572 results about "Robot dynamic" patented technology

The invention discloses a system and a method for task planning of an intelligent robot with a body based on multi-dimensional situation awareness, and particularly relates to the technical field of task planning of the intelligent robot with the body, space-time alignment is carried out on asynchronous heterogeneous data generated by a multi-modal sensor channel, and cross-modal space-time features are extracted through a cross-modal feature fusion network; a fusion situation matrix is generated, dynamic causal modeling is used to update association strength among the multi-modal data, an anti-factual reasoning engine is used to identify and trace abnormities, and an abnormities traceability result is output; dynamically adjusting the reliability weight of each sensing channel through a multi-modal credibility evaluation model by using the fusion situation matrix and an abnormal traceability result; and on the basis of the reliability weight, inputting the fusion situation matrix into a real robot dynamic model and a digital twin virtual model, executing collaborative predictive control, and starting an adaptive rule evolution mechanism when a safety score is lower than a threshold value, thereby solving the problem of fusion matrix distortion in dynamic obstacle avoidance and precise grabbing tasks.

The invention relates to the technical field of intelligent assessment and decision making, in particular to a robot dynamic risk assessment and decision making system and method based on multi-modal perception, and the system comprises a multi-modal sensor module which is used for collecting environment vision, acoustics, mechanics and position data in real time; the edge calculation unit is used for carrying out space-time alignment and feature fusion on the sensor data; the dynamic risk assessment model is used for integrating the environment uncertainty quantification module and the robot state prediction module based on a reinforcement learning framework; the decision execution interface is used for outputting a risk level and obstacle avoidance, speed reduction and shutdown instructions; by integrating visual, acoustic, mechanical and position multi-source sensor data and the like, the system can comprehensively capture various risk factors in a complex dynamic environment, so that the defect that a traditional single sensor system is insufficient in sensing dimension is overcome, and the system is particularly suitable for terrains and weather conditions with variable regions.

The invention discloses an unhooking robot dynamic unhooking control method and system based on a feedback mechanism, and relates to the technical field of robot control, and the method comprises the steps: obtaining an unhooking robot, moving the unhooking robot to a preset unhooking position, and starting a visual sensor to collect unhooking image information and environment perception image information; edge detection and feature extraction are carried out on the unhooking image information, and hook space coordinate information is determined; performing feature abstraction on the environment perception image information to obtain a target unhooking path; and dynamic characteristic parameters and motion constraint information of the unhooking robot are obtained, unhooking analysis is carried out, unhooking control parameters are obtained, and dynamic unhooking control is carried out. The technical problems that in the prior art, an unhooking robot is low in unhooking efficiency and insufficient in control precision, and certain safety risks exist are solved, and the technical effects that the unhooking efficiency and the control precision of the unhooking robot are improved, and operation safety is guaranteed are achieved.

The invention discloses a dynamic path planning and self-adaptive control method and system for a coating robot, and relates to the technical field of coating automation. The method comprises the following steps: acquiring point cloud data through three-dimensional scanning equipment, constructing a dynamically updated workpiece curved surface model, and extracting curvature, edge and high-curvature mutation region features; a spraying path is generated based on a reinforcement learning algorithm, and path density, speed and coating supply are dynamically adjusted for a high-curvature area; distance, force feedback and environment parameters are fused, and mechanical arm and spray gun parameters are dynamically adjusted; dividing operation sub-areas and distributing tasks based on robot capability characteristics to realize multi-machine cooperation; and fault redundancy control and multispectral imaging are added to optimize the coating quality. The system comprises a sensing module, a decision-making module, an execution module, a redundancy control module and a communication module. According to the invention, the uniformity of the complex curved surface coating, the multi-machine cooperation efficiency and the system anti-interference capability are improved, and the method is suitable for spraying large workpieces such as aircraft fuselages and high-speed rail vehicle bodies.

The invention discloses a robot dynamic environment adaptive sensing and navigation system based on a three-dimensional laser radar, relates to the technical field of robots, and solves the technical problems that comprehensive environment information is difficult to obtain, and the weight is difficult to adjust by fusing weather types and sensor confidence coefficients. Comprising the following steps: generating original point cloud data by combining a bionic compound eye type laser radar with a silicon photon integrated chip; marking point clouds based on a KITTI data set, performing preprocessing, training a Transform model to output a dynamic obstacle mask, and filtering background point clouds; a weather detection model is constructed, the weight is dynamically adjusted according to the weather type and the sensor confidence coefficient, and position and attitude estimation is fused; laser radar point cloud constructs a geometric map, a camera depth map generates a dense map, and semantic tags are mapped to generate an environmental semantic map; and converting the environmental semantic map into a three-dimensional grid map, and planning an obstacle avoidance path based on the grid map by using an A * algorithm.

The invention provides an industrial robot control system and method. The system comprises a trajectory planning module, a dynamic compensation module, a feedback control module, an execution module, a sensor module and a data storage module. The trajectory planning module generates an expected trajectory of an end effector of the robot; the dynamic compensation module calculates a dynamic compensation torque based on the expected trajectory and a robot dynamics model; the feedback control module generates feedback control torque according to the deviation between the real-time pose data acquired by the sensor module and the expected trajectory; the execution module drives robot joints to move according to the synthesized moment of the dynamic compensation moment and the feedback control moment; the sensor module collects robot joint angle, angular velocity and torque data in real time; the data storage module stores robot dynamic model parameters, control algorithm parameters and historical motion data. High-precision and smooth robot motion control can be achieved, the dynamic response and stability of the system are improved, and energy consumption and motion errors are reduced.

The invention discloses a robot dynamic path tracking method and system, and the method comprises the steps: constructing a nonlinear model prediction control model for a differential robot chassis; building a target cost function by considering the obstacle environment information around the robot; adjusting a control input vector in the nonlinear model predictive control model, performing real-time tracking control on the robot, calculating a target cost function value, taking the corresponding control input vector as an optimal control input vector when the target cost function value is minimum, and performing real-time tracking control on the robot. Performing dynamic path tracking control on the robot by using the optimal control input vector; the method has the advantages that real-time obstacle avoidance is reliably carried out while the robot tracks the path, complicated path planning calculation steps are not needed, the calculation process is simple, and the efficiency is high.

The invention belongs to the technical field of data processing, and particularly discloses a dynamic path planning method and system for an inspection robot, and the method comprises the steps: collecting field data through a sensor, processing a laser scanning point cloud and a camera image through a positioning mapping algorithm, and fusing multi-source information to generate a real-time map. Obtaining a three-dimensional environment model containing a dynamic obstacle position; according to the three-dimensional environment model, obstacle features are extracted, a deep learning model is adopted to identify moving personnel and randomly placed goods, obstacle types and movement tracks are judged, and a classified obstacle data set is obtained; through the classified obstacle data set, the distance and the relative speed between the obstacle and the current position of the robot are calculated, and if the distance is smaller than a preset threshold value and the speed is larger than zero, high-priority interference is marked; according to the invention, the problem of inaccurate dynamic obstacle identification and obstacle avoidance in a complex scene in the prior art is solved.

The invention discloses a building construction robot dynamic scheduling method and system based on artificial intelligence, and particularly relates to the technical field of building construction. Potential schedulable building construction robots are screened by analyzing process requirements and resource allocation of construction tasks; based on the robot task execution record, the operation stability is evaluated; the scheduling adaptability of the robot in different construction technologies is evaluated by analyzing task switching flexibility; and the real-time performance of the scheduling demand of the robot is judged by integrating the operation stability and the scheduling adaptation degree. And the flexibility demand intensity of robot scheduling is evaluated by analyzing the frequency change and the interference range of the path crossing area. Through comprehensive analysis of operation stability, scheduling adaptability and flexibility demand intensity, a robot scheduling strategy is dynamically adjusted, the method is suitable for a complex construction environment, scheduling efficiency can be optimized, construction conflicts can be reduced, and the overall performance of multi-robot cooperation can be improved.

The invention discloses an underwater robot dynamic environment sensing and navigation device and method, and relates to the technical field of underwater robos.The device comprises an underwater robot, a sensor system, a data processing system, a control system, a navigation system and a power system, and the sensor system, the data processing system, the control system, the navigation system and the power system are arranged on the underwater robot; the surrounding environment can be comprehensively sensed in real time, and the optimal path is calculated through the path planning module according to the real-time environment data; performing obstacle detection according to the environment image to obtain an obstacle detection result; when an obstacle is detected, the control system sends out an obstacle avoidance decision instruction, after the path planning module receives the obstacle avoidance decision instruction, an adaptive algorithm is used for adjusting the optimal path according to the information of the obstacle to be detected, the adjusted path is obtained, and autonomous navigation for a complex underwater environment and autonomous navigation are achieved. And the autonomous navigation and operation capabilities of the underwater robot are obviously improved.

The invention discloses a dynamic path planning method and system for a multi-axis collaborative welding robot, and belongs to the field of robot welding. The method comprises the steps that basic data of a welded workpiece is collected, and a welding basic database is constructed; constructing a geometric model of the welding seam path according to the data in the welding basic database, and parameterizing the geometric model of the welding seam path; on the basis of the parameter result, an initial track is generated between two adjacent weld joint sections of the welding path, time parameterization is conducted on the initial track, and a time-dependent time parameterization spinor track is obtained; and constructing a spinor mapping model based on inverse kinematics and a spinor theory to convert the time parameterized spinor trajectory into a target spinor. The welding quality is improved.

The invention relates to a robot dynamic grabbing control method based on visual sense and tactile sense depth fusion. The method comprises the following steps that firstly, based on visual information, surface geometric features of a target object are extracted, grabbing adaptability scores are calculated, an optimal grabbing point is selected, and a collision-free approaching track is planned; secondly, in a contact establishment stage, detecting a contact event through a touch sensor, fusing vision and touch data to unify a coordinate system, calculating a vision-touch consistency score, and applying an initial grabbing force; and thirdly, in the stable holding stage, slip detection is conducted through wavelet packet energy entropy and pressure gradient, and the grabbing force and impedance parameters are dynamically adjusted in combination with self-adaptive impedance control. According to the method, intelligent control over the whole process from approaching to contact to stable holding is achieved, and the adaptability, stability and safety of robot grabbing in the dynamic environment are improved.

The invention relates to a rigid-flexible trans-mass self-adaptive robot force position and rigidity collaborative optimization method and system, and relates to the technical field of robot control, and the method comprises the steps: firstly, carrying out the linearization processing of a robot dynamic model through a semi-implicit integral method, and obtaining a robot dynamic model; and in combination with an extended Kalman filter, stiffness parameters in the contact operation process are estimated in real time, a dynamic stiffness sensing model is constructed, and the system response capability is effectively improved. Then, a rigidity parameter is embedded into a Hertz contact model, the dependence of a traditional model on prior information of a contact surface is broken through, a dynamic interaction model between the robot and an operation object is established, and high-precision real-time sensing of normal force and friction force is achieved. And finally, under a nonlinear model predictive control framework, multi-dimensional physical constraints are constructed based on the contact force, the position and the contact rigidity, an optimal control model is obtained, control input is dynamically and adaptively updated through rolling optimization, and the stability and the control precision of the robot in rigid-flexible heterogeneous contact operation are improved.

The invention discloses a robot dynamic environment autonomous navigation method based on deep reinforcement learning. The method comprises the following steps: setting an observation space and an action space of a mobile robot; environmental data are acquired and preprocessed, feature extraction is performed on the data of the laser radar and the depth camera, and a mobile robot observation space feature map is generated; acquiring a target position of the robot, generating a path from the current position to a global target by using an A * algorithm, and generating sub-target points according to the current position and the global path; constructing a PPO deep reinforcement learning network, taking the observation space feature map and the sub-target points as the input of the network, and outputting to obtain the state value, the forward speed and the steering speed of the mobile robot to realize autonomous navigation; designing a reward function, establishing a simulation environment, and training a deep reinforcement learning network based on the reward function for autonomous navigation in a real-time complex dynamic environment. The mobile robot is actively prevented from colliding with pedestrians and obstacles while keeping advancing.

The invention relates to the technical field of service robot navigation and motion control, in particular to a service robot dynamic obstacle pre-judgment obstacle avoidance system, which comprises an environment sensing unit for outputting sensing data; the target state estimation unit outputs a target state sequence; the future evolution prediction unit is used for outputting a target future evolution result; the risk quantification unit outputs the current collision risk degree; operating the strategy management unit, and outputting strategy parameters; the evasion decision and path generation unit is used for outputting candidate evasion tracks and speed curves; the execution and control interface unit is used for outputting a chassis control instruction and implementing trajectory tracking; and the device communication and calculation platform is used for outputting a time sequence synchronization and data channel management result according to operation requirements and providing fault monitoring. According to the method, target state estimation, future evolution prediction, collision risk quantification and strategy linkage are completed within real-time constraints, consistent input and constraints are provided for planning and execution on a unified data channel, and safety and traceability are improved.

The invention discloses a robot dynamic grabbing method based on a sliding window self-attention mechanism, and the method comprises the steps: firstly installing a camera, and calculating a transformation matrix between the camera and a mechanical arm coordinate system based on a hand-eye calibration technology; processing the static data set by adopting affine transformation to generate a grabbed dynamic data set with speed information; then, a dynamic grabbing network model based on a sliding window multi-head self-attention mechanism is constructed, and a loss function optimization model considering the object movement speed and the grabbing posture at the same time is designed; and finally, the trained model is deployed in a robot grabbing environment, a visual servo grabbing control scheme based on the position is designed, and follow-up movement and grabbing action of an end effector of a mechanical arm on a to-be-grabbed object are achieved. According to the dynamic grabbing method of the robot, the grabbing posture and the movement speed of the object can be quickly and accurately predicted based on a group of continuous single-channel depth images collected by the camera, and the grabbing problem of a universal object in a complex dynamic environment is solved.

The invention relates to the technical field of robot scheduling, and discloses an environmental sanitation robot scheduling method, device and equipment based on environmental sanitation task distribution, and the method comprises the steps: collecting the garbage accumulation information and people flow density data of a plurality of urban environmental sanitation regions, and carrying out the task distribution prediction through a space-time convolution network model, an environmental sanitation demand prediction result is obtained; according to the environmental sanitation demand prediction result, creating an urban region division scheme of a plurality of urban environmental sanitation regions and robot initial grouping configuration information; according to the urban area division scheme and the initial grouping configuration information of the robots, dynamic scheduling grouping of the sanitation robots is calculated in combination with the real-time garbage accumulation event data; the path planning and cleaning operation instruction of each environmental sanitation robot is output in groups based on dynamic scheduling of the environmental sanitation robots, intelligent planning of the paths of the environmental sanitation robots is realized, and the collaborative operation efficiency and stability of a multi-environmental sanitation robot system are ensured.

The invention relates to the technical field of intelligent mobile robot path planning, in particular to a dynamic sensing path planning method for a mobile robot in an unknown environment. According to the method, an environment complexity model is established based on the obstacle density and the dynamic activity, and map partition and obstacle avoidance judgment are adaptively adjusted according to the environment complexity model; carrying out incremental updating on the near, middle and far sub-regions based on the point cloud similarity; extracting contours of the obstacle point clouds and carrying out topological optimization; dynamic candidate points are identified through double marking of rays and scanning points, and collision time is predicted and estimated in combination with spatial clustering and trajectory; and according to the collision time, hierarchical obstacle avoidance and local or global re-planning are implemented, and a repaired path is smoothed. According to the method, the path planning efficiency and the real-time obstacle avoidance capability of the mobile robot in a dynamic complex environment are remarkably improved, the consumption of computing resources is reduced, and the method is suitable for application scenes needing high dynamic response, such as warehouse logistics and unmanned driving.

An automated system for a space includes: k navigating robots operating within the space to perform a set of orders, where k is an integer greater than or equal to two, where the space includes one or more waiting areas where any navigating robot may wait without blocking movement of another navigating robot; and a control module configured to receive the set of orders and communicate, to the k navigating robots, paths p for the k navigating robots to fulfil the set of orders, with each order in the set of orders including one or more tasks including a sequence of actions to be performed at a location in the space, having an order availability time after which any task of an order may start, and being associated with a set of precedence constraints arising from at least one of a relationship between orders and a resource of an order.

The invention provides a robot dynamic grabbing method and system based on multi-modal data fusion, and the method comprises the steps: carrying out the multi-modal fusion of the collected visual data and tactile data of a to-be-grabbed dynamic object through a robot, and obtaining the multi-modal fusion data; according to the multi-modal fusion data, target recognition is conducted on the dynamic object to be grabbed through a target detection algorithm based on regional proposal, and position and posture information is obtained; based on the position and attitude information, state prediction is conducted on the dynamic object to be grabbed, and state prediction information is obtained; according to the state prediction information, utilizing a reinforcement learning algorithm to generate a grabbing path of the robot; according to the method, the collected visual data and tactile data are subjected to multi-modal fusion, so that the robot can obtain comprehensive environment information, and the adaptability of the robot to environment change is improved; the state of the to-be-grabbed object is predicted, so that the robot can pre-judge the motion trail of the object, and the grabbing success rate of the robot can be improved.

The invention discloses a dynamic digital twinning system based on 3D Gaussian splashing. The dynamic digital twinning system comprises a flexible electric power inspection robot, a 3DGS dynamic enhancement module and a fusion digital twinning background centralized control system. The flexible electric power inspection robot is used for collecting 2D images and environment point cloud data of substation equipment; the 3DGS dynamic enhancement module is used for carrying out 3DGS dynamic enhancement and generating a 3D Gaussian model; the standard digital twinborn centralized control platform is used for storing and calling data and digital twinborn bodies of the traditional static substation digital model; the model fusion port carries out space alignment and attribute interpolation fusion on the 3D Gaussian model and the static digital twin; and the visual terminal is used for displaying the fused enhanced digital twinborn body and the equipment state evaluation result. According to the invention, the dynamic enhancement of the digital twinborn model of the substation equipment can be realized, and the operation state and change condition of the equipment can be reflected more truly and intuitively.

The invention discloses a robot dynamics physical information prediction neural network construction method based on motor and external force coupling, and relates to the technical field of robots. Comprising the following steps: embedding a motor kinetic equation into a DeLaN network, and establishing an explicit dynamic mapping relation between a system motion state and a motor current through mapping between the motor current and a joint torque; introducing an external force item by fitting a Jacobian matrix, and constructing approximate mapping of the Jacobian matrix by adopting a data driving method; and an initial model is constructed, and after motor dynamics and external force modeling are incorporated into the initial model, an inverse dynamics model of the mechanical arm is obtained. Compared with an existing most advanced method, the method has higher modeling precision and generalization ability and better control performance. The innovative scheme provides solid technical support for realizing real-time accurate control in the field of robots.

Belongs to the technical field of motion control of foot-type robots, and particularly relates to an anti-interference whole-body control method and system for a quadruped robot based on stability margin perception. The expected joint position, speed and foot end contact force are obtained through nonlinear model predictive control NMPC; a whole body controller WBC based on hierarchical quadratic programming calculates a joint driving torque according to the task priority; according to the joint position, the speed and the torque fed back by the driver and a robot dynamic model, estimating the foot end contact force; calculating resultant force which causes instability of the robot relative to each support polygon side line of the robot; projecting the resultant force, and calculating an included angle between the projection force and the vertical direction; calculating a compensation acceleration through a PI controller; and the compensation acceleration is used as a part of a fuselage linear acceleration task in the WBC, so that the anti-interference capability of the robot is improved. The method is used for solving the problems that in the prior art, the stable state of the quadruped robot cannot be effectively sensed, and an anti-interference control strategy cannot be rapidly generated according to the stable state.

The invention relates to the technical field of robots, and discloses a robot dynamic inspection control system based on anti-shake performance. The visual detection module is used for collecting image data of an inspection target; the motion compensation and anti-jitter module is used for monitoring the motion state of the robot in real time and compensating jitter generated by the robot; the environment sensing module is used for detecting and acquiring change data of external environment factors in real time; the data fusion and optimization control module is used for fusing data from the visual detection module, the motion compensation and anti-jitter module and the environment perception module; and the intelligent control module is used for fusing and optimizing the control instruction generated by the control module according to the data. By introducing the motion compensation and anti-jitter technology, the motion state of the robot is monitored and optimized in real time, interference caused by vibration is eliminated, the precision and stability of images and sensor data in the dynamic inspection process of the robot can be greatly improved, and the influence of external interference on a measurement result is reduced.

The invention discloses a quadruped robot reinforcement learning motion planning method and system based on depth vision, and relates to the technical field of quadruped robot motion planning. The method comprises the steps that a simulation environment is built, modeling is carried out on stair terrains with different gradients and robot kinetic parameters, and a stair terrain model comprises stair terrains with different difficulty differences; the robot model is loaded to move in the stair terrain model, the movement condition of the robot is tracked, the speed tracking effect is obtained, and the speed tracking effect is obtained by conducting strategy evaluation on the joint torque value output by the teacher strategy network and the corresponding movement linear speed in the movement process of the robot; the speed tracking effect is evaluated, and environment parameters in the stair terrain model are adaptively adjusted according to the evaluation result. Through the reinforcement learning framework based on the depth visual information, the irregular stair terrain can be effectively learned, and the quadruped robot can stably work in a complex environment in a self-adaptive mode.

The invention belongs to a robot control method, and provides a human-guided robot-environment interaction self-adaptive control method and a related device for solving the technical problems that an existing human-guided robot-environment interaction control method is insufficient in motion stability, low in human operator safety and low in control precision. And estimating an optimal impedance parameter through a state space model, and learning a human-guided reference trajectory through a neural network in combination with the optimal impedance parameter. Then, in combination with a robot Jacobian matrix and a human-guided reference trajectory, a reference joint speed and an estimated acceleration of a joint space are obtained through closed-loop inverse kinematics, then network parameters of a robot dynamic model are obtained through approximation of an uncertain dynamic model, and finally, the robot dynamic model is obtained through combination with the reference joint speed and the network parameters of the joint space. And the mechanical arm control torque is obtained through the offset width fuzzy neural network. Accurate and compliant control under uncertain motion and dynamics conditions is realized.

The invention belongs to the technical field of agent cooperative work control, and particularly relates to a multi-robot distributed cooperative formation control method under specified time. The method comprises the following steps: constructing a multi-robot dynamic model; constructing a communication topological structure of the multi-robot formation and determining an expected formation form; virtual springs are introduced between the robots to construct a potential energy function, and formation errors of a robot system are defined; designing a time-varying gain-based gradient descent distributed controller in a task level and a controller in a specified time in a joint level; estimating and compensating unknown parameters of the robot kinetic model in real time to obtain an online estimation parameter adaptive law based on time-varying gain; a formation master controller is obtained according to all the controllers and the online estimation parameter adaptive law, and multi-robot distributed cooperative formation control is achieved through the formation master controller; according to the method, the problem of rapid reconstruction of the formation form in complex dynamic environments such as dynamic obstacle avoidance of an unmanned aerial vehicle cluster and cooperative work of multiple mechanical arms is effectively solved.

The invention relates to the technical field of intelligent control, in particular to a robot dynamic scheduling system and method based on cross-regional distribution collaboration.The system comprises a task scheduling center, a regional distribution management module, a dynamic path planning engine and a resource collaboration unit, real-time fusion of an order timeliness coefficient, a cargo type weight and a distribution area load balance degree is supported; the regional distribution management module is configured with distributed edge computing nodes; the dynamic path planning engine integrates a space-time constraint analyzer, and dynamically corrects a distribution path in combination with a real-time traffic situation map and a weather influence factor; the resource collaboration unit is deployed at a hub node of a distribution network and is configured with a dynamic task redistribution strategy. Therefore, the problems of multi-dimensional decision splitting, obvious cross-domain cooperative delay, dynamic constraint response lagging, insufficient resource elastic adaptation and the like in the prior art are solved.

The invention provides a teaching-free welding robot dynamic energy-saving path planning method and system, and relates to the technical field of industrial robos.The method comprises the steps that 1, a workpiece is scanned through a laser vision device, weld joint coordinates, obstacle information and workpiece thermal deformation data are obtained, weld joint feature points are extracted, and the weld joint feature points are obtained; establishing a mapping relation between a workpiece coordinate system and a robot base coordinate system; step 2, based on the mapping relationship, in combination with a thermal deformation prediction mechanism and sensor feedback data, implanting a dynamic response node in the initial path, calculating path point offset by using the feedback data, and generating an anti-offset path; and step 3, aiming at the anti-offset path, constructing a multi-dimensional collaborative evaluation mechanism, and carrying out synchronous adjustment and comprehensive balance on the path length, the energy consumption of the whole welding process and the welding quality parameters so as to obtain a multi-dimensional collaborative evaluation result. Weld joint deformation, clamp offset and obstacle displacement are sensed in real time, the path is dynamically planned, and the flexibility of path planning is improved.

The invention belongs to the technical field of robot kinetic parameter identification, and provides a robot kinetic parameter identification method, system, equipment and medium in order to solve the problems of poor accuracy, poor robustness and the like of an existing identification method. A robot excitation track is generated by adopting finite Fourier series, the robot excitation track is optimized under the condition that physical limitation of a robot body and real physical space collision constraint are met, and then the robot excitation track is determined as input data for identification; establishing a logarithmic Euclidean metric convex approximation regular term of the physical consistency matrix; sigmoid is used as a weight to weight the least square, and the inertial parameters and the nonlinear friction model are solved alternately by using double internal loops until the residual is converged. According to the method, the identification precision equivalent to that of long-time excitation can be rapidly obtained, pre-identification does not need to be carried out on the nonlinear friction model, and the calculation efficiency is greatly improved.