184 results about "Robot trajectory" patented technology

The invention discloses a spraying robot trajectory planning method based on depth camera scanning. The method comprises the steps that wall surface boundary polygon data, an initial spraying stroke set and a spraying dosage model parameter set are obtained; calculating a predicted coating thickness field, and performing difference calculation on the predicted coating thickness field and a preset target thickness to generate a thickness error field; performing connected domain clustering on the thickness error field to generate topological thickness error regions, and determining region type labels for the topological thickness error regions one by one; calling a matched editing operator from a discrete stroke editing operator library, performing geometric constraint verification, and generating a candidate editing scheme; and evaluating the candidate editing schemes based on a preset comprehensive scoring function, updating the initial spraying stroke set by using the scheme with the optimal score, obtaining an optimized spraying stroke set, and converting the optimized spraying stroke set into a robot control instruction. According to the method, the problem that global coverage and local thickness uniformity cannot be considered in traditional geometric planning is solved, and high-quality full-automatic spraying is achieved.

The invention discloses a robot track online compensation and precision control method and system based on multiple sensors, and belongs to the technical field of robot track control. The method comprises the following steps: acquiring target trajectory data and original data of each sensor, and performing time alignment on the original data of each sensor to generate a fusion data set comprising vibration acceleration data and pose data; performing time-frequency feature extraction based on the vibration acceleration data to obtain a dominant vibration frequency spectrum; constructing a mechanical transfer function model of the joint driving torque and the tail end vibration displacement; based on the mechanical transfer function model, constructing an adaptive notch filter to generate a feed-forward compensation sequence; according to the target trajectory data, the pose data and weight parameters, updated in real time, of the sensors, a trajectory fusion deviation value is obtained through calculation; and after the feedforward compensation sequence and the trajectory fusion deviation value are integrated, a joint driving current instruction is generated based on a fuzzy PID control algorithm. The robot trajectory control precision can be improved.

The invention relates to the technical field of robots and intelligent control, and discloses a robot trajectory control method, device and equipment based on multiple modalities and a medium, and the method comprises the steps: obtaining a natural language instruction, environment visual information and current state information of a robot, carrying out the fusion of visual and language features, and generating a fusion feature, generating a track sequence through a track generation model according to the fusion features and the robot state information, collecting a contact force signal of the tail end of the robot in contact with the target surface, adaptively adjusting impedance parameters by using the contact force signal, and generating a control instruction; and based on the contact force signal, feedback correction is carried out on the track generation model to drive the tail end of the robot to move, and multi-mode sensing and force control feedback cooperative control is achieved. According to the invention, track and force control cooperation is realized through multi-modal fusion and contact force feedback, so that the robot keeps stable fitting and adaptive motion in a complex contact scene, and the track generation precision and the imaging quality are improved.

The invention provides a robot trajectory tracking and obstacle avoidance control method, system and device and a medium, and belongs to the field of intelligent robot control, and the method comprises the steps: obtaining motion state information, obstacle information and a reference trajectory; calculating a look-ahead distance based on preset prediction time, a safety coefficient and a current maximum speed in the motion state information; selecting a locally planned target point from the reference trajectory according to the look-ahead distance; based on a dynamic window algorithm, generating a plurality of candidate tracks according to the motion state information and the obstacle information; based on the multi-target cost function and the target point, evaluating each candidate track to obtain a comprehensive cost value; and determining the candidate trajectory with the minimum comprehensive cost value as an optimal trajectory, obtaining an expected linear velocity and an expected angular velocity based on the optimal trajectory, and generating a control instruction to drive the robot to move along the optimal trajectory. According to the invention, the motion stability, path continuity and overall robustness of the robot in a continuous multi-section inspection task are improved.

The invention discloses a multi-degree-of-freedom mechanical arm control method based on quantum particle swarm optimization of a migration strategy. The method comprises the steps that system modeling is conducted; mPC to-be-adjusted parameter definition and constraint processing are carried out; adopting, adopting, and introducing penalty terms to construct a composite fitness function; updating particle positions by adopting a basic or enhanced quantum updating mode; triggering conditions are judged in a group diversity measurement mode, and when the conditions are met, a migration strategy and dynamic subgroup division are executed; the global optimal solution and the fitness value are loaded into a real-time MPC controller, and online adjustment is carried out; a prediction equation is constructed, tracking errors and energy consumption optimization are converted into a standard quadratic programming problem, and the solving precision is dynamically adjusted in combination with Cholesky pre-decomposition, a structured sparse solver and a warm-start and early stop strategy; and a closed-loop adaptive control system is constructed. According to the method, the precision, robustness and real-time performance of trajectory tracking control of the mechanical arm can be effectively improved.

The invention discloses a robot trajectory planning method for a spraying production line, a robot spraying control method and a robot trajectory planning device.The trajectory planning method comprises the steps that model surface information is extracted, an extracted surface geometric figure is processed, and a surface spraying path and a connection transition path between surfaces are generated; and finally, generating a collision-free and full-coverage complete spraying motion path. According to the robot spraying control method, an optimal solution is selected by using an optimization algorithm to plan and control robot movement, so that the robot spraying quality is improved while the stability of a robot movement track is ensured; the robot trajectory planning device provides a workpiece conveying module and a robot and is used for applying the robot trajectory planning method to real spraying production line hardware.

The invention discloses a grabbing and placing task-oriented robot trajectory planning method and device, and the method comprises the steps: obtaining a natural language task instruction, carrying out the path decomposition based on a large language model, and generating a stage sequence containing pre-grabbing, grabbing, lifting, pre-placing, placing and withdrawing, and each stage target and / or constraint; receiving the optimal grabbing pose and / or placing pose provided by the upstream module, and replacing or correcting stage targets related to grabbing and / or placing; generating a trajectory segment according to the corrected stage target and constraint, and carrying out smoothing processing to obtain a trajectory sequence; performing collision detection on the track sequence, and if collision occurs, outputting collision information and executing local repair or replanning until the collision detection is passed or a preset replanning condition is met; and outputting the executable track sequence of the robot controller. According to the scheme, stage decomposition and key pose landing are achieved, and a collision feedback closed loop is formed to obtain an executable track.

The invention discloses a singularity-avoiding trajectory planning method for a free floating space mechanical arm, and belongs to the technical field of robot trajectory planning. The method comprises the steps that the expected whole-course tail end position track and posture track of the mechanical arm are obtained; setting a control period and circularly executing; at each control moment, a generalized Jacobian matrix and system singularity measurement are calculated, and whether the system is close to singularity or not is judged; traversing all the possible values of the base postures if the base postures are close to singularity, calculating joint angle states and corresponding singularity measurement which do not change the tail end postures, and searching a system configuration corresponding to the maximum singularity measurement; then, the state of the mechanical arm is reconstructed, and the mechanical arm is made to move to the maximum singularity measurement configuration to be separated from singularity; and calculating the inverse of the generalized Jacobian matrix to obtain velocity inverse kinematics, and further calculating the joint angular velocity and the joint angle at the next moment. According to the method, an accurate trajectory planning effect without introducing any theoretical error is realized while the singularity avoidance capability is ensured.

A method of programming an industrial robot, comprising: recording movements of a robot manipulator during a demonstration-based programming session, to obtain a robot trajectory (301); using a natural-language model (171), parsing speech data describing the programming session into at least one robot parameter (305) value and annotating the robot trajectory with the robot parameter value; and, on the basis of the annotated robot trajectory (306), generating a robot program (307). The method further comprises: capturing a video (302) of the programming session; using a vision-enabled language model, VLM (172), generating descriptions (303) of movements of the robot manipulator which are visible in the video; and generating said speech data (304) on the basis of the descriptions of the robot manipulator movements.

A method for defining a trajectory of a robot (1) for moving along a path from an initial point (A) to a final point (B) in a multidimensional space, each of whose points has a plurality of space coordinates, which are position and / or orientation coordinates, wherein the trajectory is a continuous map mapping an initial instant in time to the initial point (A), a final instant to the final point (B) and intermediate instants to intermediate points of the path, comprises the steps of defining (S3) a cost function which associates a cost value to a given trajectory, defining (S3) a pluridimensional subspace of said multidimensional space and determining (S4) among trajectories extending along different paths of said subspace, a trajectory which minimizes the cost function.

The embodiment of the invention relates to the technical field of robot moving trajectory planning, and provides a robot moving trajectory optimization control method and device, electronic equipment and a medium, and the method comprises the steps: obtaining visual observation data and a natural language task instruction when a robot executes a current moving trajectory; joint reasoning is carried out on the visual observation data and the natural language task instruction, a discrete action sequence is generated, smooth optimization is carried out, and a first continuous running track is generated; the robot is controlled to be converted from the current moving track to execute the first continuous moving track, and the visual observation data and the natural language task instruction of the robot executing the first continuous moving track continue to be obtained; and smooth transition processing is carried out on the newly generated second continuous moving track and the first continuous moving track, and the robot is controlled to be transited to the second continuous moving track from the first continuous moving track. Therefore, smooth trajectory generation meeting dynamic constraints, continuous trajectory transition under dynamic instructions and safety control in an abnormal state are realized.

The invention relates to the technical field of robot motion control and trajectory planning, in particular to a robot motion trajectory planning method based on AI reinforcement learning. The method comprises the following steps: acquiring robot workspace layout information and an environment sensing probe candidate list, performing probe configuration planning, calibration and planning session index generation, and generating a planning session primary key recording structure; executing multi-source observation acquisition scheduling, data alignment and three-dimensional grid modeling, and generating a reinforcement learning state basic structure; state vector construction and continuous action strategy inference are carried out, and a candidate motion track set structure is generated; and track rolling simulation, grid collision risk assessment, multi-target reward calculation, target track selection and strategy parameter updating are carried out to generate a strategy network parameter set structure. Through combination of multi-source perception and reinforcement learning, intelligent planning of the motion trail of the robot is realized, the safety and efficiency of the trail are improved, and the adaptability and robustness in a dynamic environment are enhanced.

A system and method for estimating a collision probability between a robot and a radiance field within a three-dimensional environment includes: determining forward occupancy information indicating potential over-approximating volumes that are able to be occupied by a robot disposed at a starting position; modeling an environment of the robot using a radiance field; and computing a probability of collision between the robot and an obstacle within the environment based on the forward occupancy information and the radiance field. The estimation method is useful for robotic trajectory determination that involves discretizing a trajectory of the robot into a sequence of trajectory segments over time subintervals; computing an upper-bound for a probability of collision between the robot and a radiance field at each of the time subintervals using a Gaussian Splatting model that normalizes 3D Gaussians within the radiance field; and performing real-time trajectory adjustments based on the computed collision probability upper-bounds.

The invention provides an unmanned plate sorting area dynamic safety protection system based on multi-modal fusion perception, relates to the field of plate sorting protection, and solves the technical problem that adaptive protection cannot be performed on dynamic complex risks by depending on static rules and a single data source. The method comprises the following steps: a multi-sensor data acquisition and fusion module obtains an environment perception data set; and the personnel posture recognition and trajectory tracking module is used for analyzing the score of the correlation degree between the working personnel posture and the environment and outputting the personnel posture category, the position coordinate and the movement speed in real time. And the robot trajectory analysis and multi-channel prediction module outputs multi-channel trajectory prediction data containing short-term and long-term prediction results in real time through a kinetic model or a learning predictor. And the collision risk fusion judgment module is used for determining a historical high-frequency close-range event occurrence area and calculating a collision risk probability. And the grading early warning module generates a corresponding early warning instruction and an obstacle avoidance strategy. The plate sorting device is used in the plate sorting process.

The invention provides a kidney ultrasonic scanning robot trajectory planning method and device based on multi-modal sensing. The method comprises the steps that multi-modal sensing data in the kidney ultrasonic scanning process are obtained in real time; aiming at each mode in the multi-mode sensing data, calculating the confidence coefficient of the mode; carrying out softmax processing on the confidence coefficient of each modal to obtain a fusion weight, and mapping the fusion weight into a planning weight; constructing an objective function based on the planning weight, and setting constraint conditions including medical scanning protocol constraints and anatomy safety boundary constraints; initializing a solving process by adopting a hot start mode, solving the objective function, and optimizing the control sequence; and according to the control sequence, controlling the robot to execute a scanning action, and generating and updating a coverage map until complete scanning of the kidney is completed. According to the method, the accuracy of kidney ultrasonic scanning trajectory planning is improved.

The invention relates to the technical field of display adjustment, in particular to a display support adjustment robot trajectory optimization method, which comprises the following steps: establishing a robot trajectory database including robot body parameters, display size and weight, user adjustment preference, a kinematic model and a trajectory performance optimization target; key path points are planned according to display parameters and user preferences; a trajectory planning space is constructed based on robot parameters and key path points, an initial trajectory is generated by adopting a piecewise polynomial interpolation method, and smoothness and accuracy of a motion stage are ensured; and in the trajectory planning space, setting a multi-objective optimization function based on a performance optimization objective, and performing parameter optimization on each motion stage of the initial trajectory to obtain a trajectory with optimal comprehensive performance. According to the method, through multi-dimensional data fusion, segmented interpolation optimization and multi-target collaborative optimization, the problems that a traditional fixing support adjusting mode is insufficient in flexibility, poor in track smoothness and high in energy consumption are solved.

The invention belongs to the field of robot trajectory optimization, and particularly relates to a trajectory optimization method and system considering redundancy characteristics of a robot macro-micro force control grinding and polishing system. The method comprises the following steps: S1, generating an initial grinding and polishing track based on a CAD model, and providing a discrete point curvature estimation method to estimate the curvature of each path point; s2, on the basis of the curvature of the discrete points, a mixed optimization target of a curve energy item and a curvature change rate about curvature square is constructed, arc length and redundancy direction optimization constraints are considered, and an adjusted grinding and polishing track is generated through a nonlinear optimization method; s3, on the basis of the optimized initial points, an initial dominant point selection method based on an angle-chord height-chord length joint criterion is adopted, and the trajectory precision is kept while the number of dominant points is reduced; and S4, on the basis of the optimized curved surface dominant points, an improved least square iterative approximation method is provided to generate a parameterized B-spline grinding and polishing path generation method, and final grinding and polishing track generation is achieved.

The invention discloses a robot orchard inter-row positioning method based on multi-modal semantic graph optimization, and aims to solve the problems of inter-row leakage and inter-row jump in positioning in orchard moving operation. Time synchronization and external parameter calibration are carried out on multi-sensor data, semantic analysis and geometric preprocessing are carried out, and the positioning accuracy of the orchard inter-row positioning method is improved. The method comprises the following steps: generating tree trunk, pile body, tree wall and ground boundary observation, constructing a semantic factor graph containing row identifiers and continuous variables, adopting a graph converter to perform adaptive weighting and covariance calibration, combining soft and hard two-stage increment optimization of anti-fact mutual exclusion gating and discrete continuous combination, outputting the row identifiers, robot trajectories, transverse deviation and course angles, and constructing a semantic factor graph containing the row identifiers and the continuous variables. The technical effects of stably inhibiting the inter-row jump and improving the row identification judgment accuracy and the transverse deviation and course angle estimation precision are achieved.

The invention provides a welding robot trajectory planning method and system based on point cloud and modeling data, and belongs to the technical field of robots. The method comprises the steps that point cloud data of a workpiece to be welded are collected, and three-dimensional point cloud covering a welding seam area is extracted through preprocessing; acquiring a weld joint key point and a simulated welding path from the three-dimensional model, and generating an ideal track including tangent and normal directions; performing rough registration and fine registration on the simulated welding path and the center line of the three-dimensional point cloud to realize space matching; mapping the ideal track to a three-dimensional point cloud, and adjusting the posture of the welding gun according to the local geometric characteristics to determine the optimal angle between the welding gun and the surface of the welding seam to obtain a planned track; and verifying and correcting the planned track through closed-loop feedback to obtain a final welding track. According to the method, rapid generation and accurate alignment of the track are achieved, the method can effectively adapt to a complex welding seam structure, and the welding precision and robustness are greatly improved.

The invention discloses a Delta robot trajectory tracking method based on fuzzy-RBF (Radial Basis Function) control. The Delta robot trajectory tracking method comprises the following steps: firstly, establishing a conventional dynamic model of a Delta robot; a virtual control signal is introduced, a sliding mode surface is constructed, and a control law of a dynamic surface sliding mode controller is designed to ensure the stability and convergence of the system. And constructing a specific fuzzy logic system for adjusting the control gain. A radial basis function (RBF) structure is embedded into a fuzzy membership function, and the center and width of the membership function are iteratively updated by adopting an online adaptive optimization algorithm based on gradient descent, so that dynamic self-learning and parameter adaptation of a fuzzy system are realized. Finally, the output of the designed fuzzy-RBF self-adaptive dynamic surface sliding mode controller acts on a robot joint driving system, and high-precision trajectory tracking control of the Delta robot end effector is successfully achieved. A simulation result verifies the effectiveness and robustness of the provided control method in the aspects of processing parameter uncertainty and external disturbance.

The invention discloses a robot motion planning method, device and equipment for a constrained trajectory and a medium, and belongs to the field of industrial robot trajectory planning, and the method comprises the steps: converting the motion constraint of a robot at the current moment into a path parameter space; solving a first movement speed of the robot through a third-order sliding mode controller; performing look-ahead simulation on each second constraint boundary set of the robot in the remaining paths; if the simulation result is that the second movement speed exists, the smaller value of the first movement speed and the second movement speed serves as the movement speed of the robot; if the simulation result is that the second movement speed does not exist, the second movement speed of the last period serves as the movement speed of the robot; and controlling the robot to move for a period duration, and if the robot does not walk to the path end point after the motion is finished, circularly executing all the steps until the robot walks to the path end point. Therefore, by implementing the method, real-time planning of the constrained trajectory can be realized.

This invention discloses a robot trajectory learning method and system based on surface dynamic motion primitives. The method includes: acquiring multiple teaching trajectories, parameterizing and rotating the trajectory sampling points to construct a reference coordinate system; learning trajectory features in the u and v directions based on the DMPs framework, and generating a static surface through Gaussian smoothing and weighted averaging; modeling obstacles using ellipsoids, designing dynamic obstacle avoidance coupling terms embedded in the DMPs formula to enable real-time bending of the surface for obstacle avoidance; learning velocity and acceleration information through classical DMPs, and combining it with parameter mapping of the starting / target position of the new task to generate an obstacle avoidance trajectory and control the robot's motion. This invention solves the problems of traditional DMPs being unable to directly learn surfaces, having lag in obstacle avoidance response, and losing spatial information, improving the robot's trajectory adaptability and real-time obstacle avoidance capability in semi-structured environments, and ensuring assembly accuracy and safety.

The present application relates to the technical field of hub polishing robots, and particularly relates to a hub polishing robot trajectory planning method based on 3D vision curve technology; by comparing an actual 3D curved surface graph with a preset 3D curved surface graph, a to-be-polished region model graph of each to-be-polished region in a target hub is generated; according to the to-be-polished region model graph of each to-be-polished region, optimal polishing parameters for polishing processing of each to-be-polished region in the target hub are obtained by pairing in a database; according to the optimal polishing parameters for polishing processing of each to-be-polished region in the target hub, optimal polishing trajectories of a polishing robot are planned; and the optimal polishing trajectories of the polishing robot and the optimal polishing parameters for polishing processing of each to-be-polished region in the target hub are sent to a control terminal. More accurate, self-adaptive, efficient and consistent polishing effects can be achieved, and production efficiency and product quality are improved.

A surgical robot system includes a surgical robot, a robot arm connected to such surgical robot, and an end-effector connected to the robot arm. A registration fixture is used in conjunction with various registration systems in the surgical robot system. Such registration systems likewise include a detachable base in the form of a detachable dynamic reference base, along with an associated mount, the dynamic reference base and mount having certain features which permit the dynamic reference base to be selectively attached, detached, and reattached at different phases of an operation, whether pre-operative or intra-operative, and such successive attachments are done without the dynamic reference base, and tracking markers associated therewith, losing registration. Related methods allow for the more efficient and effective performance of operations by virtue of the dynamic reference base maintaining its registration during attachments and reattachments.

The invention provides a parallel robot trajectory optimization method and device based on a self-adaptive Bezier curve. The method comprises the steps of data acquisition, attitude analysis, structural mechanics analysis, evaluation index selection and weight formulation. Key posture parameters and stress conditions of the side protection mechanism are obtained by monitoring pressure of a rodless cavity of a side protection jack and displacement of a piston rod, and the side protection state is monitored by selecting six indexes through posture analysis and structural mechanics analysis of the side protection mechanism. In order to solve the problems of small rod diameter, small cylinder diameter and severe working environment of the side protection jack, the built-in wireless pressure and displacement sensor is adopted to obtain displacement and pressure data of a piston rod of the side protection jack, and meanwhile, the posture of the side protection plate is calculated through the obtained data information; recognizing the face protection state according to the key posture parameters of the face protection plate and the supporting force of the face protection plate to guarantee safe and efficient production of the fully mechanized coal mining face.

The invention discloses a robot self-adaptive trajectory control method based on a radial basis function neural network, belongs to the field of intelligent equipment control, remarkably reduces huge calculation burden in a traditional optimization strategy, avoids time and operation space cost caused by the huge calculation burden, does not need to depend on a model training learning process before control, and improves the control precision. Therefore, the trajectory tracking control precision of the continuum robot is effectively improved. The method mainly comprises the following steps: constructing a continuum robot nominal dynamic model through a Lagrange algorithm; designing a radial basis function neural network function for approximation estimation of Coriolis force, gravity, elastic force and external interference in subsequent controller design; subtracting the actual output from the ideal output of the system to form a tracking error z1, and constructing a first error variable; designing a Lyapunov candidate function for the integral of the first error variable, and designing a first virtual controller; the method is mainly used for track control of the continuum robot.

The invention discloses a robot intelligent welding unit and method based on 3D visual guidance, and the unit comprises an industrial robot arm, a welding gun, welding equipment, at least one 3D visual sensor and a visual processing controller. A three-dimensional reconstruction module, a self-adaptive path planning module, a real-time feedback control module and a safety monitoring module are integrated in the visual processing controller; a workpiece is scanned through the 3D vision sensor, a three-dimensional model is reconstructed, a welding seam is automatically recognized through the self-adaptive path planning module, an optimized welding path with embedded technological parameters is generated, and in the welding process, the real-time feedback control module dynamically corrects the robot track and adjusts the welding parameters based on a prediction-correction strategy, so that the welding quality is improved. And meanwhile, the safety monitoring module utilizes real-time point cloud to construct a virtual fence for collision protection, so that the whole-process intelligence from pre-welding sensing and autonomous planning to real-time correction and safety monitoring in welding is realized, and the welding precision, the process adaptability and the operation safety are greatly improved.

The invention discloses an underwater robot trajectory tracking method and a related device, and relates to the technical field of underwater robot autonomous navigation, and the method comprises the steps: synchronously collecting the original data of an underwater robot camera, an IMU and a DVL; performing time synchronization on IMU and DVL data by taking a camera timestamp as a reference, and converting DVL speed data into an IMU coordinate system; calculating a relative translation vector and a time interval based on the relative pose transformation matrix of the adjacent key frames, and determining an average speed estimation value of the underwater robot between the adjacent key frames; comparing the average speed estimation value with the actual measurement speed of the DVL, and determining a speed error function of the DVL; and constructing a weighted error cost function containing visual re-projection, IMU pre-integration and DVL speed error terms, solving optimal estimation of key frame poses and map point states through nonlinear least square optimization, and outputting an optimized trajectory of the underwater robot. According to the invention, the precision of trajectory tracking of the underwater robot is effectively improved.

With the rapid development of technologies such as the Internet of Things (IoT), smart devices, the Industrial Internet, and the Internet of Vehicles (IoV), various systems collect, process, and store massive amounts of data during operation, including sensitive information such as process parameters of industrial equipment, robot trajectories, location information of vehicle systems, indoor images of smart homes, and trade secrets of enterprise office systems. If this private information is not properly protected, it may lead to serious consequences such as the leakage of trade secrets, infringement of user privacy, and legal compliance risks. Existing technologies suffer from insufficient proactive identification capabilities, simplistic protection strategies, lack of adaptive learning, and disconnected auditing. This application provides a privacy data management method, system, and computer-readable storage medium, aiming to achieve proactive identification, hierarchical protection, adaptive optimization, and end-to-end auditing of privacy information in various types of data. It can be widely applied to various data processing scenarios such as industry, vehicles, homes, and enterprises.

The utility model discloses an industrial robot track detection device, which relates to the technical field of robot equipment and comprises a detection table, a track is arranged in the middle of the detection table, a lubricating structure is fixedly connected to the top of the detection table, a sliding block is slidably connected in the track, the lubricating structure lubricates the track and the sliding block in an oil dripping manner, and a magnetic attraction structure is movably arranged at the end part of the sliding block. An electric guide rail is arranged on the side away from the detection table and is in transmission connection with a movable block, the top of the movable block is fixedly connected with a robot body, the end of the robot body is fixedly connected with a second magnetic attraction piece, and the second magnetic attraction piece is connected with a magnetic attraction structure. According to the utility model, the lubricating structure is arranged at the top of the detection platform, so that the rail and the sliding block sliding in the rail can be lubricated in an oil dripping manner, the surface abrasion is greatly reduced, the service life of the sliding structure is prolonged, and the accuracy of later detection is improved.