860 results about "Robotics" patented technology

The invention discloses a manipulator grabbing method based on deep learning target detection and image segmentation, and relates to the technical field of artificial intelligence and robotics.The manipulator grabbing method comprises the following steps that a scene image to be processed is collected, the image quality is improved through the multi-light-source fusion image enhancement technology, and recognition errors caused by uneven illumination are reduced; and inputting the enhanced image to a pre-trained deep learning model, executing a target detection task, and outputting an initial bounding box and a category label of the target object. According to the method, through multi-light-source image enhancement and high-precision image segmentation, the accuracy of target recognition and contour extraction is remarkably improved, and the capture failure rate caused by image misjudgment is reduced. And meanwhile, geometric consistency verification and a multi-factor grabbing scoring mechanism are introduced, dynamic screening and collision pre-detection are conducted on the paths, the grabbing stability and safety of the mechanical arm in the complex environment are effectively guaranteed, and the intelligence and robustness of the whole system are remarkably improved.

The invention discloses a control method and equipment for an intelligent robot with a body and a storage medium, and belongs to the technical field of robots. The method comprises the steps of receiving a task instruction and environment perception data, performing fusion processing on the task instruction and the environment perception data, generating task semantic information associated with the task instruction, analyzing the task semantic information through an implicit planner, generating an implicit action mark, and based on the implicit action mark, generating an implicit action. And decomposing a to-be-executed task corresponding to the task instruction into a plurality of task sub-targets layer by layer, generating an action instruction sequence based on the task sub-targets, and executing a control action corresponding to the action instruction sequence. According to the method, based on the constraint of the implicit action mark and the action instruction sequence, the robot with the body can flexibly respond to environment parameter changes such as object position deviation or new task requirements in the task execution process, the coupling degree of the action instruction of the robot with the body and the predefined scene is reduced, and high stability of task execution is ensured.

The invention relates to the field of agricultural robots, and discloses an agricultural spraying robot system with visual navigation, and the system comprises a multi-mode sensing module, a dynamic environment modeling module, a time sequence prediction module, a multi-target path planning module, a spraying control module, and a hardware acceleration unit. The method comprises the steps that a dynamic semantic map is constructed through multi-modal perception, agricultural semantics and an attention mechanism are fused to predict an obstacle trajectory, a safe path is generated in combination with multi-objective optimization, the spraying amount is adjusted based on crop density, and real-time operation is ensured by means of hardware acceleration. According to the method, a dynamic semantic map is constructed through multi-modal perception, agricultural semantics is combined to enhance trajectory prediction, a safe path is generated by utilizing multi-objective optimization, the spraying amount is adaptively adjusted according to the crop density, and real-time control is realized by adopting heterogeneous hardware acceleration; the defects of a traditional agricultural robot in the aspects of navigation precision, obstacle avoidance safety, resource utilization and response speed are overcome, and the intelligent level of complex farmland operation is improved.

In various examples, a technique for generating a path between a current location and a target waypoint is disclosed that includes receiving a route plan that is associated with a plurality of waypoints representing locations in a physical environment. The technique also includes identifying a search space that includes the route plan, and identifying a target waypoint of the plurality of waypoints—the target waypoint being in a portion of the search space between a current location of a mobile robot and an end waypoint of the route plan. A path between the current location of the mobile robot and the target waypoint may then be generated.

The invention relates to the field of robots, and discloses a robot ordering method and system, and the method comprises the steps: obtaining a depth map and a color map of a current scene; obtaining a detection frame of a target object, and cutting out three-dimensional point cloud data of the corresponding object from the depth map according to a detection result; reasoning by taking the three-dimensional point cloud data as input of a reasoning model, and predicting candidate grabbing poses; and according to the predicted candidate grabbing poses, trajectory planning and motion execution are carried out. Reasonable action information can be generated according to information provided by the visual language model in combination with specific task requirements, and therefore control over the mechanical arm is achieved.

The invention discloses a control method, system and equipment of a remote rehabilitation robot, a medium and a product, and belongs to the technical field of robots. The method comprises the steps of obtaining multi-modal data of a patient end, obtaining a time delay prediction value through prediction of a long short-term memory network model, dynamically adjusting admittance parameters of an admittance model according to the multi-modal data and the time delay prediction value, and finally controlling the remote rehabilitation robot according to the dynamically adjusted admittance parameters. According to the method, the admittance parameters of the admittance model are dynamically adjusted in real time based on the time delay predicted value predicted by the long-short-term memory network model and the electromyographic signals of the patient in the multi-modal number, dual adaptation of the network state and the physiological features of the patient is achieved, the admittance parameters are dynamically adjusted through the electromyographic signals, and the change requirement of the complete rehabilitation cycle of the patient is met; admittance parameters are dynamically adjusted through the time delay prediction value reflecting the network time delay state, and the time delay disturbance requirement in the remote communication environment is met.

Conventionally, the road was visually inspected from patrols etc., and it was determined whether to respond to road disasters in the event of a disaster or the like by observing the actual road conditions. However, there was a problem that it took time and effort to make a judgment only by visual patrol, and it was difficult to respond quickly. 【Solution means】 An information acquisition unit that acquires information related to disasters, an analysis unit that analyzes two or more pieces of information acquired by the information acquisition unit, and a determination unit that determines the necessity of road disaster response from part or all of the results analyzed by the analysis unit, A road opening unit that registers a pre-formulated road opening plan and formulates an implementation plan for road opening, and a robotics unit in which a disaster response robot performs a road opening operation using artificial intelligence analysis and robotics technology, A road disaster response support system, program, and road management method, characterized by comprising:

The invention provides a six-degree-of-freedom flexible adaptive integrated large-scale connector automatic docking method based on vision and force feedback. According to the method, a parallel structure is used as a design basis, and the difficulty of automatic docking is solved by combining an active compliance thought of taking a force sensor as feedback and a thought of taking a passive compliance docking mechanism as mechanism passive compliance docking. A set of active and passive compliant control integrated large-scale connector automatic docking system based on combination of vision and force feedback is designed. As a research focus in the robotics field, the active compliance control technology can be deployed in numerous accurate docking application scenes depending on force feedback, the active compliance docking strategy is formulated for the design difficulty of the active compliance docking control, and the strategy is simple, fast, low in cost and high in reliability. The anti-interference performance and the reliability of the control system are greatly improved, the system is suitable for automatic docking occasions in the industrial automation field in some complex environments, and contributions are made to further research and expansion of the technology. The high-precision flexible butt joint technology is applied more and more widely nowadays with the continuous development of science and technology, compared with a traditional butt joint device, the automatic butt joint device designed by the invention is higher in assembly efficiency and precision and higher in intelligent degree, damage to all paths of plug-in devices is smaller, meanwhile, economic losses can be further reduced, and the automatic butt joint device is suitable for popularization and application. And human resource cost is saved.

Disclosed is a robotic system and method designed for the verification and validation of smart connected Human-Machine Interface (HMI) devices. The robotic system integrates a multi-finger modular robotic module, a haptic module, a finger actuator, vision cameras, vision lights, a finger rotary module, a Cartesian robotic arm, an onboard user interface, and an L-shaped door. The method involves replicating human finger interactions, simulating touch sensations on a device under test (DUT), ensuring continuous and reliable operation, utilizing vision cameras for navigation and verification, optimizing vision with controlled lighting, positioning cameras strategically, executing various tasks with a robotic arm, and facilitating user interaction through an onboard interface.

Collision detection useful in motion planning for robotics advantageously represents planned motions of each of a plurality of robots as obstacles when performing motion planning for any given robot in the plurality of robots that operate in a shared workspace, including taking into account the planned motions during collision assessment. Edges of a motion planning graph are assigned cost values, based at least in part on the collision assessment. Obstacles may be pruned as corresponding motions are completed. Motion planning requests may be queued, and some robots skipped, for example in response to an error or blocked condition.

The present invention generally relates to the field of robotics, and, more particularly, to a method and system for task anticipation by integrating large language models and classical planning. Conventional methods for task anticipating use data-driven deep network architectures and Large Language Models (LLMs) for task estimation but they do so at the level of high-level tasks and require a large number of training examples. Thus, embodiments of present disclosure provide a method and system for task anticipation by integrating large language models and classical planning. The disclosed method and system leverages the generic knowledge of LLMs through a small number of prompts to perform high-level task anticipation, using the anticipated tasks as joint goals in a classical planning system to compute a sequence of finer granularity actions that jointly achieve these goals.

The invention discloses an automatic foreign matter selecting system based on cooperation of vision and a manipulator, and belongs to the technical field of automatic control. Pickled vegetable products are conveyed to the unfolding platform through the lifting buckets, and a plurality of non-overlapped pickled vegetable product areas are formed; preliminary image acquisition is carried out in the primary visual area, grid division is carried out, and foreign matter confidence is calculated; a foreign matter confidence coefficient threshold value is preset to recognize a risky foreign matter pickled vegetable product area grid, and the risky foreign matter pickled vegetable product area grid is transmitted to a primary removal area; after primary foreign matter removal is completed, the pickled vegetable products are turned over and conveyed to a secondary visual area, and the secondary visual area is used for secondarily judging whether foreign matter exists in the turned pickled vegetable products or not. An intelligent judgment mechanism of visual recognition and an accurate physical execution mechanism of the mechanical arm are fused, the stability and the automation degree of system operation are improved, and the quality safety guarantee level in the food processing process is remarkably improved.

The invention provides a self-adaptive multi-modal odometer dynamic fusion method which specifically comprises the following steps: extracting edge and plane features of a laser point cloud, and generating a laser pose factor by utilizing iterative motion compensation and residual optimization; multi-scale angular point features are extracted from an original image, and visual pose factors are generated in combination with optical flow tracking and local light beam adjustment. And a dynamic factor graph is constructed, laser and visual pose factors are used as constraints, an iSAM2 engine is used for optimization solution, and a global pose is obtained. Calculating the residual mean value and variance of Lagrange factors in real time, dynamically checking the state of the sensor based on chi-square test, triggering failure response during continuous overrun, gradually removing abnormal factors, keeping historical data, aligning timestamps through SE (3) manifold interpolation, and optimizing external parameter calibration. And after the sensor is recovered, factors are re-introduced through progressive weight fusion, and a sliding window mechanism is adopted to smooth the track. The method provides a robust and high-precision fusion algorithm for the field of robots and automatic driving.

Imitation learning, or artificial intelligence-based learning from demonstration, aims to acquire an agent policy by observing and mimicking the behavior demonstrated in expert demonstrations. Imitation learning can be used to generate reliable and robust learned policies in a variety of tasks involving sequential decision-making, such as autonomous driving and robotics tasks. However, current imitation learning solutions are limited in their ability to generalize states or goals unseen from the expert's demonstrations. The present disclosure integrates a diffusion model into generative adversarial imitation learning, which, in terms of prior solutions, can provide superior performance in generalizing to states or goals unseen from the expert's demonstrations, provide data efficiency for varying the amounts of available expert data, and capture more robust and smoother rewards.

The invention discloses a multi-modal interaction method and interaction system applied to an intelligent robot. The method comprises the following steps: collecting a scene image and processing the scene image into a three-dimensional point cloud and a two-dimensional texture feature; the Gemini Robotics-ER model is used for extracting features, and the vision-language-action model is used for analyzing a language instruction into a sequence capable of being recognized by a machine; and fusing the features to generate an interactive decision matrix, planning a trajectory, calculating kinetic parameters, driving the robot to execute actions and feeding back in real time. The system comprises a multispectral visual information acquisition and preprocessing unit, a Gemini Robotics-ER model processing unit, a natural language instruction analysis unit, a vision-language-action cooperative processing unit, a trajectory planning and dynamics calculation unit and a motion control and feedback unit, and all the units work cooperatively. According to the method and the system, through multi-modal fusion and closed-loop control, interaction accuracy and real-time performance are improved, and industrial scene requirements are met.

In various examples, a technique for generating simulation data includes generating, via one or more simulations, simulation data associated with operation of a first machine in an environment. The technique also includes determining a command to the first machine based at least on the simulation data and a goal associated with the first machine and updating the simulation data based at least on the command. The technique further includes storing the simulation data, the command, and the updated simulation data in one or more data records, and causing a second machine to perform one or more actions based at least on the one or more data records.

The invention provides a data acquisition system and a data processing method, and belongs to the technical field of robots. The handheld unit comprises a handheld grip and a tool fixture, the tool fixture is connected with the handheld grip, an operator controls the tool fixture to make contact with and interact with an operation object through the handheld grip, natural operation characteristics of a human body are met, and the skill teaching efficiency can be improved. A data measuring device is arranged and used for measuring the position, posture, contact force, torque information and the like in the operation process, and the problem that traditional position teaching lacks force feedback and the problem of environmental sensitivity existing in visual motion capture can be solved. And finally, the upper computer is used for carrying out unified coordinate system conversion on the measured data, and mapping errors caused by hand-tool-environment coordinate system separation in a traditional method can be eliminated.

The invention provides a method for detecting the position of a multi-code-channel absolute encoder. The multi-code-channel absolute encoder position detection method is characterized in that an incremental signal (1), a single-period subdivision position (2), an absolute position (3) and an absolute subdivision position (4) are included, wherein the absolute position (3) is composed of a credible interval (301) and an uncertain interval (302). Firstly, an incremental signal (1) is composed of periodic AB signals detected by an encoder, then a periodic incremental subdivision phase signal phi is calculated and subdivided from the AB periodic signals, a single-period subdivision position (2) is obtained through calculation, and meanwhile, the encoder calculates an absolute position (3) of a system at the moment from an absolute code channel; a trusted interval (301) of the absolute position (3) is determined from the incremental phase signal phi, and finally an absolute subdivision position (4) is obtained after fusion correction. The method can be used for high-precision absolute position measurement, and can be widely applied to the fields of industrial automation, robot technology, aerospace, precision measurement, positioning control and the like.

The invention belongs to the field of robots, and particularly discloses a mechanical arm man-machine cooperation self-adaptive admittance control method and device, a terminal and a medium, a man-machine interaction model omitting virtual stiffness is constructed, and admittance parameters in the man-machine interaction model comprise virtual inertia and virtual damping; designing a stability observer of the physical man-machine interaction system, monitoring an external force applied by an operator by the stability observer, and performing spectral analysis on a force signal to obtain a system instability index; constructing an admittance parameter adaptive controller through a system instability index, wherein the admittance parameter adaptive controller comprises a virtual inertia adaptive controller and a virtual damping adaptive controller; and the virtual inertia and the virtual damping are adjusted based on the admittance parameter self-adaptive controller, so that the physical man-machine interaction system returns to a stable state. The method realizes self-adaptive adjustment of controller parameters, adapts to a complex environment, and keeps stability of physical man-machine interaction.

In various examples, systems and methods described herein may cause a machine to establish a network connection with a network access device based at least on map data indicating to use the network access device for network connectivity at a location of the machine. In some examples, the map data may be generated based at least on one or more network performance scores associated with one or more network access devices disposed in the environment. In some instances, the network performance score(s) may be determined based at least on one or more wireless network signals transmitted by the network access device(s) and obtained using the machine and / or another machine. The network performance score(s) may, in some examples, be indicative of a signal strength associated with the network access device(s) at the location of the machine.

The invention relates to the technical field of robots, and discloses a dexterous hand and a robotic.The dexterous hand comprises a palm base, a multi-connecting-rod mechanism, at least one connecting frame and at least one first finger assembly, the multi-connecting-rod mechanism is arranged on the palm base, and the first finger assemblies are correspondingly arranged on the connecting frames; the first finger assembly comprises a telescopic driving mechanism, a thumb assembly and a side swing assembly, the palm near end of the thumb assembly is rotatably connected with the side swing assembly around a first axis, the palm far end of the connecting frame is rotatably connected with the side swing assembly around a second axis, and the first axis and the second axis are spatially crossed. The connecting frame is arranged on the multi-connecting-rod mechanism, the telescopic driving mechanism is arranged on the connecting frame, lateral swinging of the thumb assembly can be achieved through cooperation of the telescopic driving mechanism and the lateral swinging assembly, then the dexterous hand with the multi-connecting-rod mechanism can achieve finger lateral swinging movement, the operation flexibility of the dexterous hand is improved, and the application range of the dexterous hand is widened.

The invention discloses a robot system with tactile perception, and belongs to the technical field of tactile robots. The problem that an existing robot is poor in environment pressure sensing capacity is solved. Comprising a head module, an integrated vision-inertial navigation sensing module and a head controller, two side surfaces are provided with a pair of rotatable connecting ends, and the outer surface is provided with a tactile sensor; the head controller transmits data to the upper computer through the communication module; the driving body module comprises a plurality of modularized joints which are connected end to end, touch sensors are arranged on the two side faces of each modularized joint, and the adjacent modularized joints are orthogonally connected; the modularized joint is integrated with a joint controller and a joint steering engine; the tail module integrates a tail controller and a tail steering engine, and tactile sensors are arranged on the two side faces of the tail module. The tail controller carries out data transmission with the head controller through the adjacent previous joint controller in sequence, and the head controller receives a control instruction of the upper computer and carries out data transmission with all other controllers. According to the invention, the environmental pressure can be sensed in all directions.

The invention belongs to the technical field of robots, and particularly relates to a global trajectory strategy reinforcement learning method and a robot control system, and the method comprises the following specific steps: S1, environment modeling and state definition: firstly collecting environment data, then defining a state space of a robot according to the collected environment information, the position, the posture, the sensor data and the task target information of the robot are coded and converted into a state vector which can be processed by an algorithm; s2, setting an action space: determining all actions which can be executed by the robot in a scene, and discretizing or continuously processing the actions to form the action space; according to the method, the path planning response speed can be increased, collision can be effectively avoided, and the adaptive capacity of the robot in a dynamic environment can be greatly enhanced.

The invention relates to a fruit and vegetable picking robot system based on data enhancement and identification precision optimization, and belongs to the field of robots. The system is composed of a visual identification module, a mechanical arm module, a control module and a moving module, and fruits and vegetables are identified and positioned in real time by adopting a YOLOv5 target detection algorithm. The visual module collects images through a monocular camera, and accurately detects the types and positions of fruits and vegetables by using a YOLOv5 algorithm; the control module coordinates the mechanical arm to execute the picking action according to the recognition result, and plans the moving path of the robot to adapt to the complex farmland environment; and the mechanical arm module adopts a multi-degree-of-freedom design and is provided with a flexible clamping jaw, so that efficient picking operation can be completed on the premise of protecting fruits and vegetables. In addition, the system also supports the dynamic updating of the model, and the recognition performance is continuously optimized by adding new fruit and vegetable data. The system can meet the requirements of different crops and environments while improving the picking efficiency and reducing the labor cost, and has wide application prospects.

The invention relates to a monocular vision-based map construction method, system and equipment. The monocular vision-based map construction method comprises the following steps of: acquiring a current frame image, a key frame image and a sparse point cloud map; extracting image information of the current frame image, and determining a first camera attitude; determining a prior depth map of the current frame image on the key frame image; constructing a first Gaussian model according to the first camera attitude, the image information, the sparse point cloud map, the prior depth map and the current frame image, and optimizing the first camera attitude to obtain a second camera attitude; wherein the Gaussian model comprises Gaussian distribution of a plurality of scenes; and obtaining a second Gaussian model according to the sparse point cloud map, the prior depth map, the image information and the second camera attitude, and adding the second Gaussian model to the Gaussian map according to the second camera attitude. The method can improve the positioning and map building precision, and is applied to the technical field of computer vision and robots.

The utility model relates to the technical field of robots, and provides an omni-directional mobile intelligent robot comprising a multi-axis mechanical arm so as to improve the education popularization range of the omni-directional mobile robot comprising the mechanical arm. The three groups of steering wheels are mounted at the bottom of the mainframe box, the control panel and the battery are arranged at the top of the mainframe box, the mechanical arm is mounted at the front end of the mainframe box, and the storage device is arranged between the battery and the mechanical arm. A groove is formed in the front end of the main machine box, the bottom of the mechanical arm is installed in the groove, the mechanical arm comprises a plurality of steering engines, a lower arm connecting rod, an upper arm connecting rod and a lifting mechanism, multi-degree-of-freedom movement of the mechanical arm can be achieved, the coverage range of the tail end of the mechanical arm is enlarged, and meanwhile the tail end of the mechanical arm is provided with a clamping device to complete object grabbing operation. Mecanum wheels are adopted as the three sets of steering wheels, the steering wheels are arranged at equal intervals of 120 degrees, 360-degree steering and movement of the robot can be achieved, various sensors are integrated, and the obstacle avoidance and tracking functions can be achieved; the method is suitable for large-scale popularization education.

An autonomous warehouse robotics system integrates a multi-sensor platform, adaptive payload handling, dynamic task reallocation, advanced navigation, energy management, and comprehensive safety features into one mobile robot chassis. The system utilizes LiDAR, stereo vision, ultrasonic sensors, mmWave radar, thermal cameras, and event cameras to perform complete environmental sensing and obstacle detection. Sensor fusion combines adaptive weighting, multi-modal data integration, and statistical filtering to create high-confidence maps for reactive path planning and collision avoidance. The robot's payload system features machine vision for item recognition, telescopic lifts, variable-width grippers, and real-time toolhead verification to handle a variety of goods. Fleet management is achieved through dynamic task reallocation that considers robot location, battery level, and operational delays, all coordinated by a cross-platform middleware architecture that ensures standardized communication, remote monitoring, and over-the-air updates among diverse robot brands. Energy management optimizes power usage via predictive routing, autonomous return-to-charge, and auction-based scheduling. Safety is maintained through proximity detection, behavior-based intervention, and human-robot cohabitation protocols while advanced localization is enhanced by fusing ultra-wideband positioning with visual landmark alignment, inertial sensing, and machine learning to deliver high accuracy in non-line-of-sight conditions. An onboard edge AI module further refines navigation and task prioritization through neural network inference, ensuring robust, adaptive operation in dynamic, unstructured warehouse environments.

A system for autonomous or semi-autonomous operation of a vehicle is disclosed. The system includes a machine automation portal (MAP) application configured to enable a computing device to (a) display a map of a work site and (b) provide a graphical user interface that enables a user to (i) define a boundary of an autonomous operating zone on the map and (ii) define a boundary of one or more exclusion zones. The system also includes a robotics processing unit configured to (a) receive the boundary of the autonomous operating zone and the boundary of each exclusion zone from the computing device, (b) generate a planned command path that the vehicle will travel to perform a task within the autonomous operating zone while avoiding each exclusion zone, and (c) control operation of the vehicle so that the vehicle travels the planned command path to perform the task.

The invention provides a continuum mechanical arm motion control method based on deep reinforcement learning. The method relates to the technical field of robots, and comprises the following steps: collecting multi-source information of a mechanical arm, and fusing the multi-source information of the mechanical arm to generate a state vector reflecting current kinematics and dynamics characteristics; and inputting the state vector into a deep reinforcement learning strategy, and outputting a continuous candidate control instruction matched with the state vector. According to the continuum mechanical arm motion control method based on deep reinforcement learning, the high dependence of a traditional control method on a kinetic model and parameter calibration can be effectively overcome, and accurate control over the mechanical arm under the conditions of external disturbance and structural parameter drift is achieved. By fusing multi-source information and dynamically adjusting the control strategy, the control instruction can be corrected in real time and kept stable under different physical conditions, the adaptability and the control effect of the mechanical arm in a complex environment are improved, and safety and execution precision are ensured.

The invention discloses an interaction strategy generation method based on user data, cloud equipment and robot equipment, and relates to the technical field of robots, and the method comprises the steps: receiving target user data sent by a robot end, and determining the target user data of the target user data; performing emotion analysis on the target user data through a preset analysis model to obtain an emotion state of the target user; a target portrait is constructed according to historical user data of the target user, and the historical user data comprises historically received target user data; generating a target interaction strategy based on the target portrait and the emotional state; the target interaction strategy is sent to the robot end, and the robot end is used for receiving the target interaction strategy sent by the cloud end and executing the target interaction strategy. The emotional connection and accompanying effect between the user and the robot is enhanced.