298 results about "Robot trajectory" patented technology

The invention discloses a welding robot track self-adaptive correction method based on multi-modal data fusion, and particularly relates to the technical field of industrial robots, which comprises the following steps: synchronously acquiring visual three-dimensional point cloud and arc current and voltage transient waveform data in a welding process in real time; an enhanced working trajectory feature vector fusing the visual trajectory features and the arc deviation compensation amount is constructed; respectively calculating real-time confidence coefficients of the visual sense and the arc sensing mode; inputting the confidence coefficient into a weight distribution strategy model, dynamically resolving vision and arc weight coefficients, and when the confidence coefficient of one sensor is low, lifting the weight of the other sensor to dominate correction; performing weighted fusion on the two pose adjustment amounts by using a weight coefficient to generate a six-degree-of-freedom comprehensive trajectory correction amount; and finally, the robot is driven to execute online adaptive trajectory correction. Through a confidence-driven dynamic fusion strategy, the limitation of a single sensor is overcome, and the welding robot trajectory tracking precision and the adaptive capacity are improved.

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 embodiment of the invention discloses a multi-sensor fusion anti-degradation SLAM mapping method and system. The method can effectively solve the problem of pose drift of a robot in a mapping process in structure degradation environments such as an indoor long corridor, constructs a globally consistent three-dimensional point cloud map and a robot trajectory, and comprises the following steps: realizing depth coupling of an IMU and a wheel speedometer based on extended Kalman filtering, and generating high-frequency pose prediction; denoising, down-sampling and motion distortion correction are carried out on the 4D laser radar point cloud, and the normal vector and intensity characteristics of the point cloud are extracted; a normal vector and intensity feature enhanced scanning matching algorithm is adopted, and a target function is optimized through a multi-feature weight, so that the matching precision in a degradation scene is improved; loopback detection is realized through candidate key frame screening and geometric registration verification, and a closed-loop constraint is incorporated into a factor graph for global correction; and finally, incrementally updating the global point cloud map and carrying out consistency optimization, and outputting a robust three-dimensional point cloud map and a high-precision robot track.

The invention discloses a petrochemical explosion-proof humanoid robot trajectory planning method, device and equipment, and the method comprises the steps: collecting petrochemical environment gas concentration data, robot joint motion parameters and target position information, carrying out the dangerous region calibration of the gas concentration data, generating a forbidden map, and determining safety nodes and joint motion ranges; constructing an initial path skeleton based on target position information and safety node distribution, identifying a local dangerous section and a major dangerous section, and generating a motion constraint condition and a poison avoidance path; generating a motion primitive library based on the motion constraint condition and the joint motion range, performing stability filtering to generate stable motion primitives, and forming an advancing scheme set; acquiring a joint load peak value to construct a dynamic envelope, and judging execution fitness to form an execution probability field; and constructing a three-dimensional decision space based on the poison avoidance path, the advancing scheme set and the execution probability field, searching an optimal convergence point to generate a final planning trajectory, and providing a safe and efficient trajectory planning scheme for the petrochemical explosion-proof humanoid robot.

A method of programming an industrial robot (100), with a robot manipulator (110) and robot controller (120), comprises: recording movements of the robot manipulator during a demonstration-based robot programming session, for thereby obtaining a robot trajectory; acquiring a video of the robot programming session; and generating, on the basis of the robot trajectory and the video, a robot program (C) executable by the robot controller, wherein the robot program includes at least one motion command which is expressed in a first reference frame (0 1). The method further comprises capturing operator input data indicating a first object (151) in an image in the video. The generated robot program includes a command to identify a position of an object resembling the first object in a work area (150) of a robot which executes the robot program; and the first reference frame is defined with respect to the identified position of the object resembling the first object.

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 disclosure provides systems and methods for identifying salient test runs involving an autonomous vehicle system. A processor receives sets of run data, each set representative of a driving scenario. For each set, an output set is generated, the output set comprising time-indexed events generated in response to a detected behaviour of at least one challenger agent, and a sequence of decision indicators indicating whether a driving action by an ego agent would be permissible. A data retrieval component is coupled to a results database and retrieves output sets based on the time-indexed events and the sequence of decision indicators. The processor generates the sequence of decision indicators by generating a planned trajectory of the ego agent, and determining whether the predefined driving action by the ego agent would be permissible.

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 visual prediction fused robot circumference welding control method. The method comprises the following steps: firstly, stably extracting weld track characteristics in a strong interference environment through circular motion constrained visual tracking; then, constructing a multi-modal fusion framework, and combining nonlinear state estimation with a space-time attention prediction network to realize prospective compensation of thermal deformation; and finally, a smooth and accurate robot track is generated by adopting hierarchical model predictive control. According to the method, through deep fusion of physical constraint and data driving, the problem of track deviation in the welding process is solved, and the welding seam forming quality and the system adaptability are remarkably improved.

The invention relates to a robot track synchronization control method. According to the technical scheme, efficient collection and over-distance transmission of neural signals are achieved through the quantum entanglement technology, the brain movement intention is analyzed in combination with a high-sensitivity sensor and an intelligent algorithm, an instruction is transmitted to a robot power driving system through a low-delay communication architecture, and real-time synchronization of man-machine tracks is achieved through a two-way feedback system. Meanwhile, the movement flexibility of the robot is improved by adopting a lightweight design, the problems of high delay, low precision, poor interaction experience and the like in traditional man-machine synchronous control are solved, and the method can be widely applied to the fields of exoskeleton robots, industrial collaborative robots, rehabilitation medical robots and the like. The method has the advantages of being low in delay, high in precision, capable of improving interaction experience and wide in application range.

The invention provides a method and a device for predicting an operation track of an action-free labeling robot based on body flow representation, and the method comprises the steps: taking an initial point position in the body flow of the robot, a text feature in a language task instruction and a visual feature in an initial image as multi-modal condition features; the multi-modal condition features are input into the robot trajectory prediction model to obtain body flow information, and the robot trajectory prediction model can comprehensively consider motion of the robot, object related information contained in a language instruction and visual features in an initial image, so that more accurate body flow information is generated; the robot can accurately interact with a specific object; according to the method, the motion constraint information of each joint of the robot is obtained, the body flow information is decomposed into the motion component of each joint through forward kinematics mapping based on the motion constraint information of each joint, and kinematics constraints of different joints of the robot are fully considered, so that the accurate control of the robot motion is realized.

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.

The invention discloses a robot motion path real-time deviation rectifying method based on digital twinning, which comprises the following steps that an off-line programming system generates an initial welding track according to a welding requirement, converts the initial welding track into a code file, transmits the code file to a digital twinning system, and generates a preset track Q; the virtual model layer synchronously maps a motion path of a welding robot entity to obtain a real-time mapping track P; track points of the two tracks are matched, and wrong matching is eliminated; a robot track model is constructed, a predicted track T of entity motion of the welding robot is obtained, and track point matching is conducted on the predicted track T and a preset track Q; and the deviation value of the matched track points of the two tracks is calculated, the robot electric control system corrects the welding track of the welding robot entity according to the deviation value, and the welding track is corrected in real time. According to the correction method, track points are matched only by means of a digital twin system for real-time correction; the adopted track point matching algorithm is small in error and high in matching precision.

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.

The invention relates to an encoder-RFID positioning inspection robot control method, system and device and a medium. The method comprises the following steps: firstly, acquiring a multi-view image, temperature and environmental gas concentration data of pump room equipment, performing time-space synchronization calibration by combining encoder-RFID positioning data, and fusing into multi-source preliminary fusion data; secondly, robot track data are extracted, the real-time position is calculated, RFID calibration is carried out, and features are extracted and integrated into a comprehensive data set in combination with historical data; analyzing the comprehensive data set to determine a normal mode of the equipment, and comparing parameters to generate an abnormal identifier and a fault early warning signal; and finally, wirelessly transmitting an early warning signal, receiving an instruction, adjusting the state of the robot, returning data and generating monitoring data. By adopting the method, the monitoring and positioning precision can be improved, faults can be early warned, the inspection efficiency is improved, and the operation risk is reduced.

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.

The embodiment of the invention relates to the technical field of robots, and discloses a robot trajectory control method and system. The robot track control method comprises the steps that in response to dragging external force applied by an operator, the dragging force is estimated according to external force torque; the displacement deviation value of the robot is obtained based on an admittance control model and the dragging force; the robot is controlled to operate according to the displacement deviation value and the current tail end position; and the joint speed of the robot is obtained in real time, and when the joint speed is larger than a preset speed value, reverse compensation is conducted on the joint speed. According to the scheme of the embodiment of the invention, the reliability, safety and practicability of dragging teaching can be improved.

The invention discloses an orchard crawler differential robot trajectory tracking method based on adaptive incremental control, and relates to the technical field of agricultural robot control. The method comprises the following steps: receiving environmental data collected by a robot sensor, constructing a probability grid map of an orchard environment according to the environmental data, and aligning the probability grid map with a pre-constructed map inertial coordinate system; the course angle error and the distance error between the current position and a target point are calculated based on the pose of the robot, an error vector is formed, and the target point is dynamically planned according to the fruit maturity and reachability priority detected by a visual system and a laser radar point cloud. According to the method, coordinate transformation is not needed, and different from traditional tracking control, a control instruction is generated directly based on pose increment, the course and distance error of a target point is calculated directly based on an inertial coordinate system, and real-time matrix projection operation is avoided.

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.

An industrial robot trajectory detection device, comprising a base plate (1), wherein a support plate (2) is fixedly mounted on the base plate (1), a disk (3) is provided on the support plate (2), a driving assembly is provided on the disk (3), a track assembly is provided on the driving assembly, a tension sensor (4) is provided on the track assembly, a tension spring (5) is provided on the tension sensor (4), and a first magnet (6) is fixedly mounted at the end of the tension spring (5) away from the tension sensor (4). A track plate (15) is driven by an electric motor (12) of the driving assembly for angular rotation adjustment, the position of a sliding block (17) on the track plate (15) may also be adjusted by means of an electric push rod (16), and a second magnet (10) on a robotic arm (9) and the first magnet (6) are attracted such that an industrial robot performs trajectory detection on the disk (3), and different trajectory data is detected by changing a trajectory, thereby effectively improving the work efficiency, precision and adaptability of the industrial robot.

The invention provides an inspection robot trajectory planning method suitable for a compact obstacle environment of a power distribution room, and solves the problems that keys are compact and an obstacle is close to a target key in the power distribution room environment. According to the method, a bee colony and a Q table are initialized, a fitness function is constructed, robot motion and environment constraints are introduced, and an exploration or development strategy is dynamically selected according to a population state. A particle swarm algorithm with control parameters is introduced in a scout bee stage, and the global search capability and convergence efficiency are enhanced. The optimal solution is updated through an elitist strategy and neighborhood search, and the Q value is adaptively adjusted by means of feedback, so that the strategy flexibility is improved. According to the method, the global search advantage of the particle swarm and the local development advantage of the artificial bee colony are integrated, the trajectory precision and the operation safety are guaranteed, the efficiency and the real-time performance are also improved, the success rate reaches 98.16%, the time is reduced by 10.11% compared with that of an artificial bee colony algorithm, and the method is suitable for popularization and application. The method is suitable for a patrol robot track planning task with a complex distribution room structure, a compact control panel arrangement and a closer obstacle to a target.

The invention discloses a multi-mechanical-arm shortcut optimization method and device, electronic equipment and a storage medium, and relates to the field of multi-mechanical-arm path planning. The method comprises the following steps: determining a target strategy from candidate strategies, and determining a target shortcut optimization method from candidate shortcut optimization methods by adopting the target strategy; wherein the candidate shortcut optimization method comprises at least two of a composite space method, a priority method or a path shortcut method; randomly selecting to-be-processed point pairs from the initial trajectory of the multiple mechanical arms, and optimizing original trajectory segments between the to-be-processed point pairs by adopting a target shortcut optimization method to obtain a middle shortcut; and if the middle shortcut is effective, replacing the original track section with the middle shortcut until the optimization is cut off to obtain the target tracks of the multiple mechanical arms. According to the technical scheme, a shorter and smoother multi-mechanical-arm track is generated under the condition that no collision is guaranteed, and the overall running speed and task execution efficiency are improved.

The invention relates to the field of soft-bodied robot control, in particular to a soft-bodied robot trajectory tracking control method based on event triggering model predictive control, which comprises the following steps: step 1, on the basis of a segmented constant curvature hypothesis of a soft-bodied robot, considering system uncertainty including unmodeled dynamics and exogenous disturbance; establishing a software robot dynamic model and converting the software robot dynamic model into a state space model; 2, designing an event triggering model prediction control strategy based on the state space model of the soft robot; 3, a dynamic event trigger threshold value and a dynamic prediction time domain method are introduced to improve the calculation efficiency; 4, the recursion feasibility and closed-loop stability of a control strategy are ensured through parameter design; and 5, performing simulation analysis on the end tracking of the soft robot by applying the designed event triggering model prediction control strategy.

The invention discloses an object following robot system and method based on multi-sensor fusion, and the system comprises an object detection module which comprises a plurality of sensors, and the plurality of sensors are used for obtaining the position information of a following object; the sensor fusion module is used for processing and fusing the position information from various sensors to obtain target position data; the path planning module is used for generating a real-time robot advancing track through a dynamic path planning algorithm according to the target position data; and the motion control module is used for controlling the robot to move according to the advancing track of the robot. According to the object following robot system and method based on multi-sensor fusion, multiple sensors are adopted to jointly sense the environment, and the target detection and positioning accuracy is improved; and in combination with a dynamic path planning algorithm, precise path planning and tracking of the following object are realized.