64804results about "Programme-controlled manipulator" patented technology

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

The invention discloses a system and a method for task planning of an intelligent robot with a body based on multi-dimensional situation awareness, and particularly relates to the technical field of task planning of the intelligent robot with the body, space-time alignment is carried out on asynchronous heterogeneous data generated by a multi-modal sensor channel, and cross-modal space-time features are extracted through a cross-modal feature fusion network; a fusion situation matrix is generated, dynamic causal modeling is used to update association strength among the multi-modal data, an anti-factual reasoning engine is used to identify and trace abnormities, and an abnormities traceability result is output; dynamically adjusting the reliability weight of each sensing channel through a multi-modal credibility evaluation model by using the fusion situation matrix and an abnormal traceability result; and on the basis of the reliability weight, inputting the fusion situation matrix into a real robot dynamic model and a digital twin virtual model, executing collaborative predictive control, and starting an adaptive rule evolution mechanism when a safety score is lower than a threshold value, thereby solving the problem of fusion matrix distortion in dynamic obstacle avoidance and precise grabbing tasks.

The invention discloses a manipulator grabbing planning system and method based on visual identification, and relates to the technical field of robots, the manipulator grabbing planning system comprises a visual perception module, a coordinate conversion module, a motion planning module, a control execution module, a tail end perception module and a system integration and communication module; the visual perception module realizes multi-view target detection, semantic segmentation and three-dimensional pose estimation through a binocular depth camera; the coordinate conversion module is used for converting a target pose under a camera coordinate system into a world coordinate under a mechanical arm base coordinate system; the motion planning module is responsible for generating a mechanical arm grabbing path, optimizing a strategy and supporting generalization migration of a multi-form mechanical arm; the control execution module drives the six-axis cooperative mechanical arm to complete the grabbing action, and vision-force mixed feedback closed-loop control is achieved. The tail end sensing module monitors the grabbing state in real time through a touch sensor and dynamically adjusts a grabbing strategy; and the system integration and communication module is used for realizing real-time data interaction and cooperative work among the modules of the system.

The invention discloses an industrial robot real-time adaptive control method and system based on digital twinning, and relates to the technical field of industrial robots. The digital twin engine module runs a high-fidelity dynamics simulation model and an environment interaction model, performs real-time state estimation, abnormal working condition recognition and twin parameter dynamic updating, is seamlessly integrated with the control execution module, and provides decision support with high robustness and high adaptability for an industrial scene; the adaptive control module performs online rolling optimization on a control strategy based on a deep reinforcement learning algorithm, generates joint space trajectory correction, tail end precision compensation and dynamic load adaptability optimal instructions, and realizes parameter adaptive setting through fuzzy logic or a neural network; and the fault diagnosis module performs multi-scale time sequence analysis by using an LSTM and convolutional neural network fusion model, detects position offset, moment sudden change or temperature overrun and other abnormalities, and triggers emergency shutdown, sound-light alarm and an adaptive recovery strategy.

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

The invention provides a robot alarm processing method and system based on a target detection algorithm and a cloud platform, and relates to the technical field of robot alarm processing, and the method comprises the steps: carrying out the detection of an abnormal target based on a convolutional neural network target detection model of the cloud platform in combination with a residual structure, a channel attention mechanism and an improved anchor-frame-free target detection algorithm, performing feature fusion through a feature fusion network guided by depth information to obtain accurate three-dimensional representation data, constructing a dynamic anomaly association graph by using an improved time sequence graph neural network, and generating a multi-level decision sequence in combination with an improved hierarchical reinforcement learning algorithm, and finally, collaborative task planning is carried out through a task decomposition network to obtain a task allocation scheme, and the robot carries out exception handling according to the optimal motion track, obstacle avoidance track data, real-time pose data, key operation sequence data and contact force feedback data.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling an agent interacting with an environment. In one aspect, a method comprises: receiving a user text sequence that characterizes a robotic task to be performed by the agent in the environment; converting the user text sequence into an input text sequence in a standardized format for a visual language understanding task; obtaining an observation image characterizing a state of the environment; processing (i) the input text sequence in the standardized format and (ii) the observation image using a policy neural network to generate a policy output that defines an action to be performed by the agent in response to the observation image; selecting an action to be performed by the agent using the policy output; and causing the agent to perform the selected action.

The invention discloses a target recognition and grabbing posture planning system for a collaborative robot, and belongs to the technical field of mechanical arms. According to material posture information provided by a sensing module, the optimal grabbing posture is intelligently matched from a preset grabbing strategy library by fusing mechanical arm movement track parameters and target material physical characteristics; the initial accuracy of the grabbing scheme is ensured, in the grabbing process, the camera continuously collects the material poses in real time, and the offset is accurately calculated by comparing the real-time poses with the expected poses. Once material deviation is detected, a visual and force closed-loop adjusting mechanism is triggered immediately, a force-position hybrid control model is utilized, position deviation is synchronously corrected, and grabbing force parameters are dynamically adjusted, so that the grabbing stability and precision are remarkably improved, the grabbing failure rate is effectively reduced, manual intervention and reset time are greatly shortened, and the grabbing efficiency is improved. Therefore, the production efficiency is comprehensively improved.

Disclosed in the present invention is an obstacle avoidance method for an industrial robot in a complex environment, comprising the following steps: S1, performing model simplification on an industrial robot in a working space, and simplifying an obstacle into an obstacle point cloud; S2, placing a depth camera outside a motion space of the industrial robot in an "eye-to-hand" arrangement mode; and S3, before the industrial robot moves, using a G-APF-RRT* algorithm to plan a feasible path in the working space. In the present invention, the calculation mode of the distance between the industrial robot and the obstacle is simple, the required time is short, virtual joint interpolation is used and a spherical bounding box is used at a joint to completely envelope the industrial robot so as to simplify a model, and distance calculation can be completed only by calculating the distance between the center of sphere of the limited spherical bounding box and each point in the obstacle point cloud.

The invention provides an industrial robot motion planning method based on a diffusion model. The method comprises the following steps: firstly, pre-training a point cloud encoder to compress and embed obstacle point cloud under task conditions into a potential space, and constructing a forward kinematics robot state coding algorithm and a path data collision detection module; then, task information is coded based on the pre-trained point cloud encoder, noise adding and noise reduction are carried out on task data based on a Transform diffusion model, and modeling is carried out on data probability distribution of a physical relationship between robot connecting rods; and finally, based on the trained neural network model, combining a small amount of motion feedback, generating an image from random Gaussian noise, and further improving the generation speed and the success rate. Finally, the neural network model can output an optimal path under a specific task condition.

The invention provides an industrial robot autonomous collaborative decision-making method and system based on multi-modal sensing and a medium, and belongs to the technical field of industrial robot intelligent control. The method comprises the steps of performing cross-modal space-time alignment to eliminate data space-time differences by collecting visual, tactile and auditory information, realizing multi-modal feature fusion in combination with a dynamic weight adjustment mechanism and an attention calculation model, and generating a joint decision strategy through a deep learning optimization model. The system dynamically allocates sensor weights according to task types, introduces a multi-objective optimization mechanism of energy consumption, precision and safety, and sets a fault-tolerant rule to automatically recover the weights or recalibrate the sensors. According to the method, the problems of rigid data fusion and single optimization dimension in traditional multi-modal decision making are solved, and the precision, the response speed and the environmental adaptability of collaborative operation of the industrial robot are remarkably improved.

The invention provides an intelligent inspection path planning method and system based on reinforcement learning, and relates to the technical field of path planning, comprising the steps of constructing a scene understanding model and a dynamic interaction model, and forming a cognitive enhanced digital twin system. Metaheuristic search and social learning mechanisms are fused, and the inspection efficiency, energy consumption and risk are balanced through a multi-target conflict coordinator. The group behavior emergence prediction model achieves dynamic prediction of a collaborative mode, the federated learning framework achieves aggregation of experience knowledge, a group evolution strategy with high adaptability is generated, and closed-loop optimization from task execution to cognitive enhancement is achieved through a robustness evaluation mechanism fed back in real time and a task allocation optimizer based on an attention mechanism. And the inspection efficiency and adaptability are improved.

The invention provides a multi-agent cooperative task inference and robot scheduling system, which comprises an interaction unit used for acquiring a natural language instruction issued by a user, sending the natural language instruction to an intelligent processing unit and interacting with the intelligent processing unit, receiving an execution result of a task and body state information of a robot, and sending the received execution result to the intelligent processing unit; sending a task missing information supplementing request when the task information is missing; the intelligent processing unit is used for dynamically sensing an environment and a task state according to a user instruction, a body state and environment information fed back by the robot and stored memory, and performing dynamic planning and decision making based on a sensing result and an inverse result of a task execution result to realize task reasoning and robot scheduling; and the robot cluster executes tasks according to the action sequence and feeds back own body state information and environment information. According to the invention, through a multi-agent cooperation framework based on a large model, the robot is endowed with dynamic reasoning and active interaction capabilities, and the requirements of efficient task reasoning and cooperative execution in a dynamic environment are met.

The invention provides an automatic walking and positioning method and system for a signal positioning and measuring robot in a complex environment, and relates to the field of technical positioning and measuring robots, and the method comprises the steps: obtaining an indoor three-dimensional map and multi-mode perception data, carrying out the semantic segmentation through a graph neural network, and achieving the environment matching through the combination of a point cloud registration technology; and generating robot motion track planning data. Multi-dimensional signal features are collected through a phased-array antenna system, and a signal feature distribution diagram is constructed in combination with a robot pose and an environment electromagnetic propagation model. A partitioning strategy and a multi-constraint optimization algorithm are adopted to calculate three-dimensional coordinates of a signal source, and finally, the signal source is accurately positioned through a depth calibration network.

The invention discloses a laser cutting machine track searching path planning optimization method and system, and relates to the technical field of laser cutting, the laser cutting machine track searching path planning optimization method comprises the following steps: analyzing geometrical characteristics of a to-be-cut workpiece, and planning an initial cutting path of a laser cutting machine in combination with processing drawing information; optimizing the initial cutting path by using a path optimization algorithm to generate a priority cutting path; a cutting head optimization algorithm is introduced, and a laser cutting head is controlled to avoid a repeated cutting area; cutting interference factors influencing the workpiece in the cutting task are analyzed, and the error influence range of each cutting interference factor on the workpiece is judged; and correcting the error of the laser cutting head in real time by using an error correction model based on the error influence range. According to the method, the propagation path and characteristics of the laser beam can be more accurately considered, the cutting path is optimized, and then the positioning and tracking accuracy of the laser cutting head is improved.

The invention discloses a double-mechanical-arm adaptive motor neural network optimization system, and relates to the technical field of mechanical arm motion control, and the system is characterized in that a feature fusion sensing module synchronizes multi-sensor data through an FPGA, combines technologies such as YOLOv5s, Poi ntNet + + and Doppler frequency shift, achieves precise extraction and fusion of obstacle 2D / 3D features, and generates a real-time environment state matrix; the kinematic chain prediction module quantifies the obstacle motion probability based on historical trajectory clustering and Bayesian prediction, constructs a collision risk field, and dynamically evaluates the collision risk in the future 2 seconds; the planning module searches a low-risk path by adopting an improved A algorithm, optimizes a joint movement track in combination with quadratic programming, embeds kinematics and dynamics constraints, and ensures the feasibility and safety of the path; in addition, a correction mechanism and an energy consumption optimization mechanism are introduced, motion parameters are dynamically adjusted according to the real-time state of the mechanical arm and environment interference, and the obstacle avoidance performance and the operation efficiency of the mechanical arm are further improved.

The invention provides a multi-target collaborative optimization control method and system for an igniter robot production line, and relates to the technical field of automatic control, and the method comprises the steps: collecting real-time operation parameters of the production line, carrying out the wavelet denoising, feature extraction and fusion, inputting a hierarchical asynchronous dominant actor commentator model, and in a strategy network layer, carrying out the wavelet denoising, feature extraction and fusion; a robot trajectory planning scheme is optimized and calculated through an improved near-end strategy, a production line speed control scheme is obtained through a layered dual-state action value function in combination with an adaptive Coubeck-Leibler divergence constraint, an energy consumption distribution scheme is obtained based on pseudo-parallel Monte Carlo tree search and an improved soft actor commentator algorithm, and an energy consumption control scheme is obtained. The value network layer evaluates an energy consumption distribution scheme and generates a combined control strategy, after the strategy is executed, a cycle converter model is improved to evaluate production indexes, model parameters are dynamically optimized, and multi-target collaborative optimization is achieved.

A humanoid robot includes an upper region includes a head and neck assembly having a neck portion and a head portion coupled to the neck portion. Said head portion includes: a frontal shell having a rear edge, a rear shell having a frontal edge, and an illumination assembly. The illumination assembly is configured to illuminate a region that: (i) extends between a rear edge of the frontal shell and an extent of the frontal edge of the rear shell, (ii) is positioned adjacent to the extent of the rear edge of the frontal shell, and (iii) is positioned adjacent to the extent of the frontal edge of the rear shell.

A folding robot for assisting with and performing household chores includes a head having a screen and a sensor, an elongate neck member, a torso, one or more arms connected to the torso, a riser member connected and rotatable with respect to the torso, one or more legs connected to the riser member by a hip joint, and a controller configured to send and receive data associated with the robot for completion of household tasks. Each arm includes a first arm member, a second arm member, a wrist member, and at least two fingers. Each leg includes a leg member, a first drive wheel disposed at a first end of the leg member coaxial with the hip joint, and a second drive wheel disposed at a second end of the leg member. The first drive wheel and second drive wheel are independently controllable for travel of the robot.

One or more data streams may be used for controlling multi-system interaction. For example, a data stream may be received, and a surgical option associated with a surgical instrument may be selected based on the data stream. A control signal associated with the surgical instrument may be generated based on the selected surgical option. A surgical device may receive an external data stream from a source external to the surgical system. The device may derive, based at least on the external data stream, decision contextual information. The device may select a surgical option associated with a surgical instrument based on the decision context information. The device may generate a visual indication of the decision context information associated with selecting the surgical option. The device may generate a control signal associated with the surgical instrument based on the selected surgical option.

Certain aspects of the present disclosure provide techniques and apparatus for processing data via a set of machine learning models to cause a device to perform a task. The method generally includes accessing data characterizing a physical environment in which a device is operating. A set of affordable actions is generated based on processing the data via a first set of machine learning models. A selected action to be performed in the physical environment is generated via a second set of machine learning models based on the set of affordable actions and a task. The device is then caused to execute the first selected action.

The invention provides an intelligent robot inspection system in a power distribution and transformation intelligent auxiliary system, and relates to the technical field of intelligent sensing and control, and the system comprises a real-time operation collection module which is used for planning a global path of an intelligent inspection robot, generating an initial inspection path, carrying out real-time operation collection, and obtaining a multi-modal detection data set; the fault feature recognition module is used for performing fault feature recognition, generating an abnormal feature set and constructing a dynamic risk map; the simulation maintenance feedback module is used for carrying out simulation maintenance feedback according to the inspection correction path in combination with the equipment maintenance suggestion and generating a maintenance feedback result; and the inspection control module is used for generating an optimized inspection path to perform inspection control on the intelligent inspection robot. According to the invention, the technical problem that the routing inspection efficiency and the fault diagnosis accuracy are influenced because routing inspection path planning often depends on a preset environment map and the intelligent routing inspection robot cannot automatically adjust the routing inspection path according to the change of the equipment state in the prior art is solved.

A method for terrain and constraint planning a step plan includes receiving, at data processing hardware of a robot, image data of an environment about the robot from at least one image sensor. The robot includes a body and legs. The method also includes generating, by the data processing hardware, a body-obstacle map, a ground height map, and a step-obstacle map based on the image data and generating, by the data processing hardware, a body path for movement of the body of the robot while maneuvering in the environment based on the body-obstacle map. The method also includes generating, by the data processing hardware, a step path for the legs of the robot while maneuvering in the environment based on the body path, the body-obstacle map, the ground height map, and the step-obstacle map.

The invention relates to the technical field of robot control, in particular to a multi-degree-of-freedom mechanical arm obstacle avoidance control method based on machine vision, which comprises the following steps: acquiring 360-degree panoramic image data around a mechanical arm through a multi-camera system, and combining two-dimensional and three-dimensional information acquired by an RGB camera and a depth camera to construct an environment model. A pre-trained convolutional neural network model is used for identifying fixed and moving obstacles in an image, a Kalman filter is combined to predict the motion trail of the moving obstacle, an improved fast expansion random tree algorithm is used for path planning, it is ensured that the efficient and accurate obstacle avoidance capacity of the mechanical arm is kept in a dynamic environment, and the obstacle avoidance efficiency of the mechanical arm is improved. The motion state of the mechanical arm is controlled and monitored through real-time feedback, and deviation caused by inertia or errors is avoided. The dynamic adaptive capacity and the safe operation performance of the mechanical arm in the complex environment can be improved, and the mechanical arm is widely applied to robots in the fields of industry, medical treatment and the like.

The invention discloses a robot control method and device based on multi-modal data fusion, and the method comprises the steps: responding to a control instruction inputted by a user, receiving and preprocessing sensing information fed back by a plurality of types of sensors preset on a robot, and obtaining multi-modal data; dynamically adjusting the weight of each modal data, and extracting the feature vector of each modal data; according to the dynamically adjusted weight of each modal data, fusing the extracted feature vectors of each modal data to generate a comprehensive feature vector for representing the comprehensive state of the current environment; according to the comprehensive feature vector, the control instruction is analyzed through a preset large language model, and a final decision result of the robot is obtained; and according to the final decision result, calling and executing an instruction set required to be executed by the robot so as to drive the robot to complete related actions. Therefore, by adopting the embodiment of the invention, the robot can effectively process and utilize information from different modes to the maximum extent, and better decision and performance of the robot are realized.

The invention relates to the technical field of intelligent assessment and decision making, in particular to a robot dynamic risk assessment and decision making system and method based on multi-modal perception, and the system comprises a multi-modal sensor module which is used for collecting environment vision, acoustics, mechanics and position data in real time; the edge calculation unit is used for carrying out space-time alignment and feature fusion on the sensor data; the dynamic risk assessment model is used for integrating the environment uncertainty quantification module and the robot state prediction module based on a reinforcement learning framework; the decision execution interface is used for outputting a risk level and obstacle avoidance, speed reduction and shutdown instructions; by integrating visual, acoustic, mechanical and position multi-source sensor data and the like, the system can comprehensively capture various risk factors in a complex dynamic environment, so that the defect that a traditional single sensor system is insufficient in sensing dimension is overcome, and the system is particularly suitable for terrains and weather conditions with variable regions.

A method of footstep contact detection includes receiving joint dynamics data for a swing phase of a swing leg of the robot, receiving odometry data indicative of a pose of the robot, determining whether an impact on the swing leg is indicative of a touchdown of the swing leg based on the joint dynamics data and an amount of completion of the swing phase, and determining when the impact on the swing leg is not indicative of the touchdown of the swing leg, a cause of the impact based on the joint dynamics data and the odometry data.

The invention discloses a heterogeneous double-mechanical-arm tail end pose mapping method, system and device and a storage medium, and the method comprises the following steps: S1, obtaining the angular velocity, position and tail end pose information of each joint of a main mechanical arm, and calculating the spatial position and pose of the tail end of the main mechanical arm; s2, motion trail data in the teaching process of the main mechanical arm is collected, a long-short-term memory network model is established, the pose of the future time step length is predicted, and the motion trail of the auxiliary mechanical arm is planned; s3, data information is transmitted, and the problem of communication delay is solved; s4, the pose information of the tail end of the main mechanical arm is solved, the joint angle is adjusted, and the error is adjusted in combination with proportional-integral-differential control; s5, the position and speed signals are filtered, a mechanical arm dynamic model is established, an adaptive control law is designed, and a trajectory smoothing algorithm is adopted; and S6, establishing a virtual obstacle map, and limiting a safe working space. According to the method, comprehensive and accurate mapping and prediction of the tail end poses of the heterogeneous double mechanical arms are achieved.

The invention discloses a portal crane grab bucket control system and method based on multi-sensor fusion, and particularly relates to the technical field of portal crane grab bucket control. Image acquisition equipment and multiple types of sensors are arranged in a portal crane operation area, a multi-dimensional input data set under a unified time sequence is constructed, an optimized visual detection model and an attention mechanism are combined, high-precision recognition and abnormity judgment of the grab bucket state are achieved, and the system not only can automatically plan a grabbing path and a posture adjusting instruction, but also can automatically adjust the grabbing posture of the grab bucket. And control parameters can be dynamically optimized according to the grabbing effect and pose feedback, the grab bucket recognition accuracy, the control stability and the system intelligence level are remarkably improved, and particularly higher robustness and reliability are shown in the small-size, complex-pose or multi-target operation environment.