respectively; constructing a double-arm diffusion generation model for establishing a mapping relation from initial action distribution to real double-arm coordination action distribution; training a single-arm action prediction model and a double-arm diffusion generation model based on the double-arm robot training data set; and acquiring motion prediction priori of the left arm and the right arm, constructing Gaussian distribution taking the single-arm priori as a mean value and a preset variance as a radius, sampling from the Gaussian distribution to obtain an initial motion vector, inputting the initial motion vector into a trained double-arm diffusion generation model, and de-noising the initial motion vector to generate double-arm motion. According to the invention, the independence of single-arm motion and the collaboration of double-arm control can be considered, and the double-arm motion control effect is optimized.