404 results about "Robot trajectory" patented technology

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.

A method for controlling a robot. The method includes determining, for each robotic pose of a plurality of predetermined robot trajectories, a respective embedding in an embedding space having the structure of a hyperbolic manifold by searching an optimum of an objective function which incites, for each of the predetermined robot trajectories, the embeddings of the robotic poses of the predetermined robot trajectory to follow pre-defined dynamics of the embedding space, determining, for a starting pose from which the robot is to be controlled, a start embedding in the embedding space (, and, for a desired end pose, an end embedding in the embedding space and a geodesic between the start embedding and the end embedding according to a pullback metric of the embedding space and controlling the robot according to a sequence of robotic poses given by the determined geodesic.

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.

A method of programming an industrial robot (100), composed of a robot manipulator (110) and a robot controller (120), comprises: recording movements of the robot manipulator during a kinesthetic programming session, for thereby obtaining a robot trajectory; capturing speech data while recording the movements of the robot manipulator; decomposing the recorded speech data into a plurality of phases; for each phase of the speech data, parsing the speech data into robot parameter values using a natural-language model (170), identifying a contemporaneous phase of the robot trajectory and annotating it with the robot parameter values; and, on the basis of the annotated robot trajectory, generating a robot program executable by the robot controller.

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 system for steel structure welding, relates to the field of steel structure machining equipment, and aims at solving the problem that a steel structure can be welded by collecting robot track data and a welding seam image in the welding process, constructing a track continuity evaluation model, identifying a path change trend and fusing an image structure and track change information. The position and confidence of a track interruption section are recognized, so that the high-precision recognition and automatic recovery capability after path interruption is improved; a smooth compensation track is generated by adopting third-order Bessel interpolation based on starting and ending points of an interruption section, curvature and posture consistency registration with an original track is completed, and continuity and stability of a welding path are ensured; multi-modal data such as current, voltage, temperature, acoustics and welding seam images are collected through multiple channels, welding abnormity is recognized, state feature vectors are formed, and the problems such as splashing abnormity and welding seam deviation can be found in real time; through multi-dimensional analysis and dynamic adjustment of the whole welding process, the stability, intelligence and adaptability of the welding system are remarkably improved.

The invention discloses a cooperative positioning and trajectory planning method based on an air-ground robot system, and the method comprises the steps: enabling unmanned vehicles to construct a global environment map through a laser radar, achieving the positioning of the unmanned vehicles, obtaining the distances between the unmanned vehicles through the combination of an IMU (Inertial Measurement Unit) and a UWB (Ultra Wideband) distance measurement chip of the unmanned vehicles, and carrying out the real-time pose estimation. Based on the fused pose and global map information, a method of combining mixed A * search and nonlinear trajectory optimization is adopted, a collaborative trajectory planning problem is converted into a multi-constraint optimization model, the sight distance quality between air-ground robots, an unmanned vehicle team-shaped geometric structure and an unmanned aerial vehicle rapid navigation problem are comprehensively considered, and the space-ground robot cooperative trajectory planning method is established. And generating a collision-free optimized cooperative trajectory meeting multi-robot kinematics constraints in real time. According to the method, the positioning precision and the coordination degree of the air-ground coordination system in a complex dynamic environment are remarkably improved, and a safe, efficient and real-time heterogeneous multi-robot trajectory planning method is provided for various application scenes such as routing inspection, search and rescue, environment monitoring and the like.

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.

The invention belongs to the technical field of aircraft coating processing, and particularly relates to an aircraft surface automatic spraying method based on a heavy-load robot, which comprises the following steps: carrying out geometric modeling on an aircraft body workpiece; a spray gun tool coordinate system is calibrated, and TCP coordinate optimization calculation is carried out by fusing spray gun 3D model parameters and flame long distance values; constructing a global workpiece coordinate system dynamic database; automatic coverage of a multi-layer U-shaped reciprocating spraying path is achieved through a path planning algorithm; after a fixed spraying distance parameter is set, the spraying software process package generates a robot track, and path simulation is carried out; a robot movement track program is generated by combining parameters of coating types, plasma flame lengths, spraying angles and spraying speeds; and a low-speed transition point is inserted into a track alarm point, and dynamic smooth connection of a spraying path is achieved. The spraying efficiency of the large machine body workpiece is improved, and manual intervention is reduced; and the adaptability is high, and spraying objects of different shapes and sizes can be rapidly dealt with.

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 finite iteration error tracking learning control method for robot accurate trajectory tracking. The method comprises the steps of establishing a kinetic equation of a repeated operation robot system, determining a discrete dynamic model of the robot system, designing a finite iteration error tracking learning controller and carrying out simulation verification. According to the method, under the conditions that the initial position of the robot system is arbitrary and the expected trajectory changes, the number of iterations required for achieving arbitrary precision tracking of the given expected error trajectory by the output error of the robot system can be calculated by designing an error tracking learning controller and only utilizing error information obtained through first iteration. According to the method, the limitation that zero error convergence needs to depend on infinite iterations in a traditional iterative learning control method is effectively overcome; meanwhile, it is ensured that the robot system can still achieve the preset tracking precision with the rapid convergence capacity under the disturbance conditions that the initial position is arbitrary and the expected trajectory changes, and the practicability and the popularization value of iterative learning control in actual engineering are improved.

Techniques for robot control using multi-modal user inputs include receiving one or more multi-modal inputs from a user, extracting a motion hint from the one or more multi-modal inputs, generating estimated noise based on a current motion scene for the robot, generating a plurality of candidate motion plans, iteratively denoising the plurality of candidate motion plans based on the estimated noise and the motion hint to generate a plurality of revised robot motion plans, selecting a robot motion plan from the plurality of revised robot motion plans, generating a robot trajectory from the selected robot motion plan; and commanding the robot to perform a first step of the robot trajectory.

The invention relates to the technical field of mobile robot control, and discloses a wheeled robot trajectory tracking anti-interference control system, which comprises a nonlinear dynamics modeling module used for establishing a nonlinear dynamics model of a wheeled robot so as to describe the motion characteristics of the robot under the influence of ground friction force, sliding force and modeling errors; the disturbance estimation module is used for estimating external disturbance and system modeling errors in real time through an extended state observer and providing disturbance compensation data for the controller; and the sliding mode control module is used for realizing rapid convergence of track deviation by constructing a sliding mode surface and enhancing the anti-interference capability of the system by dynamically adjusting the sliding mode gain. According to the invention, through nonlinear dynamic modeling, disturbance estimation, sliding mode control and model prediction control fusion and real-time trajectory optimization, high-precision trajectory tracking is realized, and the anti-interference capability and the dynamic environment adaptability are enhanced.

The invention discloses a robot track generation method and a robot track generation system based on a Kolmogorov-Arnold network. The method comprises the following steps: extracting observation data and corresponding action data from a data source file for storing a robot movement track; the motion data is used as a diffusion model noise adding object, observation data is used as a condition to train a KAN strategy network, and the KAN strategy network is formed by fusing a Kolmogorov-Arnold network into a basic network architecture of a diffusion strategy in a modular form and is used for predicting noise to be added; the trained KAN strategy network is selected according to the field task scene requirement, predicted noise is generated according to actual observation data and action data of the robot, and the next action is generated by using the predicted noise through an inverse diffusion formula of the diffusion model. According to the method, an effective and smooth track can be generated, and the overall performance is remarkably improved.

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 discloses a shoe tree grabbing robot track planning system and method based on multi-modal sensing. The system comprises a shoe tree storage area, a mechanical arm grabbing assembly, a multi-modal sensing device, a posture recognition module, an interference sensing module, a grabbing point optimization module, a stable window generation module, a path planning module, a fine adjustment control module and a self-learning module. The system extracts the posture of the shoe tree by fusing visual images, structural sound waves and infrared contour information, then utilizes interference fringes to analyze and judge the material and friction characteristics, combines stability to judge and screen a grabbable target, and executes posture fine adjustment and fitting at the tail end of a clamping jaw through a touch sensing film. And the grabbing failure information is used for updating the strategy database to realize self-adaptive optimization of the grabbing strategy. The method is suitable for grabbing special-shaped objects and is high in robustness and success rate.

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 belongs to the technical field of industrial robots, and particularly discloses a six-axis robot trajectory planning method in a non-structural environment, which comprises the following steps: presetting starting points of an initial motion trajectory for a six-axis robot in a non-structural environment, performing interpolation between the starting points, and performing kinematics inversion solution of the six-axis robot, determining the position of a joint at each interpolation point; fitting and deriving all interpolation points by utilizing a quintic B-sample curve, and determining the speed, acceleration and impact of each joint; by taking minimization of total motion time, average acceleration and impact of joints as optimization objectives and taking acceleration and impact as constraint conditions, an objective function of trajectory planning of the six-axis robot in the non-structural environment is constructed; an improved multi-target grey wolf optimization algorithm is adopted to optimize the target function, and the position, the speed, the acceleration and the impact of the optimized six-axis robot joint are obtained; and determining the motion track of the six-axis robot based on the optimized position, speed, acceleration and impact of the joint of the six-axis robot.

The invention discloses a multi-robot probability trajectory generation method based on distributed model predictive control, and relates to the technical field of multi-robot systems, and the method constructs collision avoidance constraints based on a safety corridor with time perception. Uncertainty introduced by state estimation noise and motion interference is fully considered in construction of collision avoidance constraint, probability collision avoidance constraint is converted into deterministic constraint of mean value and covariance of robot states, and robustness of collision avoidance is ensured; meanwhile, in order to solve the problem of deadlock possibly occurring in a multi-robot system, a warning tape mechanism is introduced into probability constraint for avoiding collision and is combined with a right hand rule, and robot trajectory planning is dynamically adjusted; finally, the probabilistic collision constraint avoiding and deadlock preventing mechanisms are integrated into a distributed model predictive control framework, so that a locally optimal collision-free trajectory is generated.

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 provides a control operation method for a discharge robot of a rare earth metal electrolytic furnace, which comprises the following steps: the discharge robot acquires the electrolytic state of the electrolytic furnace through a visual sensor, and judges whether the electrolytic state reaches a discharge standard or not; after it is judged that the discharging standard is met, a discharging robot grabs a discharging tool, the discharging tool is preheated, and the discharging tool is moved to the discharging position to prepare for discharging; the discharging robot moves along the planned path, the contact force between the robot and the electrolytic furnace is monitored through a force sensing system, the track of the robot is corrected according to the contact force, and the discharging process is executed; after the discharging process is finished, whether discharging continues or not is judged, and if not, the process is finished. The molten metal discharging standard is judged in real time through the visual sensor, the position of the furnace opening is dynamically calibrated, tool deformation is monitored, flexible control over the tail end is achieved through the force sensor, damage to the furnace body in the digging operation is avoided, manual participation is reduced, and the operation efficiency is greatly improved.

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.

Provided is a generalization generation method of industrial robot pose trajectories supporting changeable operation path points, which relates to the field of robot trajectory planning. The method includes: acquiring a plurality of groups of robot end pose trajectories; aligning time steps of the pose trajectories via a multi-dimensional dynamic time warping algorithm; constructing a Gaussian mixture model of robot pose trajectories in combination with a variational Bayesian method and an attitude quaternion tangent space mapping method; calculating reference pose trajectory distribution via a Gaussian mixture regression method; performing kernelized representation on pose trajectory distribution, and solving an optimal hyperparameter of a kernel function by minimizing a root mean square error of a reproduced reference trajectory; and generating the robot end pose trajectories adapted to the operation path points by updating the reference pose trajectory distribution.

The invention discloses a robot trajectory planning method and device fusing physical constraints, equipment, a medium and a product, and relates to the technical field of trajectory planning, and the method comprises the steps: obtaining a current trajectory state quantity sequence of a robot; constructing an LTSM-RDNN network based on a long short-term memory network and a cyclic deep neural network, training the LTSM-RDNN network by using the optimal trajectory data set, and introducing trajectory state quantity constraint loss and controlled quantity constraint loss by using a PINN theory in the training process of the LTSM-RDNN network to obtain a trajectory prediction model based on the PINN network; based on the trajectory state quantity sequence, determining the optimal control quantity of the robot at the current time point by adopting a trajectory prediction model based on a PINN network; and the robot is controlled according to the optimal control quantity at the current time point, and real-time trajectory planning of the robot is achieved. Real-time trajectory planning can be carried out according to the physical constraint of the robot in the actual scene, and the feasibility of the trajectory is improved.