179 results about "Motion direction" patented technology

The invention discloses an autonomous collision avoidance method and system for an unmanned ship in a mixed traffic mode, and the method comprises the following steps: obtaining the current position coordinate information, course information and surrounding environment information of the unmanned ship in real time through a ship positioning system and a ship recognition system on the unmanned ship; combining the target point, constructing a path plan and calculating the energy consumption of a navigation path, selecting a first navigation path, identifying and classifying static obstacles and dynamic obstacles, monitoring the static obstacles in real time, calculating and evaluating a detour difference, adjusting the path according to a collision avoidance rule, and generating a second navigation path; predicting the movement direction of the surrounding dynamic obstacles, and judging whether a collision risk exists or not; predicting a future position and adjusting the course to generate a third navigation path; and respectively evaluating the electric quantity threshold, the energy consumption threshold, the battery electric quantity and the energy consumption value, and adjusting an energy use strategy in real time. According to the invention, the accuracy of the navigation path is improved, and the safety of the unmanned ship in a complex environment is improved.

The invention provides an elevation map path planning method and system based on a terrain adaptive potential energy field, and relates to the technical field of path planning. Comprising the steps of calculating the gradient and roughness of the position of an intelligent agent, setting a terrain adaptive gain, dynamically adjusting the gradient and roughness, and obtaining a terrain adaptive repulsive force field and a composite potential energy field in combination with a basic repulsive force field; determining the motion direction of the agent based on the composite potential energy field, and determining the next position of the agent; in the process that the intelligent agent moves to the next position, whether local re-planning needs to be carried out on the next position of the intelligent agent or not is judged based on a preset environment change judgment rule; and when re-planning is needed, recalculating the terrain adaptive repulsive force field and the dynamic obstacle repulsive force field, updating the composite potential energy field, and re-determining the next position of the intelligent body. According to the invention, the terrain can be effectively sensed, the planning strategy is adaptively adjusted, and high precision, high efficiency and strong robustness are considered.

The invention provides a large-diameter long-pipe-section pipe pile hoisting path planning method and device, and relates to the technical field of cranes. According to the method, motion state parameters of pipe sections and pipe piles are obtained in real time, and in combination with three-dimensional environment sensing data of a hoisting area, a space operation diagram containing obstacles and a safety buffer area is constructed; generating a plurality of groups of candidate paths combined by speeds and steering angles in the fan-shaped region according to a set angle interval by taking the current motion direction as a reference; removing paths conflicting with obstacles through collision detection, and reserving an effective path set; based on multi-dimensional feature analysis of a security item index, a smoothness index and a progress index, a scoring function is constructed by presetting a weight, and an optimal path is dynamically selected; path planning is carried out through a fixed time interval until the pipe pile stably arrives at a target point; through real-time environment modeling and dynamic path planning, the problems of obstacle avoidance and efficiency of large-diameter pipe pile hoisting in a complex scene are solved, and hoisting safety and operation accuracy are remarkably improved.

The invention discloses a method for calculating a vehicle speed based on a moving unmanned aerial vehicle video, and belongs to the technical field of vehicle speed calculation methods through videos, and the method comprises the steps: extracting global feature points of a video frame through employing an SIFT operator, calculating a homography matrix H through combining with an RANSAC algorithm, projecting a previous frame image to a current frame, and carrying out the calculation of the current frame image; the method effectively eliminates the interference of the motion (such as flight attitude change and displacement) of the unmanned aerial vehicle on the vehicle speed calculation, solves the problem of speed measurement failure caused by the deviation of the motion directions of the unmanned aerial vehicle and the vehicle in the traditional method, enables the algorithm to be suitable for complex scenes such as curves and multi-angle flight of the unmanned aerial vehicle, improves the adaptability of the system in a dynamic environment, and improves the reliability of the system. According to the method, the YOLOv5-1 model is combined with the DeepSORT algorithm, high-precision detection and stable tracking of the vehicle are achieved, even under the complex conditions that the vehicle is partially shielded, illumination changes and the like, continuous detection frame association can still be kept, and reliable time sequence data support is provided for vehicle displacement calculation.

The invention provides an intelligent control method and system for obstacle avoidance sudden stop of an AMR robot and a medium, and the method comprises the steps: determining the position coordinates of an obstacle based on the original image data and point cloud data of the AMR robot in a target scene; the linear distance between the obstacle and the AMR robot is determined on the basis of the position coordinates in combination with real-time positioning information of the AMR robot, and the movement speed and the movement direction of the obstacle are determined on the basis of position time sequence changes of the position coordinates of the obstacle in continuous time; determining the shortest predicted meeting time between the obstacle and the AMR robot based on the linear distance, the movement speed and the movement direction in combination with the driving speed and the driving direction of the AMR robot; and if the shortest predicted meeting time is smaller than the corresponding dynamic safety threshold value, generating a sudden stop control instruction and sending the sudden stop control instruction to a motion control system so as to control the AMR robot to execute sudden stop operation. According to the invention, balance of safety and efficiency of the AMR in a dynamic environment is realized.

This invention discloses a target tracking method for unmanned aerial vehicles (UAVs), relating to the field of computer vision, aiming to solve the problems of target occlusion, rapid movement, and efficiency in dynamic scenes. The method includes: acquiring the target's bounding box and confidence score through a detector, and predicting the target's position and state using a noisy adaptive Kalman filter; proposing a confidence-based feature extraction and judgment strategy, performing IoU matching between the trajectory and the detection box; for targets with a matching degree below a threshold and a confidence score change rate above a threshold, extracting appearance features using a lightweight feature re-identification network (Rep-OSNet); otherwise, reusing features from the previous frame; designing a cost function that fuses motion direction and appearance similarity to perform cascaded matching between the target and the confirmed trajectory; performing secondary association matching on targets and trajectories that mismatch in the cascaded matching; updating the matched trajectory state using a noisy adaptive Kalman filter, deleting long-term lost mismatched trajectories, and outputting the target trajectory prediction box and ID.

The invention relates to a target track association method in a multi-target environment. The method comprises the following steps: firstly, preprocessing a new received trace point; secondly, predicting the next position of each track by using the existing track historical information and adopting a Kalman filtering algorithm, meanwhile, adjusting a state transition matrix and a process noise covariance matrix in real time by considering factors such as ocean current and wind direction in an offshore environment, and adjusting an observation noise covariance matrix according to meteorological information; then comprehensively considering distance, speed difference and motion direction difference to calculate a correlation degree magnitude between a new receiving trace point and each predicted track position; and finally, judging whether the newly received trace point belongs to the existing target track or forms a new target by adopting a threshold judgment method according to the correlation degree value, and updating the state of the track. The method can effectively cope with multi-target intersection, observation loss and clutter interference, improves the stability of a matching result, can realize continuous tracking of a marine target, can adjust parameters according to the environment and target characteristics, and has high adaptability.

The present invention discloses a multi-target tracking method based on weak clues and trajectory prediction, which belongs to the technical field of deep learning multi-target tracking. The present invention first sets a target detection model, and then sets a target tracking algorithm. When occlusion occurs, the position-speed-LSTM model is used to predict the future trajectory of the target and calculate the motion direction cost; when there is no occlusion, the Kalman filter is used to predict the trajectory; when calculating the position cost, the appearance feature model is used to extract the appearance features based on the designed mixed height intersection-union ratio using the altitude state as a weak clue. After obtaining each clue, the trajectory of the target is assigned using the strong and weak clues through the Hungarian algorithm. In order to balance and utilize each cost, the present invention realizes multi-target tracking through three-dimensional association based on the configured allocation cost strategy. The present invention solves the problems of occlusion, similar objects, appearance degradation, etc. to a certain extent, and enhances the model generalization ability and target tracking ability for complex scenes.

The invention relates to the technical field of inertial navigation, in particular to an indoor pedestrian positioning method based on a ResNet and Transform network, which comprises the following steps: firstly, acquiring IMU (Inertial Measurement Unit) data of a smart phone, acquiring a real motion track of a pedestrian through equipment carrying a Tango technology, and preprocessing the acquired data; extracting motion features of the IMU data through a ResNet network, and predicting instantaneous speed based on residual mapping between learning input data; then, inputting the predicted instantaneous speed sequence into a Transform model, capturing time sequence features of IMU data through a self-attention mechanism, and combining with speed prediction to optimize a trajectory in a motion direction; and finally, evaluating the accuracy of navigation positioning according to the predicted trajectory and the real trajectory. According to the method, the deep learning technology and IMU data characteristics are fully combined, the accuracy and robustness of trajectory prediction are remarkably improved, and the method has high practical application value.

The invention relates to a tumble detection method and device, electronic equipment and a storage medium, the tumble detection method is applied to wearable equipment, the tumble detection method comprises the following steps: obtaining original motion data, the original motion data being used for representing a motion direction and a motion acceleration; in response to the original motion data meeting a preset signal strength condition, awakening a fall detection mode; and in the fall detection mode, determining a fall detection result based on the original motion data. According to the tumble detection method, the tumble detection mode is awakened only when the original motion data meets the preset signal intensity condition, pre-screening of the original motion data is achieved, and whether the tumble detection mode needs to be awakened to determine the tumble detection result or not can be judged according to the signal intensity; the power consumption of the device when the fall detection mode is not awakened is reduced, the endurance time of the device is ensured, and the user experience is improved.

The invention discloses an unmanned ship path optimization method based on visual detection, and belongs to the technical field of unmanned ships, and the method comprises the steps: receiving first image information collected by a visual sensor carried by an unmanned ship and navigation parameter information from the unmanned ship, and constructing an environment perception model; performing motion trend prediction and unmanned ship collision risk comprehensive evaluation on the dynamic target; constructing a multi-dimensional threat assessment function, carrying out quantitative sorting on all known collision risk sources, defining a threat assessment index for each risk source, and optimizing a multi-target conflict logic; and dynamically adjusting the course and speed of the unmanned ship based on the output threat assessment result. In the implementation process of the technical scheme, the surrounding environment is perceived in real time through visual detection, parameters such as obstacle types, distances, relative speeds and movement directions are input into a multi-dimensional threat assessment function, and dynamic weighted sorting of different risk sources is realized, so that navigation path optimization is performed on the unmanned ship.

The invention relates to the technical field of path planning, and discloses a path tracking method and system based on prospective optimization and curvature self-adaption, a controller and a readable storage medium, and the method comprises the steps: obtaining the current pose and global path information of a target robot, and transferring the current pose and global path information to a vehicle body coordinate system; calculating a real-time look-ahead distance of the target robot according to the global path information to determine a look-ahead point; calculating a position error and an angle error according to the look-ahead point, and calculating a real-time PID parameter according to the position error and the angle error; the real-time path curvature is calculated according to the look-ahead point, the maximum linear speed is determined according to the real-time path curvature, and the current speed and the motion direction of the target robot are determined according to the maximum linear speed and the real-time PID parameters. According to the invention, through combination of the double PID controllers and the pure tracking algorithm, high-precision path tracking of the omni-directional mobile robot is realized.

The invention discloses a multi-person redirection walking and position prediction method based on an artificial potential field, and belongs to the technical field of virtual reality and robot navigation. Aiming at the problems of fixed repulsive force weight, high collision risk and frequent resetting in a multi-user dynamic scene in a traditional redirection walking algorithm and collision caused by insufficient short-term position prediction precision of predictive redirection, the weight distribution of repulsive force between an environment and users is adjusted in real time according to user density through a dynamic situation field weight optimization mechanism; the repulsive force intensity is enhanced when a user approaches an obstacle or a dense area, meanwhile, a safety distance and direction sensing mechanism is introduced, the user movement direction and obstacle included angle analysis is combined, deviation from a high-risk area is guided preferentially, and the invalid reset frequency is reduced. The prediction model adopts an encoder-decoder architecture, an attention mechanism is embedded in a multi-layer long-short-term memory network, key time sequence characteristics in a space-time trajectory are dynamically captured, and the short-term position prediction precision is improved.

The invention relates to the technical field of computer vision, in particular to a motion perception rockfall intelligent monitoring method and device based on deep learning. The method comprises the following steps: receiving a monitoring video from a monitoring area; constructing a rockfall detection and tracking framework based on a YOLOv8 target detection network and a ByteTrack target tracking algorithm; preprocessing the monitoring image, inputting the monitoring image into a YOLOv8 detection network to carry out moving target feature extraction, and outputting a rockfall detection result; and track matching is carried out by using a ByteTrack algorithm in combination with Euclidean distance and motion direction constraint, and a rockfall motion track is generated. According to the method, a traditional image algorithm and a deep learning algorithm are combined, moving target detection and multi-target tracking are constructed, and rockfall in a complex rock background is accurately captured from multiple dimensions; according to the method, the space coordinate position of the rockfall target can be accurately obtained, a complete movement track can be generated for each rockfall target, statistical information of rockfall events is effectively obtained, and a reliable basis is provided for disaster grade evaluation and early warning.

The invention discloses an unmanned air cushion boat autonomous obstacle avoidance path planning method and system based on a dynamic LOS, a medium and a program product, and the method comprises the steps: firstly, building point cloud data and a grid map, and determining a starting point and a target point; and initializing and dynamically maintaining a waypoint set, and generating LOS guide trajectories including trajectories of additional and expected duration. Then collision detection is carried out on the generated track, if collision is found, the movement direction of an obstacle is determined, and an A * algorithm is used for planning a shortest obstacle avoidance path; and moving the track points along the obstacle avoidance direction, generating a new LOS guide track, and repeating the collision detection until no collision exists. A safe distance threshold value is set, the shortest distance between the unmanned air cushion boat and the obstacle boat is predicted through a CPA method, and if the distance is smaller than the threshold value, an obstacle avoidance waypoint and a direction movement track point determined according to the coastal event rule are inserted. And finally, constructing an LOS guide track with an expected duration according to the current waypoint, and running along the track. Compared with a single LOS guiding method, the method has the advantages that the calculation amount is less, the operation speed is higher, and the obstacle avoidance function can be realized.

The invention relates to a pure vision autonomous navigation method for a mobile robot based on ground structure line guidance. The method comprises the following steps: binocular-inertia joint perception and state estimation; carrying out ground area constraint and two-dimensional line segment extraction; performing line segment screening and geometric fitting based on motion direction constraint; carrying out three-dimensional parametric modeling and category discrimination on multiple types of ground structure lines; calculating a pose error of a relative structure line and constructing a reference trajectory; performing closed-loop motion control based on the Lapunov stability; carrying out key position judgment and motion decision making based on an image template; compared with the prior art, by introducing a visual guidance mechanism based on a ground structure line and fully utilizing natural ground structure characteristics such as a space structure boundary line, stable autonomous navigation of the mobile robot is realized without depending on artificial markers such as a high-precision map, GNSS (Global Navigation Satellite System) positioning, a two-dimensional code and a magnetic stripe; the dependence of the system on complex sensors and environment modification is effectively reduced, and the deployment and maintenance cost is remarkably reduced; meanwhile, through unified modeling of multi-type structural line features, the adaptive capacity and robustness of the navigation method in various application scenes such as warehouse logistics, park inspection, in-plant transportation and semi-structured roads are improved, high navigation stability and reliability can be kept in tasks such as fixed route inspection, lane keeping and back-and-forth running, and the navigation method is suitable for large-scale popularization and application. And the method has good engineering practical value and popularization and application prospects.

The invention discloses a salamander-imitating robot omni-directional motion control method and system based on reinforcement learning, and belongs to the field of electromechanical system control. The method comprises the following steps: acquiring robot state information and a motion instruction; a periodic phase variable is generated through a phase integration module, and target actions of all joints are output through a track generator; designing a reward function based on behavior constraint, and constraining motion directivity and attitude stability; the morphological symmetry of the body structure of the salamander-imitating robot is utilized to symmetrically enhance the state-action sample so as to expand training data; and a reinforcement learning algorithm is adopted, and a stable and efficient omnidirectional motion control model is obtained according to a reward feedback and symmetric enhancement data iteration updating control strategy. The method can realize the generation of the omni-directional motion strategy driven by reinforcement learning without presetting a gait model, has the advantages of natural motion mode, high control precision and strong adaptability, and can be widely applied to the fields of complex terrain detection, intelligent inspection and the like.

A method for simulating temporomandibular joint (TMJ) surgery based on an artificial intelligence (AI) technology includes: obtaining temporal bone corners and a temporal bone edge contour in each TMJ image based on a motion synchronization degree of and a distance between any two corners in each TMJ image; obtaining a plurality of mandible corners and a mandible motion direction based on a distance between the temporal bone corners in each TMJ image and coordinates of the corners in the TMJ image in a coordinate system; obtaining a real mandible edge contour in each TMJ image based on an edge contour on which the mandible corners are located and the mandible motion direction in each TMJ image; and constructing a TMJ anatomical model based on the real mandible edge contours and the temporal bone edge contours in all TMJ images.

The invention discloses an object tracking method and device and electronic equipment, and relates to the technical field of vehicles and the technical field of automatic driving. The method comprises the following steps: acquiring historical state data, a current prediction state and to-be-matched data corresponding to a scene object; based on the historical state data, the current prediction state and the to-be-matched data, determining the motion direction similarity between the tracking frame and the candidate frame; calculating a region overlapping degree between the tracking frame and the candidate frame by using the current prediction state and the to-be-matched data; performing matching analysis on the tracking frame and the candidate frame according to the motion direction similarity and the region overlapping degree to obtain a matching result; and determining the candidate frame as the tracking frame matched with the scene object at the current moment in response to the fact that the matching result indicates that the tracking frame is successfully matched with the candidate frame. According to the invention, the technical problems of easy mismatching and low tracking accuracy of an object tracking method in the prior art are solved.

The invention discloses a visual special effect processing method and device, a storage medium and a terminal, relates to the technical field of computer graphic image processing, and aims at solving the problem that in a complex special effect scene, the number and motion states of particles in different areas are remarkably different. Counting the number and the movement direction of particles in the region, and evaluating the operation complex characteristics of the region; when the evaluation result is a high-complexity feature, region division is carried out, the particle state of each divided region is obtained, the ratio of dynamic particles to static particles is calculated, the particle pushing speed is detected, and the total loss of particle operation is calculated; setting a detection time point, extracting particle transparency and evaluating the transparency change degree; the total loss and the transparency change degree are synthesized, whether the particle pushing processing is switched into particle reactivation or not is judged, and therefore the special effect fluency is improved, and the particle loss is reduced.

A display method and an electronic device, which are applicable to the technical field of display, and can reduce the risk of motion sickness caused by the inconsistency between motion information seen by eyes and motion information perceived by other organs of the body. The electronic device moves along with the movement of a vehicle. The method comprises: determining a motion state of a vehicle, wherein the motion state indicates a motion direction and / or a motion speed of the vehicle, and the motion direction comprises a horizontal direction and a vertical direction; and then displaying, in a preset area in a display interface of an electronic device, a dynamic effect corresponding to the motion state. A dynamic effect corresponding to a motion state of a vehicle is displayed in a preset area in a display interface of an electronic device, and the motion state of the vehicle is simulated, so that motion information seen by eyes of a user is consistent with motion information perceived by other organs of the body.

The invention provides a course rapid initial alignment method and device for vehicle-mounted integrated navigation, and relates to the technical field of navigation calibration, and the method comprises the following steps: based on three-axis acceleration data, eliminating a component of gravitational acceleration in a horizontal direction through attitude compensation, and obtaining a horizontal longitudinal acceleration measurement value; carrying out plane coordinate projection based on the GNSS positioning data to obtain a track point sequence; determining the horizontal displacement length of the current track point and other track points in the track point sequence; on the basis of the horizontal longitudinal acceleration measurement value and the track point sequence, the motion direction is judged through the speed change trend and the horizontal acceleration direction, the current track angle is corrected, and a corrected course angle is obtained; and based on the corrected course angle and the Z-axis angular velocity of the gyroscope measured by the IMU module, estimating the course angle error and the zero offset of the gyroscope by adopting an adaptive Kalman filtering algorithm, and outputting the corrected course angle. According to the invention, fast and accurate course initialization of combined navigation in a low-speed equipment operation scene can be realized.

The application belongs to the technical field of radio positioning and motion control, and specifically discloses a kind of exoskeleton control filtering methods based on UWB and IMU fusion, comprising: deploying UWB base station, UWB label and IMU module;First displacement of knee joint in horizontal direction is measured by UWB label, first forward speed is calculated, second displacement of knee joint in horizontal direction is measured by IMU module, and second forward speed is calculated;State equation and observation equation are constructed, Sigma point is generated and untraceable Kalman filtering iteration is executed, and prediction result is optimized until the confidence of prediction result reaches a preset value.The application applies PDOA algorithm to solve angle, the angle and angular velocity measured by IMU module are fused by untraceable Kalman filtering, the motion direction error caused by single sensor is avoided, and the attitude estimation precision of exoskeleton control is improved.The application is suitable for exoskeleton control.

The application provides a crane group boom anti-collision early warning method and system based on digital twinning, which comprises the following steps: based on the data collected by a rotary encoder, an angle sensor, an inertial measurement unit, a laser radar and a ship body attitude measuring device, a dynamic three-dimensional digital twinning model is constructed and updated in real time; in the model, real-time path planning is performed on each crane boom, the spatial interference risk with the bulkhead, hatch coaming and other booms is evaluated, and a collision-free optimal action path is generated; the path nodes are dynamically risk evaluated, the minimum distance, relative speed, motion direction angle and ship body swing disturbance are comprehensively considered, and a collision risk score is generated; according to the score, hierarchical intervention control is implemented, the collision is avoided, and the attitude stability of the suspended goods is maintained. The application realizes the safety early warning and intelligent regulation and control of multi-machine cooperative operation under high dynamic sea conditions, and effectively improves the operation efficiency and safety.

The invention discloses a task-aware robot availability prediction method based on retrieval enhancement and cross-image alignment. The method comprises the following steps: constructing an accessibility memory bank containing object appearance characterization and interaction prior; performing semantic filtering on the memory bank based on the task tag, and retrieving a reference sample according to appearance similarity; a prediction process is divided into two parts, namely static contact point positioning and dynamic action direction prediction, contact points are correspondingly positioned by querying dense features of an image and a reference sample, and an action direction is regressed by a retrieval-enhanced cross-image alignment model; the model performs action vector modulation on the reference features, and performs gating cross-attention fusion with the query image features, and the fusion weight is jointly determined by the similarity and the learnable weight; and finally, the two-dimensional contact point and the action direction are lifted into a three-dimensional pose and a three-dimensional trajectory, and executable grabbing and post-contact actions are generated. According to the method, cross-category robust generalization under the condition of few samples can be realized, and the operation success rate of the robot is improved.

The embodiment of the present invention discloses a method, device, computer equipment and storage medium for detecting abnormal ship trajectories. The method includes: obtaining ship AIS data to obtain AIS data to be detected; obtaining longitude and latitude; calculating the angle between two movement directions and the positive direction of the x-axis; calculating the difference between the angle between two movement directions and the positive direction of the x-axis to obtain the trajectory angle; screening the trajectory point indexes that meet the requirements and retaining them in a list; grouping the list to obtain a grouping result; generating three different types of trajectories with different lengths according to the characteristics of S-type trajectories, O-type trajectories and irregular trajectories to obtain abnormal trajectories; using the DTW algorithm to standardize the grouping results and the three abnormal trajectories, then rotating the abnormal trajectories at different angles and calculating the minimum path value; determining the abnormal type to which the grouping result belongs. By implementing the method of the embodiment of the present invention, it is possible to achieve classification detection of abnormal trajectories, with high detection reliability and low time consumption.

The invention belongs to the technical field of virtual reality, and provides a ball training scene simulation method, device and equipment, a medium and a program product, and the method comprises the steps: obtaining the motion data, collected by multiple sensors, of a target player in a training field; the target player is any player participating in training; the motion data comprises a motion track, a motion speed, a motion direction and a three-dimensional position of the target player; generating an obstacle projection strategy according to the motion data, and sending the obstacle projection strategy to projection equipment; and the projection device projects an obstacle in the training field according to the obstacle projection strategy so as to simulate an obstacle scene for training of the target player. The movement condition of the target player is captured through multiple sensors, and the obstacle projection strategy is generated, so that the projection of the obstacle is dynamically adjusted according to the movement condition of the player, the dynamic nature and complexity of training scene simulation are improved, and the training requirements of the player can be better met.

The invention belongs to the technical field of radio positioning and motion control, and particularly discloses an exoskeleton control filtering method based on UWB and IMU fusion, and the method comprises the steps: deploying a UWB base station, a UWB tag, and an IMU module; the first displacement of the knee joint in the horizontal direction is measured through the UWB tag, the first forward speed is calculated, the second displacement of the knee joint in the horizontal direction is measured through the IMU module, and the second forward speed is calculated; and constructing a state equation and an observation equation, generating Sigma points, executing unscented Kalman filtering iteration, and optimizing a prediction result until the confidence coefficient of the prediction result reaches a preset value. According to the invention, the angle is calculated by using the PDOA algorithm, and the angle and the angular velocity measured by the IMU module are fused through the unscented Kalman filtering, so that the motion direction error caused by a single sensor is avoided, and the attitude estimation precision of exoskeleton control is improved. The method is suitable for exoskeleton control.

This application relates to a method, device, computer device, storage medium, and computer program product for generating an operation trajectory. The method includes: obtaining the motion parameters of the first motion component and the motion parameters of the second motion component; wherein the motion parameters include an initial velocity curve and an initial position; determining the distance between the first motion component and the second motion component according to the motion parameters of the first motion component and the motion parameters of the second motion component; determining the perpendicular distance of the processing component to the motion directions of the first motion component and the second motion component according to the distance; screening the positions of the processing components whose perpendicular distances meet a preset value range to obtain the operation trajectory of the processing component. Using this method can improve the accuracy of generating the operation trajectory.

Embodiments of the present application disclose a motion control method, device, equipment and medium, wherein the method comprises: acquiring a control force parameter on a motion control component of a target control object through a three-dimensional force sensor; determining a target motion parameter of at least one motion mechanism of the target control object according to a mapping relationship between the control force parameter and a preset motion parameter in a corresponding motion direction; and controlling the motion mechanism to move according to the target motion parameter. The technical solution of the embodiments of the present application solves the technical problem that the existing motion equipment cannot accurately perceive the control force to move, and can accurately perceive any direction and size of the control force to accurately control the motion of the target control object, thereby improving the accuracy and flexibility of the motion control of the target control object.