1147 results about "Kinematical model" patented technology

A system improves the design of artificial implant components for use in joint replacement surgeries. The system includes an anthropometric static image data analyzer, an implant model data generator, a kinematic model simulator, and a dynamic response data analyzer. The implant model data generator may also use image data of a joint in motion for modification of the implant model data used in the kinematic simulation. Dynamic response data generated by the kinematic model simulation is analyzed by the dynamic response data analyzer to generate differential data that may be used to further refine the implant model data.

The invention discloses an unmanned vehicle dynamic path programming method based on environment uncertainty, comprising steps of establishing a vehicle kinetics model, establishing a dynamic environment model and necessary conditions for anew programming a path, obtaining a motion state original value of the unmanned vehicle, a vehicle motion state original target value and a vehicle motion state candidate target value, generating a candidate path, obtaining an optimal path through selection based on a safety index and a rapidity index, and reprogramming the optimal path of the unmanned vehicle when the unmanned vehicle motion environment satisfies the condition where the new path can be programmed. Compared with the prior art, the invention can not only satisfy the safety requirement for safety driving, but guarantees the driving efficiency under the constraint of the vehicle model. The invention realizes the coordination optimization of the performance index through distribution of various weights, realizes the real-time planning under the condition where a plurality of dynamic obstacles exist, and effectively improves the safety of the unmanned vehicle driving.

The invention relates to a system and a method for assessment of walking and miming gait in human. The method is preferably based on the fusion of a portable device featuring inertial sensors and several new dedicated signal processing algorithms: the detection of specific temporal events and parameters, 5 optimized fusion and de-drifted integration of inertial signals, automatic and online virtual alignment of sensors module, 3D foot kinematics estimation, a kinematic model for automatic online heel and toe position estimation, and finally the extraction of relevant and clinically meaning-full outcome parameters. Advantageously including at least one wireless inertial module attached to foot, the system provides common spatio-temporal parameters (gait cycle time, stride length, and stride velocity), with the 10 advantage of being able to work in unconstrained condition such as during turning or running. It furthermore may provide original parameters for each gait cycle, both temporal (load, foot-flat and push duration) and spatial (foot clearance and turning angle), and their inter-cycles variability. The system and method according to the invention allows the assessment of various aspects of gait which have shown recently to be of premium importance in research and clinical field, including foot clearance, 15 turns, gait initiation and termination, running, or gait variability. The system may be light weight, easy to wear and use, and suitable for any application requiring objective and quantitative evaluation of gait without heavy laboratory settings.

A system for determining motion information including attitude and angular rate of a dynamic object. The system includes a magnetic-field sensing device to measure in the body coordinate frame of reference an intensity and/or direction of a magnetic field in three substantially orthogonal directions; an acceleration-sensing device adapted to measure total acceleration of the object in the body coordinate frame of reference; and a processor adapted to calculate attitude and angular rate by combining total acceleration measurement data and magnetic field measurement data with the kinematic model in a filter.

The invention discloses a multi-robot formation method, belonging to the field of intelligent control. The method comprises the following steps of: controlling the whole formation motion trail by the leader motion trail, firstly, determining a kinematics model of the leader, and determining the direction of the motion of the leader according to a resultant force of a repulsive force and a gravitational force; creating a motion model of following the leader by the follower, following the leader by the follower according to certain distance and angle, and determining the motion trail of the follower according to a motion model created by the artificial potential field; introducing an AdHoc between the leader and the follower, creating information feedback, and ensuring that no loss occurs in the process of following the leader by the follower. With the method provided by the invention, a multi-robot system can successfully avoid obstacles in the process of finishing tasks to reach a target point, and also can keep initial order in the whole process, implement real-time order control on multiple robots and be more suitable for some occasions where multiple robots are needed for finishing tasks (such as transporting, rescuing and the like) synchronously.

The invention relates to a parallel mechanism with three freedom degrees of twice rotation and once motion in a robot mechanism. The invention aims at providing the parallel mechanism which has the advantages of simple kinematic model, easy control, simple structure, high rapidity and low manufacturing cost. The parallel mechanism with three freedom degrees of twice rotation and once motion is characterized by comprising a mobile platform, a stand, a first branch, a second branch and a third branch, wherein the first branch, the second branch and the third branch are connected in parallel between the mobile platform and the stand; in the three branches, each branch is connected with a first single-freedom-degree motion pair, a second single-freedom-degree motion pair and a universal hinge in sequence from the stand to the mobile platform, wherein the third branch is additionally provided with a rotating pair between the second single-freedom-degree motion pair and the universal hinge; axial lines of first rotating shafts of the two universal hinges in the first branch and the second branch are parallel with each other; central points of the three universal hinges in the three branches are collinear; and the axial line of the first rotating shaft of the universal hinge in the third branch is vertical or parallel to the axial lines of the first rotating shafts of the two universal hinges in the first branch and the second branch.

The invention relates to a genetic-algorithm-based trajectory planning optimization method for a mobile mechanical arm. According to the technical scheme, the method comprises the following steps of first establishing a forward kinematic model and an inverse kinematic model of a multi-degree-of-freedom mobile mechanical arm; then fitting a joint trajectory by adopting a composite curve of a quartic polynomial mathematical model and a quintic polynomial mathematical model, and calculating solutions of the corresponding mathematical models according to a linear constraint equation; next selecting a trajectory optimization target according to the principles of shortest motion time, minimum spatial motion distance and less than or equal to maximum set joint torque of the mobile mechanical arm; and finally globally optimizing the optimization target by utilizing a genetic algorithm to obtain an optimal trajectory curve of an end actuator of the mechanical arm. According to the method, the trajectory planning efficiency and the tracking accuracy of the mechanical arm are improved, and the problems of real-time trajectory planning of the mobile mechanical arm and trajectory planning optimization and control of the mechanical arm in an uncertain environment are also solved; and the trajectory planning optimization method for the mobile mechanical arm is effective.

A method for estimating joint angles of a multi-segment limb from inertial sensor data accurately estimates and tracks the orientations of multiple segments of the limb as a function of time using data from a single inertial measurement unit worn at the distal end of the limb. Estimated joint angles are computed from measured inertial data as a function of time in a single step using a nonlinear state space estimator. The estimator preferably includes a tracking filter such as an unscented Kalman filter or particle filter. The nonlinear state space estimator incorporates state space evolution equations based on a kinematic model of the multi-segment limb.

The invention provides a side wind resistance landing flight track tracking control method based on side direction guide, which belongs to the flight track tracking control technology of airplanes landed under the side wind condition. The flight track tracking control method based on side direction guide is adopted, and comprises the following steps: calculating the inner ring posture control commands through a side direction guide law by the flight track tracking side direction deviation; generating steering engine control commands by an inner ring posture controller for controlling the airplane posture; and calculating the current side direction deviation by the flight line switching logics according to the current state of the airplane for the side direction guide law calculation. A transverse side direction movement model of the airplane is used for simulating the dynamic and kinematic model of the airplane. When being adopted, the side direction flight track control system based on the ground speed guide has better inhabitation effect on the side wind phenomena such as side direction gust wind, air turbulence, side direction wind shear and the like when the airplane is landed. At the same time, the side direction flight line switching logics can also meet the requirement of approach flight of the airplane under the side wind condition.

The invention relates to a method for controlling a rigid spacecraft for target attitude tracking, and belongs to the technical field of the high-precision and high-stability attitude tracking control of spacecrafts. The method solves the problem that when the attitude tracking spacecraft runs in a low orbit in outer space, the conventional control method cannot eliminate the inherent flutter of a sliding mode variable structure. The method comprises the following steps: 1, establishing a kinetic model and a kinematic model of the rigid spacecraft; 2, setting an attitude tracking error and anexpected attitude parameter of the rigid spacecraft, and combining the attitude tracking error and the expectation attitude parameter with the kinetic model and the kinematic model to establish a mathematical model for the attitude tracking; 3, adopting a control algorithm of a sliding mode variable structure controller to adjust a control law of the mathematical model which is established in thestep 2 and is used for the attitude tracking, and simultaneously combining an observation result of a disturbance observer to modify the control law; and 4, controlling the rigid spacecraft by using the modified control law obtained in the step 3 to realize the attitude tracking. The method is suitable for the attitude tracking of targets running in the outer space.

A position-tracking system for tracking the position of an object is disclosed. According to various embodiments, the tracking system includes a tracking device that is connected to or otherwise affixed to the object to be tracked. The tracking device may include, among other things, an inertial sensor assembly, radio transceivers and a processor. The position tracking system may also include a host processing system that is in communication with the tracking device. The position tracking system may provide variable-resolution position information based on the environment in which the object is moving. In a “wide resolution” area, the system may compute a general position for the object based on a wireless telephone network Cell-ID/map correlation architecture. In a high-resolution area, greater position resolution may be realized from the combination of a wireless aiding system and inputs from the inertial sensors. In the high-resolution mode, the system may exploit distinct patterns of motion that can be identified as motion “signatures” that are characteristic of certain types of motion. Kinematic (or object movement) models may be constructed based on these motion signatures and the position tracking system may estimate the state of the object based on the kinematic model for the current mode of the object. Adaptive and cascaded Kalman filtering may be employed in the analysis to more accurately estimate the position and velocity of the object based on the motion pattern identified.

The invention discloses a trajectory tracking sliding mode control method for a spraying mobile robot. The method comprises the following steps of: performing mechanism analysis on a mobile robot, and establishing a mobile robot kinematic model with non-integrity constraint; establishing a controlled object mathematical model of each branch controller of a wheeled mobile robot provided with a motor driving shaft disturbance term; identifying a traveling path by utilizing a computer vision system, and determining an expected motion track of each branch driving motor according to the kinematic model deduced in the previous step; detecting the rotating speed of the motor, calculating the actual motion angular velocity and actual motion angular acceleration of left and right driving motors of the mobile robot, and calculating the deviation and deviation derivative between the expected angular velocity and the actual angular velocity of each driving motor; establishing a sliding mode switching function which meets the speed control requirement of the driving motor; determining the sliding mode controller control quantity of the left and right driving motors of the mobile robot on the basis of the sliding mode surface function s; and respectively transmitting the control quantity of the motor of the mobile robot to the left and right driving motors.

The invention discloses an automotive adaptive cruise control method taking multiple targets into consideration. A layer control strategy is utilized, upper layer control can decide an expected longitudinal acceleration according to a target vehicle and a current state of a controlled vehicle, and lower layer control can track the expected longitudinal acceleration through a reverse recursion method. The automotive adaptive cruise control method comprises the following steps: a mutual longitudinal dynamitic model between two vehicles is established, a design model predicating controller can obtain the expected distance between two vehicles according to a constant time headway strategy, a model prediction control algorithm is utilized to decide an expected longitudinal acceleration for tracking the expected distance between the two vehicles, vehicle control work conditions are divided into a driving work condition and a braking work condition, reverse longitudinal dynamic models for the two work conditions are established respectively according to a vehicle driving equation, an expected throttle percentage is obtained according to the vehicle reverse longitudinal dynamic model and the expected acceleration in the driving work condition, and an expected brake pedal openness is obtained according to the expected acceleration in the braking work condition.

The invention provides a cooperative landing method for multiple unmanned aerial vehicles. The method comprises the followings steps of: establishing an Ad Hoc communication network for multiple unmanned aerial vehicles; searching for the self position and landing landmark of each unmanned aerial vehicle by using an onboard sensor, and transmitting a data packet to the neighboring unmanned aerial vehicle in a broadcasting way on the basis of the Ad Hoc communication network, wherein the data packet comprises position information and state information; calculating relative distances and relative angles between each unmanned aerial vehicle and the landing mark as well as between the unmanned aerial vehicle and the neighboring unmanned aerial vehicle in combination with self information of the unmanned aerial vehicle according to the received data packet; and constructing an environmental space potential field, calculating the attraction Fatt of the landing mark and repulsive force Frep from the neighboring unmanned aerial vehicles, calculating potential field resultant force, building a kinematic model, and determining the movement track of each unmanned aerial vehicle. Due to the adoption of the cooperative landing method, onboard sensor information can be shared and controlled cooperatively when an unmanned helicopter cluster performs a task, the safety of cluster landing is ensured, and high adaptability and expandability are achieved.

The invention discloses a human-vehicle cooperative steering control method considering real-time allocation of driving rights. The method solves the problem of driving right allocation between drivers and vehicle automatic driving controllers in a human-vehicle cooperative driving process. The method comprises the specific steps of 1, establishing a vehicle dynamics model and a vehicle kinematicsmodel; 2, establishing a vehicle automatic driving controller; 3, establishing a human-vehicle co-driving system model; 4, adopting a model prediction method for designing a human-vehicle co-drivingsystem controller; 5, allocating the driving rights, calculating a control amount, executing the control amount, and achieving the process of controlling a vehicle to steer through the cooperation ofa driver and the vehicle automatic driving controller. By means of the method, under the condition that the vehicle automatic driving controller and the driver drive the vehicle together, the allocation of the steering driving rights of the vehicle automatic driving controller and the driver can be optimized in real time online, and the operation of steering the vehicle is cooperatively completedby the vehicle automatic driving controller and the driver.

The invention discloses an intelligent automobile path tracking hybrid control method, and belongs to the technical field of intelligent automobile lateral control. The intelligent automobile path tracking hybrid control method comprises the following steps that step 1, an automobile lateral control preview kinematics model at a low speed is established; step 2, an automobile lateral control dynamic model at a high speed is established; and step 3, a path tracking hybrid controller is designed, the path tracking hybrid controller comprises a lateral controller, a supervisor and a switching stability fuzzy controller, wherein the lateral controller is designed based on PID control and model predictive control, the supervisor determines a tracking mode based on longitudinal vehicle speed, and the switching stability fuzzy controller is designed based on a fuzzy control theory. The intelligent automobile path tracking hybrid control method effectively solves the problem of lateral controlperformance requirements under high-speed and low-speed working conditions of an intelligent automobile, and the easy realization, accuracy and stability of path tracking of the intelligent automobile are improved.

The present teaching relates to method, system, medium, and implementation of human-like vehicle control for an autonomous vehicle. Information related to a target motion to be achieved by the autonomous vehicle is received, wherein the information includes a current vehicle state of the autonomous vehicle. A first vehicle control signal is generated with respect to the target motion and the given vehicle state in accordance with a vehicle kinematic model. A second vehicle control signal is generated in accordance with a human-like vehicle control model, with respect to the target motion, the given vehicle state, and the first vehicle control signal, wherein the second vehicle control signal modifies the first vehicle control signal to achieve human-like vehicle control behavior.

Methods, apparatus, and systems for operating a surgical system. In accordance with a method, a position of a surgical instrument is measured, the surgical instrument being included in a mechanical assembly having a plurality of joints and a first number of degrees of freedom, the position of the surgical instrument being measured for each of a second number of degrees of freedom of the surgical instrument. The method further includes estimating a position of each of the joints, where estimating the position of each joint includes applying the position measurements to at least one kinematic model of the mechanical assembly, the kinematic model having a third number of degrees of freedom greater than the first number of degrees of freedom. The method further includes controlling the mechanical assembly based on the estimated position of the joints.

An automated method for the generation of (i) human models comprehensive of shape and joint centers information and/or (ii) subject specific models from multiple video streams is provided. To achieve these objectives, a kinematic model is learnt space from a training data set. The training data set includes kinematic models associated with corresponding morphological models. A shape model is identified as well as one or more poses of the subject. The learnt kinematic model space and the identified shape model are combined to generate a full body model of the subject starting from as few as one-static pose. Further, to generate a full body model of an arbitrary human subject, the learnt kinematic model space and the identified shape model are combined using a parameter set. The invention is applicable for fully automatic markerless motion capture and generation of complete human models.

The invention discloses a method for quick solution of six-degree-of-freedom humanoid dexterous arm inverse kinematics. The method includes: firstly, setting up a six-degree-of-freedom human arm simulated joint structural model, solving an elbow joint angle and two joint angles of the shoulder joint according to a planar relation formed by the shoulder center, the elbow center, the wrist center and the neck center of the dexterous arm and the tail end position of the dexterous arm, and further solving three joint angles of the wrist joint by the aid of the dexterous arm tail-end posture relation; and calculating position error of a dexterous arm target link to prove effectiveness of the solution of the inverse kinematics by means of a positive kinematic model. Compared with a numerical solving method for robot inverse kinematics, the method for quick solution of six-degree-of-freedom humanoid dexterous arm inverse kinematics is high in precision and quick in speed, and can be used for humanoid dexterous arm operational tasks having high requirements on precision and instantaneity.

The invention discloses a driving trajectory predicting system integrating a kinematic model and a behavioral cognition model. The driving trajectory predicting system is characterized in that an interactive mixing module subjects the prediction result of each prediction module at a last time moment to interactive mixing to output a mixed result used for prediction at a next time moment; the prediction module comprises a behavioral cognition trajectory prediction module based on behavioral cognition and a motion trajectory prediction module based on kinematics; the behavioral cognition trajectory prediction module and the motion trajectory prediction module performs prediction according to the mixed result output by the interactive mixing module and output respective prediction results including a vehicle position and a covariance matrix; a fusion updating module fuses a final prediction result according to the prediction results, updates a weight coefficient, and outputs a vehicle position and a covariance matrix at a certain time moment in the future. The driving trajectory predicting system can continuously estimate the position state and the driving behavior of the vehicle in a vehicle driving process, predicts a driving trajectory, and provides assistance for intelligent driving decision.