9 results about "Robot modeling" patented technology

The invention discloses a multi-machine system number-real consistency error collaborative prediction method and system based on multi-agent reinforcement learning, and belongs to the technical field of multi-robot systems and artificial intelligence. According to the method, each robot is modeled as an intelligent agent, a centralized training distributed execution framework is adopted, and collaborative prediction of errors is achieved. Each agent collects own, neighbor and environment state information in real time, multi-source information integration is carried out through feature embedding and gating fusion, the influence of different information sources on errors is dynamically evaluated by adopting a layered attention mechanism, and propagation and coupling rules of the errors in a cluster are effectively modeled. In the training stage, the centralized commentator network is utilized to evaluate the global prediction performance, the cooperative optimization of each executor network is guided, and the prediction precision is improved. According to the method, active and collaborative prediction of the multi-machine system number-real consistency error is realized, and a key technical support is provided for error feed-forward compensation and system reliability guarantee.

This invention discloses a spatial domain impedance learning control method for a five-bar parallel robot, comprising the following steps: Step 1, establishing an Eulerian-Lagrange model of the five-bar parallel robot; Step 2, establishing a human-machine system interaction force model based on the trajectory tracking error of the parallel robot; Step 3, estimating impedance dynamics based on the Eulerian-Lagrange model; Step 4, designing an iterative learning strategy for human-machine interaction force based on the human-machine system interaction force model and impedance dynamics; Step 5, conducting control tests on the five-bar parallel robot using the iterative learning strategy for human-machine interaction force. The learning control method proposed in this invention ensures the convergence of tracking errors, considers the uncertainties in robot modeling, and further guarantees the safety of human-machine interaction.

The application discloses a kind of industrial robot dynamics identification method with nonlinear viscous friction, it is related to robot dynamics modeling and control field.The method is considered in robot modeling, nonlinear term of viscous friction, different from traditional coulomb viscous friction model, its content includes robot friction model construction, excitation trajectory model selection, parameter optimization and dynamics parameter solving.In excitation trajectory selection optimization, the trajectory model of five order Fourier series and five polynomials is selected, and the genetic algorithm with penalty function is used to optimize the parameters of excitation trajectory;In solving process, based on interior point method, iteration fitting is carried out, and complete dynamics parameters of robot are obtained.The friction model considered in the application has higher modeling accuracy, and the problem of system jitter at start and end position is overcome, the efficiency and accuracy of robot dynamics parameter identification are improved, and can be used in robot mobile handling, polishing and polishing and heavy load hydraulic industrial scene.

A parallel robot modeling method considering omni-directional motion error of flexible joint comprises the following steps: S1: defining mechanism end output pose, and establishing kinematics model of robot based on closed loop vector method; S2: combining motion variable of flexible joint to construct flexible joint output pose, and the motion variable comprises displacement amount and rotation angle amount of flexible joint in non-functional direction; S3: establishing local coordinate system in each flexible joint and moving joint in branch chain, and constructing homogeneous coordinate transformation matrix and transmission relationship among each matrix; S4: constructing mapping relationship between motion variable of each flexible joint and end output pose; S5: constructing force and torque balance equation of each flexible joint and moving joint in branch chain; S6: solving end output pose of mechanism by simultaneously solving each equation.The present application considers motion error of flexible joint in non-functional direction, establishes robot model with higher precision, and ensures operation precision and reliability of robot.

A simulation system includes: an acquisition unit that acquires sensor information detected by plural of sensors installed in an area in which a human worker moves and performs work; a generation unit that generates model data obtained by modeling a robot for replacing work of the human worker on the basis of the sensor information; and a display control unit that performs control to display the robot on the basis of the model data.

The invention relates to the technical field of robot modeling, and provides a robot URDF model construction method and device, electronic equipment and a storage medium. The method comprises the following steps: receiving task description input by a user; clarifying and optimizing the task description to generate an optimized task description; performing semantic analysis on the optimized task description, and extracting task demand parameters; selecting a plurality of candidate components from a robot component library based on the task demand parameters; before the selected candidate components are combined, executing the following cross-domain compatibility pre-verification on the selected candidate components; according to combination constraints among the components, combining the candidate components subjected to cross-domain compatibility pre-verification, and generating a plurality of candidate URDF (Uniform Resource Description Framework) models; and based on the task demand parameters, evaluating the candidate URDF model in combination with a Pareto leading face to obtain an optimal URDF model, and outputting the optimal URDF model. According to the technical scheme, the robot modeling threshold is remarkably reduced, and the matching efficiency of the model and the task is improved.

The invention discloses a robot inverse kinematics experiment teaching method and device, a storage medium and a program product. The method comprises the following steps that 1, the structure and parameters of a robot are analyzed; 2, drawing a robot model sketch according to the structure and parameters of the robot; step 3, setting a robot joint coordinate system according to the robot model sketch; 4, according to the robot joint coordinate system, robot DH parameters are solved; carrying out DH parameter judgment, and if the DH parameter is correct, carrying out the next step; otherwise, repeating the steps 1 to 4; 5, robot modeling is carried out according to the solved DH parameters; step 6, setting a motion path of an end effector of the robot; 7, discretely generating a motion track in the Matlab; 8, according to an inverse kinematics equation, solving joint parameters corresponding to the discretized motion trail; 9, robot joint parameters are output; robot joint parameters are judged, and if the joint parameters are correct, the next step is carried out; otherwise, repeating the steps 8 to 9; 10, a robot controller is read, and joint parameters are input; step 11, controlling the robot to run on line, judging a running track, and if the running track is the same as a set track, completing the experiment; otherwise, repeating the steps 7 to 11. According to the method, the inverse kinematics solution and verification experiment of robotics can be conveniently carried out.

The invention discloses a robot distributed control method and system based on a fluid dynamics principle, and the method comprises the steps: obtaining the image information of a target region, extracting the contour of a target pattern, and constructing a truncated signed distance field to generate target guide potential energy; the robot is uniformly divided into a plurality of sub-regions for initial deployment; all the robots are modeled as particle elements, and interaction force between the robots is constructed through a smoothed particle fluid mechanics method; a distributed control strategy is designed for each robot, the control input of the distributed control strategy is the sum of target guiding force and interaction force, and meanwhile damping control is combined to stabilize system response; in the control process, as the robots enter the target area, the target guiding force is gradually attenuated to zero, only the interaction force between the robots is reserved, and therefore automatic construction of the target pattern is achieved. According to the invention, a completely distributed pattern construction and space regulation and control strategy is realized, and the method has good expansibility, adaptive ability and control stability.

The application relates to the technical field of robot modeling, and provides a robot URDF model construction method and device, electronic equipment and a storage medium. The method comprises the following steps: receiving a task description input by a user; clarifying and optimizing the task description to generate an optimized task description; performing semantic analysis on the optimized task description to extract task requirement parameters; selecting multiple candidate components from a robot component library based on the task requirement parameters; performing cross-domain compatibility pre-verification on the selected candidate components before the selected candidate components are combined; combining the candidate components that have passed the cross-domain compatibility pre-verification according to combination constraints between the components to generate multiple candidate URDF models; and evaluating the candidate URDF models based on the task requirement parameters and in combination with a Pareto frontier surface to obtain an optimal URDF model and output the optimal URDF model. The technical scheme significantly reduces the threshold of robot modeling and improves the matching efficiency of the model and the task.