1349 results about "Trajectory optimization" patented technology

The invention relates to the technical field of industrial automation control, and discloses a multi-machine collaborative industrial robot intelligent scheduling system and an application method.The system comprises a data acquisition module for acquiring real-time motion data through a multi-modal sensor; the feature fusion module is used for generating environment dynamic feature data based on graph neural network fusion data; the strategy generation module is used for generating collaborative strategy parameters by utilizing a collaborative reinforcement learning model; the trajectory optimization module is used for constructing a multi-target trajectory optimization model to plan an optimal synchronous trajectory; the motion control module is used for establishing a layered motion control model to realize cooperative motion control; a dynamic resource allocation module can also be included to optimize resource allocation. The application method sequentially executes corresponding steps according to the modules of the system. According to the method, the environment can be accurately sensed, the cooperation strategy can be optimized, the efficient track can be planned, accurate motion control and reasonable resource allocation can be realized, and the cooperative motion control capability and the production efficiency of the industrial robot in the complex environment can be effectively improved.

The invention discloses a trajectory optimization method based on a multi-source heterogeneous positioning data fusion algorithm, and relates to the technical field of intelligent navigation and high-precision positioning, multi-modal data are acquired through a multi-modal sensor array, positioning redundancy of scenes such as tunnels and indoor scenes is enhanced, a weight distribution strategy is dynamically adjusted through an Actor-Critic network architecture, and the positioning accuracy is improved. The state space input comprises an environment semantic tag, a historical error sequence and a real-time noise variance, the output action space is continuous weight distribution of each data source, a multi-target reward function optimization strategy is combined, scene adaptability is realized, a local SLAM map, inertial navigation error parameters and a weight distribution strategy are shared in real time based on a V2X protocol, and the real-time performance of the system is improved. According to the method, a single device accumulative error is compensated by using adjacent vehicle data, a terminal locally trains an error compensation model, parameters are uploaded to a cloud end through differential privacy encryption, the cloud end adopts a FedAvg algorithm to aggregate a global model and issue the global model, the error difference between devices is inhibited, and dynamic road network updating and scene differentiation model distribution are supported at the same time.

The invention provides an igniter robot production line control method and system based on deep reinforcement learning, and relates to the technical field of robot production line control, and the method comprises the steps: collecting the real-time state data of a production line station, extracting features through a multi-scale residual neural network and a spatial pyramid pooling module, and carrying out the feature extraction; channel attention and space attention modules are combined to perform feature enhancement, a cross-modal encoder is used to perform time sequence modeling, task decomposition is performed based on hierarchical reinforcement learning, soft constraint trajectory optimization and adaptive impedance control are used to generate a station control instruction, and a multi-target Pareto evaluation and priority dual experience pool is used to perform sample screening. Network parameters are optimized and updated through a near-end strategy, and a production line optimization decision scheme is output, so that the production line control precision can be improved, the system robustness can be enhanced, and the optimization of the production efficiency is realized.

The invention discloses a point cloud data rapid processing system based on 3D vision, and relates to the technical field of point cloud data processing. The binocular acquisition module generates an anti-interference high-precision point cloud; the preprocessing module is used for reducing noise and dimensionality and retaining key geometric features; the hierarchical feature extraction module fuses curvature weight and a non-maximum suppression strategy, and improves the robustness of an ORB algorithm in rotation, scale and noise scenes; the industrial scene segmentation module accurately separates stacked objects through normal vector clustering and a dynamic region growing algorithm, and secondarily verifies boundaries in combination with a lightweight semantic model; the two-stage registration module dynamically adjusts a threshold value based on error feedback to realize pose optimization from coarse registration to fine registration; the coordinate mapping module establishes a space mapping model through multi-attitude calibration and robot multi-axis linkage trajectory optimization. The problem that in the prior art, a 3D machine vision algorithm is high in time delay is solved, the real-time performance and rapidity of 3D model recognition and feature extraction are improved, and the efficiency requirement of industrial production is met.

The invention discloses a multi-modal data fusion-based high dam underwater defect measurement method, which comprises the following steps of: obtaining water flow velocity data, water pressure data and water quality turbidity data, performing preprocessing by adopting a self-adaptive median filtering algorithm, and constructing a hydrodynamic field feature model; then generating an initial trajectory based on the hydrodynamic field feature model and detection target area data, and performing trajectory optimization and stability evaluation in combination with dynamic constraint conditions; adjusting sonar parameters according to the turbidity influence and the trajectory parameters to perform data acquisition; carrying out space-time alignment through extended Kalman filtering, and carrying out feature fusion and optimization by adopting an attention mechanism; and finally, performing multi-scale consistency verification and accurate positioning mapping to obtain a final defect detection result. According to the invention, the problems of defect detection precision and reliability in a complex underwater environment are solved.

The invention relates to the technical field of unmanned aerial vehicle control, and discloses an unmanned aerial vehicle inspection control system and method based on data analysis, and the method comprises the steps: receiving environment feature data of a target region, and constructing a static environment model; obtaining kinetic parameters of the unmanned aerial vehicle, and calculating a feasible flight envelope in combination with the static environment model; after taking off, the unmanned aerial vehicle collects environment data in real time, and a multi-level environment topology model is constructed; acquiring state data in real time, and judging an obstacle avoidance level; if yes, local path planning is carried out, and an optimal flight path is generated; acquiring trajectory deviation and energy consumption data, dynamically calibrating the flight envelope, and optimizing the flight envelope; according to the method, environment perception, trajectory optimization and online learning are combined, the method is adaptive to a complex environment, and the intelligent level of unmanned aerial vehicle inspection is improved.

The embodiment of the invention relates to robot path planning, in particular to a self-adaptive robot trajectory planning method and system based on deep reinforcement learning, and the method can completely present the motion condition of a robot by obtaining a real-time trajectory data set covering an environment interaction state and a motion attitude sequence; key features can be accurately extracted through dynamic track feature extraction processing; a self-adaptive trajectory optimization strategy is generated based on a pre-trained deep reinforcement learning strategy network, so that the strategy can be flexibly adjusted according to dynamic characteristics; incremental trajectory correction processing is carried out on the robot motion posture sequence, the trajectory can be optimized step by step, and an optimized trajectory sequence conforming to dynamic environment adaptability is generated; and the optimized trajectory sequence is synchronized to the motion control system, so that the robot can quickly and accurately execute the optimized trajectory, and the motion planning capability and execution efficiency of the robot in a complex dynamic environment are remarkably enhanced.

The invention discloses an expressway emergency management and control method and system, and the method comprises the steps: building a multi-dimensional heterogeneous data fusion model through a time-space diagram neural network according to the traffic flow, meteorological data and accident reports collected by roadside sensing equipment, and carrying out the dynamic prediction of the risk level of a road segment, and obtaining a real-time risk level map of a whole road network; based on the real-time risk level map, outputting a dynamic shunting scheme containing variable lane marks and a speed limiting strategy; according to the dynamic shunting scheme, generating a vehicle trajectory optimization strategy; and outputting an anti-interference optimization control strategy based on a vehicle trajectory optimization strategy in combination with sudden obstacle data polled by the unmanned aerial vehicle in real time. According to the embodiment of the invention, rapid and accurate path adjustment and cooperative control can be carried out, and the emergency management level of the expressway is improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of robot strategy training, financial science and technology, medical treatment and health and the like, and discloses a strategy optimization method and device based on an interaction track, equipment and a medium. The reinforcement learning agent interacts with the environment to generate an action and state sequence and record an interaction track, an optimal action strategy is generated based on a track optimization strategy, an original empirical data set is further generated, and a pre-training model is optimized through supervised learning to obtain a target strategy model. According to the method, a task-related high-quality trajectory is generated through reinforcement learning agent and environment interaction, original empirical data is extracted on this basis, and a pre-training strategy model is optimized in combination with a supervised learning mechanism, so that the sample utilization efficiency is effectively improved, and the generalization ability and execution robustness of the model under a multi-task condition are enhanced.

The invention relates to the technical field of path planning, and discloses a robot path planning method, which comprises the following steps that a hybrid cost map is constructed, and the hybrid cost map comprises a static obstacle field, a dynamic obstacle probability prediction field and a terrain energy consumption field; constructing an anisotropic heuristic function, and searching on the mixed cost map to obtain an initial path; parameterizing the initial path into a group of piecewise polynomial curves, constructing a joint optimization target which takes the total curvature, the piecewise polynomial curves and the space-time overlapping integral of a dynamic obstacle probability prediction field, and performing trajectory optimization under the condition of meeting the kinematics constraint of the robot to obtain a smooth space-time trajectory; when the task is executed, the information divergence is continuously calculated, and when the information divergence exceeds a preset threshold value, the current state of the robot serves as a new starting point, and the complete path planning process is executed again. According to the method, the future collision risk is prospectively avoided, the energy consumption of the robot is considered, and the comprehensive quality of the path is improved from the source.

The invention discloses a civil heterogeneous unmanned cluster path coordination method and system based on unmanned-unmanned cooperation, and belongs to the field of civil unmanned aerial vehicle path coordination, and the method comprises the steps: obtaining an unmanned aerial vehicle cooperative flight task, carrying out the analysis of the unmanned aerial vehicle cooperative flight task, and building a subtask monomer; idle unmanned aerial vehicle self-inspection is executed, a feature set of idle unmanned aerial vehicles is established, feature matching is performed, and unmanned aerial vehicle-subtask mapping is established; establishing a search cluster and a following cluster; according to the pre-flight trajectory, executing real-time environmental data acquisition by using the search cluster; and pre-flight trajectory adjustment is carried out to complete path coordination of the unmanned aerial vehicle cooperative flight task. The technical problems that in the prior art, the environment adaptability of civil unmanned aerial vehicle cluster path planning is insufficient, and the heterogeneous cluster cooperation efficiency is not high are solved, and the technical effects that environment perception and trajectory optimization are carried out through cooperation of the search cluster and the follow-up cluster, and the environment adaptability and cooperation efficiency of the heterogeneous unmanned aerial vehicle cluster are improved are achieved.

The invention relates to a mechanical arm track optimization method based on deep reinforcement learning. The mechanical arm track optimization method comprises the following steps that firstly, kinematics modeling is conducted on a mechanical arm; 2, multi-objective optimization problem modeling is carried out, trajectory planning of the mechanical arm is regarded as a multi-objective optimization problem, and the main objective of optimization comprises three aspects of trajectory precision, trajectory smoothness and energy consumption; and 3, optimizing a PPO reinforcement learning algorithm by using a near-end strategy, and designing a trajectory planning algorithm.

The invention provides an orchard agricultural robot navigation method, and belongs to the technical field of agricultural robots. The method comprises the following steps: constructing a priori map of an orchard; carrying out odometer initialization and rear end correction on the robot; carrying out ground segmentation and obstacle identification; constructing a cost map: constructing the cost map through static obstacles of a prior map and dynamic obstacles after ground segmentation, and realizing visualization of the obstacles; and path planning: assigning an initial point and an end point on the cost map, firstly realizing global path planning, bypassing an obstacle by using local path planning to realize an obstacle avoidance function when a dynamic obstacle appears in the advancing process, and performing real-time trajectory optimization and smooth control. According to the method, accurate positioning and autonomous navigation of the robot in a complex orchard environment can be realized; meanwhile, through the combination of global path planning and local path planning, the robot can flexibly process static and dynamic obstacles, and the robustness of path planning and the effectiveness of a navigation task are ensured.

The invention relates to the technical field of intelligent laser processing path planning, and discloses a laser flight processing path planning method and device for a complex curved surface, and the method comprises the steps: obtaining the geometric continuity features of the curved surface, carrying out the region division and boundary feature extraction, obtaining the geometric features of the boundary points of each region, and obtaining the geometric features of the boundary points of each region; performing track generation based on boundary geometric features to obtain an initial processing track path, then performing heat accumulation analysis and track screening, identifying a track section needing to be adjusted, executing multi-target path scheduling optimization, and obtaining adjusted track execution feature data; processing sequence optimization is carried out based on the feature data, and an optimized processing sequence is obtained; performing thermal input analysis and parameter correction according to the processing sequence to obtain an updated processing parameter set; and finally, performing global coordination based on a simulated annealing algorithm, generating a final processing track set, completing track optimization and instruction set generation, and outputting a complete processing scheme. According to the method, the problem of insufficient track fitting degree can be solved.

The invention relates to a tower crane operation control system based on complex scene three-dimensional real-time modeling. According to the system, a lifting hook is coarsely positioned through a lifting hook positioning and state sensing unit, a real-time position is positioned by combining a laser radar point cloud clustering algorithm with historical pose data, and visual tracking is synchronously performed by means of a tower top camera AI; converting the real-time point cloud data into a 3D voxel grid map, generating a global path by using a 3DA algorithm, and outputting a hoisting track after smooth processing and track optimization; establishing a sling-lifting hook double-pendulum dynamic model, predicting a state sequence based on a model prediction control algorithm, and adjusting a control signal through a feedforward compensation item and a feedback correction item; and the man-machine interaction and monitoring unit is used for displaying the cantilever angle, the lifting hook height and the three-dimensional map of the tower crane in real time and remotely intervening the operation state of the tower crane. According to the system, multi-source data are fused to construct a high-precision three-dimensional map, lifting hook positioning and full-view tracking are achieved, and lifting safety and trajectory tracking precision are improved through path planning and dynamics control.

The invention provides an end-to-end automatic driving decision control method and system, and belongs to the technical field of automatic driving. The invention relates to an end-to-end automatic driving decision control method based on multi-modal perception and hierarchical trajectory optimization, and the method comprises the steps: constructing an end-to-end decision closed loop through combining the zero sample migration capability of a vision-language-action (VLA) model with a hierarchical optimization architecture: analyzing multi-modal input (vision, language and point cloud) by using a pre-trained VLA model to generate path points; the vehicle pose is dynamically adjusted through upper-layer optimization to expand a feasible solution space, a smooth track meeting dynamics and collision avoidance constraints is solved in real time through lower-layer optimization, and finally a vehicle control instruction is output. According to the method, a multi-modal sensing and hierarchical trajectory optimization mechanism is fused, and the sensing generalization ability, the path planning feasibility and the control execution robustness of the system in a complex traffic environment are effectively improved.

The invention discloses an unmanned aerial vehicle flight path optimization system and method based on artificial intelligence and Internet of Things, and relates to the technical field of unmanned aerial vehicle control, and the method comprises the steps: collecting communication delay, environment data and an unmanned aerial vehicle state in real time; analyzing historical communication delay data by using a Markov chain, and predicting a basic delay value in a short time in the future; correcting the basic delay value through a machine learning model; outputting a final delay prediction result; calculating a delay risk index, and judging a delay risk level; based on the delay risk level, when the delay risk level is low risk, sending a control instruction in advance according to the final delay prediction result; and when the delay risk level is medium or high risk, calling a pre-trained reinforcement learning strategy library, and generating a self-adaptive control instruction. According to the method, the multi-dimensional risk index is constructed, multiple strategies are provided to adjust the flight path according to the real-time data and the risk index, and the flight stability of the unmanned aerial vehicle is improved.

The invention discloses a super-long water tunnel defect underwater positioning device and method, and the method comprises the steps: obtaining multi-source sensor data of an underwater vehicle, and carrying out the time synchronization and preprocessing of the data; establishing an acoustic environment feature model, identifying strong reflective identification information, and updating environment parameters; the dynamic weight of each data source is calculated, data consistency check is carried out, and multi-source data fusion is executed; performing trajectory optimization on a fusion positioning result, executing identification point constraint processing, and performing backtracking correction on a historical trajectory; and performing multiple verification and uncertainty evaluation on the positioning result. According to the method, through multi-source data fusion and a multi-level optimization strategy, the problems of multipath effect, thermocline influence, inertial navigation accumulative errors and image quality in a complex underwater environment are effectively solved, and high-precision positioning in the ultra-long water delivery tunnel is realized.

The invention, which belongs to the technical field of wireless communication, provides a position-guided channel knowledge graph construction method comprising the following steps: constructing an unmanned aerial vehicle auxiliary communication scene comprising a ground base station, and constructing a channel knowledge graph model; designing a back propagation neural network, training the back propagation neural network by using enough training samples corresponding to a ground base station j so as to learn a recessive nonlinear relationship between the position of the unmanned aerial vehicle and a channel gain, and representing a channel knowledge graph model as a dominant derivable function of the position of the unmanned aerial vehicle and the channel gain; and introducing a transfer learning algorithm, and performing fine tuning on the trained back propagation neural network of the ground base station j by using a limited sample corresponding to the target ground base station j 'to obtain a back propagation neural network of the target ground base station j', thereby obtaining a channel knowledge graph of the target ground base station j '. The method is a classical optimization method suitable for unmanned aerial vehicle trajectory optimization and deployment problems.

The invention relates to the technical field of image processing, and provides an image target tracking method and device based on deep learning, and the method comprises the steps: obtaining an original frame sequence of a target video, carrying out the adaptive frame screening of the original frame sequence, and obtaining a plurality of feature frame sequences, key frames and common frames, performing feature extraction and feature mapping on all the feature frame sequences to obtain target trajectory nodes, performing trajectory optimization calculation on the key frames and the common frames by using all the target trajectory nodes to obtain key frame trajectories and common frame trajectories, and inputting the key frame trajectories and the common frame trajectories into a preset deep learning model to perform trajectory reconstruction optimization so as to obtain a target trajectory; and obtaining a tracking target trajectory. By performing adaptive frame screening on the original frame sequence and generating the feature frame sequence, the key frame and the common frame, the data processing efficiency and the information extraction accuracy are improved, the accurate generation of the target motion track is realized, and the problems of reduced tracking accuracy and delayed system response in a complex scene are solved.

The invention discloses a real-time temperature measurement and optimal scheduling method for a rail-mounted intelligent inspection robot, and the method comprises the steps: obtaining the temperature of equipment along a rail in real time through a temperature sensor on the inspection robot, analyzing the working state of the equipment along the rail, and recognizing a potential overheating risk region; establishing a multi-robot collaborative inspection task scheduling model according to the position information and the emergency degree of the potential overheating risk area; planning an inspection path for the multi-robot collaborative task scheduling data by taking the shortest time as an index, optimizing the inspection path by adopting a fusion improved ant colony algorithm and combining a TEB strategy, and obtaining a global planning path; a trajectory optimization strategy based on Minimum Snap is adopted for the global planning path, a Bezier curve is utilized to optimize the path trajectory, a multi-robot collaborative task scheduling model is combined with an auction algorithm to dynamically allocate tasks, efficient utilization of resources and priority processing of emergency tasks are ensured, the inspection efficiency and safety are improved, and the operation cost is reduced.

The invention relates to the field of navigation, and more particularly discloses a gradient optimization driven unmanned aerial vehicle real-time obstacle avoidance multi-stage trajectory planning method, which comprises the following steps of: in a trajectory initialization stage, firstly, searching a collision-free geometric path considering steering limitation of an unmanned aerial vehicle on an occupied grid map by utilizing an improved algorithm to generate an initial B-spline control point; then, in a trajectory optimization stage, constructing a multi-target trajectory optimization problem, introducing an obstacle avoidance constraint based on an Euclidean distance field map, and optimizing the initial control point set in combination with unmanned aerial vehicle parameters and optimization weights to obtain a trajectory meeting an obstacle avoidance requirement; and finally, for the optimized trajectory, performing dynamic feasibility evaluation based on unmanned aerial vehicle kinematics limitation in a trajectory correction stage, and if the trajectory does not meet the constraint, performing correction based on the minimum curvature constraint on the control point to ensure that the finally generated trajectory is not only obstacle-avoiding but also feasible in dynamics, and finally, determining that the trajectory does not meet the constraint. Therefore, the real-time obstacle avoidance capability of the unmanned aerial vehicle in a complex environment is effectively improved.

The invention relates to the technical field of ground mobile unmanned equipment control, and discloses a ground mobile unmanned equipment autonomous obstacle avoidance control system optimized by artificial intelligence. The system comprises an environment perception layer, a bimodal risk assessment layer, a dynamic decision-making layer, a trajectory optimization layer and a feedback optimization layer. The environment sensing layer adopts a retina fovea centralis imitating mechanism to perform non-uniform sampling on laser radar point cloud data to generate dynamic point cloud partitions; the bimodal risk assessment layer fuses two types of radar data to generate static and dynamic obstacle risk assessment diagrams; the dynamic decision-making layer establishes space-time mapping and generates an obstacle confidence coefficient matrix through a graph neural network; the trajectory optimization layer converts the matrix into a control parameter based on a multi-objective evolutionary algorithm, and issues the control parameter through a time-sensitive network protocol; and the feedback optimization layer monitors environment change, calculates deviation, generates an effectiveness index, and dynamically adjusts a point cloud acquisition strategy until the index is optimal. According to the system, the autonomous obstacle avoidance capability and adaptability of the ground mobile unmanned equipment in a complex environment are enhanced.

The invention discloses a key safety scene generation system for an automatic driving automobile based on a diffusion model. The key safety scene generation system is characterized in that a traffic module performs de-noising processing on a noise track through the diffusion model and generates real diversified traffic scenes; the key safety scene generation module calls a mixed guidance function in each step of denoising process of the diffusion model to guide an opponent agent to collide with a self-agent so as to generate an antagonistic key safety scene; the expert trajectory optimization module performs multiple iterative optimization on the target function through an Adam optimizer to generate a safe and real expert trajectory; the planner enhancement module carries out fine adjustment on a pre-trained planner through the generated key safety scene and the expert track thereof, so that the ability of the planner to cope with extreme scenes is improved; the safety and robustness of the automatic driving planner in an extreme environment are improved by generating a real, diversified and antagonistic key safety scene.

The invention relates to the technical field of intelligent control, in particular to a full-automatic plug-in testing machine control system and method, and the system comprises a multi-dimensional data collection module, an abnormal state recognition module, an action termination judgment module, a parameter correction module and a trajectory optimization execution module. According to the invention, based on multi-sensor array fusion, multi-physical quantity cooperative acquisition in equipment operation is realized, vibration, temperature, path and plugging force data are comprehensively calibrated by using a time axis, and through spectral analysis and three-dimensional deviation comparison, fine abnormity and trend change are dynamically revealed, and operation fluctuation and potential abnormity are identified in real time. According to the method, the reasonable interval of the insertion and extraction force is automatically calculated, the action duration and path trend are adjusted, the operation parameters are continuously corrected, equipment abnormity early warning, accurate intervention and multi-parameter linkage adjustment are achieved in the whole process, the test continuity, the execution sensitivity and the prevention and control capacity are improved, misoperation is effectively restrained, abrasion is reduced, and the high-density intelligent test requirement is met.

The invention belongs to the technical field of air-space-ground integrated networks, and discloses an unmanned aerial vehicle track optimization and task offloading method for an air-space-ground integrated network. According to the method, the task unloading efficiency in the air-space-ground integrated network is optimized, optimization of energy consumption and time delay is focused on, various conditions of the air-space-ground integrated network are comprehensively considered, a decoupling communication mode is considered, and the influence of service interruption is reduced. And constructing an optimization model to minimize the weighted sum of time delay and energy consumption. In order to improve the collaboration of heterogeneous agents in a space-air-ground integrated network, based on a hierarchical reinforcement learning framework, a hierarchical learning-based two-stage Qmix algorithm is proposed, and the collaboration of a low-orbit satellite and an unmanned aerial vehicle is reinforced. According to Pycharm platform simulation and multi-scene verification, the method has remarkable advantages in average response time delay, energy consumption and QoS indexes, the network performance is effectively improved, and user requirements are met.

The invention discloses a ship target identification tracking method and system based on multi-source data fusion, and relates to the field of electric digital data processing, and the method comprises the steps: collecting ship monitoring data of a multi-source sensor, including position coordinates, area and color features; generating an initial ship trajectory according to the monitoring data, calculating the speed, area and color similarity of a detection point, and associating to obtain a trajectory pair; carrying out abnormal point identification and trajectory optimization based on a similarity energy function containing options such as dynamics and continuity; calculating the state difference between the optimized trajectories to perform similarity test, and judging the trajectories exceeding a threshold value as the same target to form a multi-source trajectory group; and calculating a dynamic fusion weight according to the error standard deviation of the trajectory relative to the average trajectory, and performing weighted fusion to obtain a target trajectory. By implementing the method, the fusion precision of the ship target trajectory can be improved.

The invention provides a robot kinetic parameter identification method and system. The robot kinetic parameter identification method comprises the steps of establishing a mapping relation between a minimum parameter set and a standard parameter set when a rigid body kinetic model is linearized; establishing a symmetric positive definite matrix manifold, and calculating a related formula according to the differential geometry of the manifold; establishing a geometric excitation optimization criterion and generating an optimal excitation track with the condition number of the traditional observation matrix; establishing a parameter identification optimization objective function, and constructing a geometric convex approximation upper bound constraint suitable for iteration as a regular term; and iteratively identifying a minimum parameter set and a nonlinear friction parameter. According to the method, geometric excitation trajectory optimization criteria and traditional observation matrix condition number excitation robot system characteristics are combined, convex approximation upper bound constraints suitable for iterative optimization are constructed to serve as regular terms of a parameter identification optimization function, iterative identification is conducted on the convex approximation upper bound constraints and a non-linear friction model, inertial parameters better conforming to a robot structure are obtained, and the method is suitable for large-scale popularization and application. And the identification precision is improved while the physical rationality of parameters is ensured.

The invention belongs to the field of robot motion control, and discloses an online generation and control method and system for whole-body coordinated walking motion of a humanoid robot, a computer readable storage medium and a computer program product. According to the method, in walking motion generation, a whole-body walking motion track including upper body joint motion information is generated in real time by adopting a whole-body kinematics-centroid dynamics model; a model predictive control model is adopted to realize organic combination of motion trail generation and real-time tracking control; and solving the model prediction control model, and taking the optimal state x * and the corresponding control input u * as the input of whole body control. According to the method, in the planning stage, namely the whole-body walking motion trail including upper body joint motion information, the whole-body dynamic characteristics of the arms, the waist and the like are comprehensively considered, and a new method of long-time-domain trajectory optimization and online control is combined, so that real whole-body coordinated walking is fundamentally realized.

The invention relates to the technical field of aluminum plate processing, and discloses an aluminum plate processing cleaning intelligent monitoring system, which comprises a three-dimensional deformation sensing module, a surface residue detection module, a thermal stress distribution analysis module, a vibration spectrum identification module, a processing track optimization module, an environmental parameter fusion module, an intelligent cleaning regulation module and the like. The three-dimensional deformation sensing module realizes aluminum plate surface three-dimensional reconstruction and deformation characteristic spectrum generation through an industrial camera and an algorithm; the surface residue detection module identifies residue distribution and classification by using a laser speckle interferometer and the like; the thermal stress distribution analysis module inverts thermal stress by means of an infrared focal plane array; the vibration spectrum identification module analyzes the equipment vibration signal; the processing track optimization module generates an optimized track based on an ant colony algorithm and the like; the environmental parameter fusion module generates a fusion decision index through data dimension reduction and correlation analysis; and the intelligent cleaning regulation and control module combines feedback and learning to optimize cleaning parameters. The system achieves multi-dimensional monitoring and intelligent cleaning regulation and control, and the machining precision and efficiency are improved.