7555 results about "Motion control" 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 multi-modal perception humanoid robot action adaptive control method and system, and belongs to the technical field of computers, and the method comprises the steps: obtaining first multi-modal data; the first multi-modal data comprises first image data, first point cloud data and first force sense data; performing data fusion on the first image data and the first point cloud data, and constructing a three-dimensional model of the scene in real time; generating an optimal path according to the real-time environment data of the three-dimensional model, converting the optimal path into a joint instruction of the humanoid robot, and generating an action control strategy; according to the action control strategy, arm movement of the robot is adjusted through visual servo control, and first operation data are obtained; the path and action of the robot are adjusted in a closed-loop control mode, and second operation data are obtained; and reinforcement learning and regular evaluation are carried out through a DDPG algorithm, and an action control strategy is continuously optimized. According to the invention, multi-modal sensing robot motion adaptive control with strong adaptive capability, high operation precision and good robustness can be realized.

The present invention relates to a digital twin processing method and system, and a cloud platform. The method comprises: acquiring production system elements, carrying out abstraction definition and parameterization description on the production system elements by means of digital-to-analog knowledge fusion of a mathematical model and a mechanistic model, so as to construct a digital twin ontology model; analyzing and reconstructing model data to obtain a mapping model of which object variables can be directly accessed and operated by a collective motion control method, so that the model is visualized at the cloud; and using an external data source to drive parameter update and operation matching of the model by means of a motion control method, so as to complete cooperative deployment and synchronous evolution of an actual physical device and the model in the production process on a cloud server. According to the present invention, a model is constructed by means of digital-to-analog knowledge fusion of a mathematical model and a mechanistic model, the model is mapped to achieve motion visualization, model parameter update and operation matching on the cloud are achieved, and then cooperative deployment and synchronous evolution of a physical device and the model are completed.

The invention discloses an underwater robot autonomous obstacle avoidance and path planning system based on a multi-modal sensor, particularly relates to the field of underwater robot navigation, solves the problems of multi-modal sensing and path planning in a complex environment, and unifies the feature space of a multi-source heterogeneous sensor based on a physical field correction mode of an acousto-optic fluctuation rule. Fusion deviation caused by physical property difference of sonar and visual data is overcome, and obstacle characterization precision and dynamic environment adaptability are remarkably improved; a dynamic confidence decision-making mechanism is combined with a path curvature-moment constraint model, the environmental perception credibility and the robot motion performance are synchronously fused into a path generation process, the feasibility of motion control is ensured while the geometric collision risk is avoided, and the contradiction between the safety and the performability in path planning is solved; the global path optimization eliminates the hidden danger of local path sudden change through iterative smoothing under double constraints of physical field and dynamics, and forms an optimal navigation scheme considering obstacle avoidance efficiency, energy consumption and motion stability.

The invention discloses a rescue robot path planning method and system under industrial vision assistance, and relates to the field related to industrial vision, and the method comprises the steps: collecting three-dimensional space data of a rescue environment in real time, generating a dynamic environment point cloud data set, and constructing a three-dimensional semantic map of a rescue area; thermal imaging data updated in real time are called, path analysis is carried out in combination with the three-dimensional semantic map, and a path planning strategy set is obtained; and predicting the motion track of the dynamic obstacle based on the local dynamic obstacle avoidance strategy, optimizing the global path planning strategy according to obstacle prediction track data, and generating a motion control instruction of the rescue robot. The technical problem of poor real-time performance and adaptability of path planning caused by insufficient perception of environment dynamic information in path planning of an existing rescue robot is solved, the strong perception capability depending on industrial vision is achieved, the environment dynamic information is accurately captured in real time, and the real-time performance of path planning is improved. And the real-time response speed of path planning and the adaptability to a dynamic environment are 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 provides a trajectory control video generation method and device based on depth information and time-frequency optimization, and relates to the technical field of image processing, and the method comprises the steps: optimizing a 3D trajectory through multi-entity segmentation, depth estimation and time-frequency decomposition in combination with a user instruction, and generating a control signal through a multi-scale fusion network; finally, the signals and original images are input into an improved Stable Video Diffusion model to generate a video potential representation sequence, the problems that an existing video generation method is insufficient in dynamic entity motion control precision and poor in cross-frame consistency are solved, and through 3D trajectory modeling guided by depth information and a time-frequency joint optimization mechanism, the video potential representation sequence is generated. And the motion smoothness, the space authenticity and the time-frequency stability of the generated video are obviously improved.

The invention provides a motion control method and system for an intelligent robot, and the method comprises the steps: collecting environment and state data through an intelligent sensor group of a humanoid robot, inputting the environment and state data into a pre-training first neural network model, and obtaining motion prediction data and an environment analysis result; constructing a motion planning model, and performing energy consumption-stability multi-objective optimization on the joint motion track by adopting a preset first algorithm; the central controller generates a joint position, speed and torque reference trajectory based on an optimization result; and the local second controller of each joint locally adjusts the reference trajectory within the prediction time domain according to the real-time feedback. According to the invention, multiple sensors are combined with the mixed attention neural network to realize environment and self state intelligent perception, and the problem of multi-sensor data fusion time sequence dependence is solved; through energy consumption-stability multi-objective optimization, the complex environment movement efficiency is remarkably improved; the central controller and the local controller work cooperatively, and in combination with an edge computing architecture, the communication delay is reduced, and the system response speed is improved.

The invention discloses a robot anthropomorphic motion control method and system based on skeleton mapping. The method and system are used for enhancing anthropomorphic motion performance and environment adaptability of a humanoid robot. The method comprises the following steps: calculating a scale proportion and a posture transformation matrix between a human body and the humanoid robot based on initial posture data; according to the motion data, recursively calculating original pose information of each human body joint in a world coordinate system through a forward kinematics algorithm; mapping the original pose information into expected pose information of the humanoid robot on the key coordinate system by applying a scale proportion and a pose transformation matrix; a target joint angle of the humanoid robot is solved according to the expected pose information, and contact state information of legs of the humanoid robot and the ground is calculated according to the target joint angle; and the target joint angle and the contact state information serve as input, a motion control strategy of the humanoid robot is trained through a deep reinforcement learning algorithm, and humanoid motion control over the humanoid robot is achieved.

The invention relates to the technical field of robot autonomous navigation and control, and discloses a robot system based on a sensing system, which comprises a multi-mode sensing module for outputting data to a dynamic environment modeling module; the dynamic environment modeling module is used for transmitting the model to the path planning module; the path planning module outputs the path sequence to the neural motion control module; the neural motion control module outputs the control signal to the dynamic execution module; the dynamic execution module is used for receiving the pulse control signal and driving each joint of the robot to execute the path in combination with inverse kinematics solution and a feedback control mechanism; and the collaborative optimization module is in two-way communication with the modeling module, the path planning module and the neural motion control module. According to the method, the contradiction between real-time performance and integrity of environment modeling in a complex scene is solved by fusing cross-modal information complementary characteristics of binocular vision, millimeter-wave radar and inertial data and combining mathematical representation of a hypergraph topology model on a dynamic obstacle interaction relationship.

The invention relates to a power grid monitoring bionic robot with multi-mode sensing and voice interaction functions. The acquisition unit acquires sound, images, temperature and power grid equipment surface micro-vibration information, completes data preprocessing and time and space calibration, and generates a sensing data packet. And the identification unit performs cross judgment based on the abrupt change characteristics and correlation of different types of data, identifies electrical, structural or environmental anomalies, and generates abnormal region description information. The construction unit analyzes whether a multi-factor coupling risk exists in combination with a power grid environment type and a historical rule, and generates task description information including processing suggestions and development path prediction. The analysis unit supports speech enhancement and tone extraction in a high-interference environment, and extracts a user interaction intention in combination with task description information. And the control unit jointly judges a behavior correction strategy according to the task description information and the interaction instruction information, generates a corresponding motion control instruction and voice feedback, and realizes autonomous monitoring and interaction response in a complex power grid environment.

The invention discloses a mechanical arm motion control method based on multi-agent cooperation, and the method comprises the steps: firstly, receiving an RGB image through a sub-task generation agent, and generating a structured sub-task sequence according to a natural language task instruction of the RGB image; secondly, performing joint modeling on a task text and a scene image through a 3D sensing intelligent body, positioning specific coordinates of a target object in a three-dimensional space, reasoning dynamic characteristics of a current environment based on historical state information of a robot by combining an environment sensor, and generating an environment sensing vector; and finally, the action generation agent performs fusion modeling according to the subtask text, the subtask target coordinates, the current state of the robot and the environment perception vector, generates a continuous action vector, drives a mechanical arm to complete each subtask action, and constructs closed-loop feedback by a controller and a discriminator to realize task execution state judgment and automatic circulation. The precise action control instruction can be effectively generated, and the task execution fineness of the mechanical arm is remarkably improved.

The invention belongs to the technical field of ship path tracking, and discloses a ship path tracking method based on adaptive line-of-sight guidance and fuzzy adaptive PID, and the method comprises the steps: presetting a path point, and obtaining a real-time coordinate; obtaining an actual expected heading angle # imgabs0 # and a ship observation heading angle # imgabs1 #, subtracting the actual expected heading angle # imgabs0 # and the ship observation heading angle # imgabs1 # to obtain a heading deviation # imgabs2 #, and calculating a heading deviation rate # imgabs3 #; the course deviation and the course deviation rate are input into a fuzzy self-adaptive PID motion control module, and gain parameters are output through a fuzzy control algorithm; the gain parameter and # imgabs4 # are input into a PID controller to calculate a ship rudder angle # imgabs5 #; inputting # imgabs6 # into the motion mathematical model to update the motion state, and forming a closed-loop updated motion state to enable the ship to approach a preset path point; and judging a ship path after the state information is updated. The expected course of the ship is calculated in real time and the sideslip angle of the ship and the control parameters of the ship are adjusted in real time through self-adaptive sight guidance, and the control parameters are automatically adjusted in real time by means of the fuzzy self-adaptive PID motion control module to achieve accurate motion control, so that the ship travels according to a preset planned path.

The invention relates to the technical field of motion control, in particular to a CNC machine tool motion control system which comprises a cutting force feedback monitoring module, a multi-axis load coordination module, a spindle vibration suppression module and a dynamic path optimization module. According to the method, the cutting force fluctuation of each axle can be accurately mastered by monitoring the axle cutting force in real time and analyzing the load change, the load and the feeding speed of the axle are adjusted according to real-time feedback, machining errors caused by uneven loads are reduced, and the vibration amplitude and frequency can be calculated and the rotating speed of the main shaft can be adjusted by monitoring the vibration data of the main shaft in real time. The resonance phenomenon is effectively restrained, the machining stability is improved, meanwhile, dynamic path optimization is conducted, the dynamic response problem caused by improper path setting is avoided by evaluating the dynamic response of a machine tool and adjusting the machining path, the cutting direction and the feeding speed in real time, the machining precision and stability are remarkably improved, and the machining efficiency is improved. And the complex machining environment adaptability of the machine tool is improved.

The invention provides a hybrid representation learning-based omni-directional motion control method for a quadruped robot in a three-dimensional environment, aiming at solving the problem that an existing motion control method for the quadruped robot is limited by a narrow forward view angle and external perception noise of the robot, so that the omni-directional motion capability in the three-dimensional environment is difficult to obtain. Comprising the steps of designing various input and output contents of a first-stage end-to-end neural network learning framework, designing a training process including mixed representation learning based on a reinforcement learning framework, constructing a specific neural network model of each module, and constructing a quadruped robot simulation motion control training environment. The method has the advantages that network input and output content beneficial to efficiently learning the relation between actual observation of the robot and the environment truth value is provided, and a mixed representation learning method including supervised learning and comparative learning is introduced, so that the robot can effectively deduce environment information beyond the visual field range of the robot in the training process.

The invention discloses a humanoid robot grabbing method based on three-dimensional vision, and the method comprises the following specific steps: S1, environment modeling and perception: carrying out the three-dimensional scanning of a working environment, and constructing a three-dimensional map of the environment; s2, object identification and 6D attitude estimation: identifying a target object, and estimating the position and attitude of the object in a camera coordinate system; s3, coordinate conversion and target positioning, wherein the posture of the object is converted into a robot base coordinate system from a camera coordinate system through TF conversion; and S4, path planning and motion control are conducted, specifically, path planning is conducted through a cumotion algorithm, a grabbing path is generated, and the two arms are driven to move according to the planned track to achieve grabbing. By means of environment modeling and sensing, the robot can update environment information in real time, conduct three-dimensional scanning on the working environment and generate the three-dimensional grid model of the environment in real time, the model is used for obstacle detection and avoidance planning, and the dynamic environment adaptability of the robot to object grabbing is improved.

The invention provides an array detection and positioning method combined with multi-welding-head battery welding, which comprises the following steps: monitoring the distribution of a welding head temperature field in real time by adopting an infrared thermal imaging technology, judging whether the welding head temperature field deviates or not by combining a preset temperature threshold value, and judging that the welding head temperature field deviates if detecting that the temperature of a certain welding head exceeds a threshold value range; according to the offset of the welding position, motion control parameters of the digital welding system are adjusted in a self-adaptive mode, and the offset error of a welding head is compensated in real time through a closed-loop feedback control algorithm; the welding spot quality online monitoring technology is adopted, the welding spot fusion depth and the welding spot diameter are extracted, whether the welding spot quality is qualified or not is judged in real time in combination with a pre-established welding spot quality evaluation model, and if the quality of a plurality of continuous welding spots is not qualified, it is judged that the welding spot quality is affected by welding head temperature field deviation.

The invention discloses an intelligent head-mounted device with a camera capable of rotating along with the camera, and the device comprises an eye movement tracking camera which is used for collecting and measuring the image data of the iris of the two eyes of a wearer; the IMU sensor is used for detecting head movement data of a wearer; the image acquisition and processing module is used for generating eyeball movement data according to the image data of the iris of the two eyes, and converting the eyeball movement data and the head movement data into a rotation instruction; the binocular camera motion control device executes the rotation instruction, drives the binocular camera to move, moves along with the iris and the eyeball, and collects image data in real time to realize line-of-sight tracking; and the image acquisition and processing module is also used for carrying out image and / or video fusion processing according to the received image acquired by the binocular camera in combination with the head movement data and the eyeball movement data. According to the invention, the problem of limitation in the aspects of acquiring depth information and expanding the visual field can be solved, and a more flexible camera tracking and adjusting mechanism is designed, so that wider and clearer image acquisition is realized.

The invention provides a servo motor real-time adaptive control method and system based on artificial intelligence, and the method comprises the steps: injecting a tiny excitation signal into a servo motor, collecting temperature and electromagnetic response data under different working conditions, and constructing a multi-dimensional information data set; performing real-time reasoning on the relationship between the temperature gradient and the magnetic path characteristics by using deep transfer learning to obtain multi-dimensional parameter deviation vectors such as stator flux linkage unbalance degree, permanent magnet flux weakening degree and torque coefficient change; generating a multi-scale compensation strategy through a physical enhancement reinforcement learning method and the multi-dimensional parameter deviation vector; and carrying out Lie group transformation processing on the control law under a differential geometric framework, and realizing accurate compensation on the temperature gradient effect through a dual-channel-dual-time scale framework. According to the method, heterogeneous sensing and multi-dimensional compensation strategies are adopted, the problem that the control precision of a traditional method is insufficient under the complex temperature gradient condition is solved, and the high-precision motion control performance is maintained under the dynamic working condition that the temperature distribution of the motor is rapidly changed.

The invention discloses an automatic control code generation and verification method and device, equipment and a storage medium, and relates to the technical field of automatic control, the method is applied to a large language model, and a natural language command is received; performing matching retrieval on the vector database according to the natural language command to obtain an example code snippet; obtaining API structured information corresponding to the example code snippets from a knowledge graph database; generating an initial control code based on the example code snippet and the API structured information; and performing multi-stage virtual operation verification on the initial control code in the software motion control system, and generating a target control code according to a multi-stage verification result confirmed by a user for multiple times and the initial control code. According to the method, the initial control code is automatically generated through double-database retrieval based on the large language model, then multi-stage code verification is performed through the software motion control system, the target control code is generated in combination with verification results confirmed by a user for multiple times, and the code generation speed and reliability are improved.

The invention relates to the field of intelligent mobile equipment control, and discloses a self-adaptive obstacle avoidance control system and an application method thereof, and the system comprises a data collection module, an environment modeling module, an obstacle segmentation module, a path planning module and a motion control module. The data acquisition module is used for acquiring multi-modal data of the surrounding environment of the vehicle; the environment modeling module fuses the multi-modal data, and generates a dynamic environment map including the spatial position, the dynamic characteristic and the free passing area of the obstacle; the obstacle segmentation module is used for segmenting the dynamic environment map and identifying an obstacle area and a free passing area; the path planning module calculates an optimal path with the minimum cost; the motion control module adjusts the motion state of the vehicle in real time through a PID control algorithm or a model prediction control method, and path tracking and dynamic obstacle avoidance are achieved. The method has high-precision environment perception capability, flexible path planning capability and efficient dynamic obstacle avoidance control capability, and can be widely applied to the fields of industrial automation and intelligent logistics.

The invention relates to the technical field of surgical robot motion control, and discloses a surgical robot motion planning method based on a diffusion model, and the method comprises the steps: obtaining obstacle point cloud data, an initial posture and a target posture in a surgical environment; encoding the obstacle point cloud data into a potential space, and combining the encoded information with the initial attitude and the target attitude into a condition code; a Transform structure of an encoder is adopted to replace a U-Net structure in a traditional diffusion model, and the diffusion model is trained through forward diffusion and reverse denoising processes; in training, using a comprehensive loss function to optimize model performance, combining configuration space loss, geometric task space loss and collision loss, and introducing physical constraints to ensure that the trajectory conforms to kinematic characteristics and avoid collision; and in the operation task, generating a motion track based on the trained diffusion model. According to the invention, the motion planning efficiency and safety of the surgical robot in a complex environment can be improved.

The invention discloses a mechanical arm precision control method based on a neural network, and relates to the field of mechanical arm precision control, and the method comprises the steps: S1, constructing a multi-source sensing data collection system of a mechanical arm system, and obtaining a joint control instruction, tail end track feedback, an environment state and stress data in real time; s2, constructing a time sequence prediction model based on a mask encoder, processing high-dimensional dynamic error data by adopting a mask encoder structure, adaptively dividing time sequences of different sampling frequencies through a multi-patch projection module, and introducing an arbitrary variable attention mechanism to capture time sequence dependence and heterogeneity between variables; s3, training the model by adopting a composite loss function; and S4, the mechanical arm target pose error point estimation output by the trained model is injected into a mechanical arm control instruction, the motion track of the mechanical arm is compensated in real time, and the mechanical arm is driven to execute high-precision motion control. The problems that in the prior art, efficient and accurate modeling of a multivariable time sequence is difficult, and scheduling optimization is insufficient are solved.

The invention discloses a path planning and hierarchical cooperative control method and system for an unmanned aerial vehicle cluster. The system comprises a path planning module and a formation motion control module. The method comprises the steps that firstly, an improved RRT * algorithm is adopted by a path planning module, through a multi-strategy heuristic node expansion mechanism fusing target bias and artificial potential field guidance and comprehensively considering path length, channel volume and Z-axis height change, a center path and a three-dimensional safe channel which take into account safety and smoothness are planned for an unmanned aerial vehicle cluster; and then, based on the central path, the formation motion control module adopts a distributed model prediction control framework, designs different optimization targets for a navigator and a follower, and solves an optimal control instruction on line, so that a cluster is guided to complete trajectory tracking, collision avoidance among individuals and self-adaptive formation reconstruction in a secure channel. According to the invention, the navigation problem of the unmanned aerial vehicle cluster in a complex obstacle environment is solved, and the path planning efficiency and the robustness of cooperative control are improved.

The invention provides an automatic driving-oriented kinematics priori guided vehicle trajectory generation method, and belongs to the technical field of trajectory generation and motion control in automatic driving. Comprising the following steps: constructing a vehicle kinematics differential equation, performing nonlinear compensation and control correction based on a double-flow mechanism to obtain a vehicle explicit physical state, and converting the vehicle explicit physical state into a vehicle implicit physical state; feature extraction, target detection and multi-mode fusion are carried out on the collected point cloud data and the vehicle surrounding image data, and environment information of a scene where the vehicle is located is provided; anchoring Gaussian distribution to simulate a feasible noise track of a vehicle in a current scene, generating a noise track candidate sample through sampling and noise adding, performing reverse denoising reasoning on the noise track candidate sample, generating a track anchor point, and generating a reasoning noise track around the track anchor point; environment information of a scene where the vehicle is located, a reasoning noise track and an implicit physical state are input into a diffusion decoder for iterative training, and kinematic prior guides generation of a future track of the vehicle in the denoising process.

The invention discloses a lineation robot control system based on a satellite positioning and orientation technology, which relates to the technical field of robot motion control and comprises a satellite positioning and orientation module, an inertial navigation module, a fusion positioning and orientation software module, a motor control software module, a motion control software module and a pattern planning software module. The satellite positioning and orientation module is used for acquiring latitude and longitude coordinates of the current position of the robot by receiving multi-frequency satellite signals; the inertial navigation module is used for collecting posture and motion data of the robot in real time through a gyroscope and an accelerometer. According to the lineation robot control system provided by the invention, through collaborative design of the multi-frequency-point GNSS receiver and the anti-multipath interference antenna array, centimeter-level continuous positioning capability in a complex urban environment is realized, and satellite signal reflection and shielding interference in scenes such as viaducts and avenues are effectively overcome; and satellite positioning and inertial navigation data are deeply fused through a tight coupling Kalman filtering algorithm.

A joint simulation and operation operability evaluation method based on wind power operation and maintenance ship motion dynamic response and active compensation gangway ladder motion control comprises the steps that an operation and maintenance ship frequency domain seakeeping model is built, and a hydrodynamic coefficient library is calculated; wave exciting force is reconstructed based on a Cummins time domain motion equation, and ship motion response prediction is realized by adopting implicit generalized alpha time domain composite integral; the dynamic positioning system extracts low-frequency motion through a Kalman filter and optimizes thruster thrust distribution to stabilize the position of the ship. A gangway ladder dynamic model is established based on a Lagrange equation, real-time control over displacement, speed and acceleration of the top end of the gangway ladder is achieved through a feedforward-feedback composite control strategy, and nonlinear output of a hydraulic actuator is optimized. And realizing joint simulation of the coupling system through time domain synchronization and self-adaptive PID (Proportion Integration Differentiation) control. And checking gangway ladder movement based on a preset threshold value and a space boundary condition, and evaluating the operability of the climbing operation. According to the method, the limitation of traditional single-disciplinary analysis is broken through, and the forecasting precision of wind power operation and maintenance operation operability is effectively improved.

The invention relates to the technical field of robots, and discloses a multi-source data fusion method of a tracked robot, which realizes dynamic environment adaptation and autonomous decision optimization through a multi-source sensing cooperation mechanism, and comprises the following steps: dynamically adjusting exposure parameters of a visual module based on environment illumination intensity, and avoiding overexposure or underexposure of an image through a feedback adjustment mechanism; fusing data of the accelerometer and the gyroscope to generate attitude angle compensation parameters; the heat dissipation system is intelligently regulated and controlled by comparing infrared thermal imaging and temperature sensor data; dynamic task priority distribution is realized; optimizing an advancing route to reduce complex terrain navigation deviation, in addition, establishing a multi-source sensing coordination mechanism, monitoring health indexes such as motor temperature and vibration frequency in real time, and triggering abnormal state judgment; track optimization is implemented in combination with pavement mechanical characteristics and a kinematic model; and executing the staged energy management strategy. According to the method, the environment sensing precision and the motion control reliability of the tracked robot in a complex environment are remarkably improved.

The invention relates to the technical field of precision detection, in particular to a device and a method for nondestructive testing of a wafer surface. The device provided by the invention comprises a detection light path, a motion control module, a narrow channel detection end, a wide channel detection end and a data acquisition and processing module, the motion control module is used for bearing a to-be-tested wafer and adjusting the position; the detection light path comprises an incident light path, a wide-channel light path and a narrow-channel light path; the incident light path is used for projecting a light beam and vertically projecting the light beam to a wafer to be measured; the narrow channel light path is used for collecting scattered light with the diffusion angle of [5 degrees, 24 degrees] generated on the surface of the wafer to be detected and gathering the scattered light to the narrow channel detection end, and the wide channel light path is used for collecting scattered light with the diffusion angle of [25 degrees, 70 degrees] generated on the surface of the wafer to be detected and gathering the scattered light to the wide channel detection end. According to the device for nondestructive testing of the surface of the wafer, the detection light is vertically incident, the detection light can be reflected out along the original path when the wafer is qualified, and only the defective wafer can generate scattered light, so that the wafer defect can be quickly and accurately identified. According to the wafer defect detection method, the defects of high cost, low precision and high operation difficulty of a wafer defect detection method in the prior art are overcome.

The invention relates to the technical field of robots, and discloses an SLAM navigation-based inspection robot inspection track self-correction method, which comprises the following steps: S1, acquiring environmental data through a 2D laser radar, an attitude sensor and an odometer sensor, and generating an initial environmental map and a navigation path by using an SLAM mapping algorithm; s2, fusing the laser radar pose data, the odometer pose data and the pose sensor pose data to obtain real-time pose information, comparing the current pose with the target path pose, and calculating a path error; s3, a robot motion instruction is adjusted through a compensation control algorithm based on the path error, and the path deviation is corrected; s4, continuously monitoring path errors and correcting the path errors in real time; and S5, storing the corrected track as a navigation path file. According to the invention, by introducing the robot remote controller, complex functions such as mapping, navigation, navigation point marking, path storage, motion control and the like are modularized, and the operation process is greatly simplified.