32 results about "Robot hardware" patented technology

The invention provides a control method, which is applied to an artificial intelligence experiment system, at least comprises a robot hardware platform, a controller, a wheeled robot chassis and an industrial mechanical arm, and the method comprises the following steps: starting an operation system and executing hardware self-inspection; loading a large language model service and a traditional AI model, wherein the traditional AI model comprises a speech recognition model, a target detection model and a semantic analysis model; a natural language instruction of a user is received, voice is converted into a structured text through a voice recognition model, an instruction intention is analyzed through a large language model and a semantic analysis model, robot control parameters are generated, and a multi-modal interaction control signal is obtained; based on the multi-mode interaction control signal, a camera is called to collect image data, the position and category of a target object to be grabbed by the robot are recognized through a target detection model, the moving path of the robot and the grabbing track of the mechanical arm are calculated in combination with laser radar data, and the wheeled robot chassis and the industrial mechanical arm are controlled to execute coordinated actions.

The invention discloses a fire-fighting reconnaissance robot system hardware reliability evaluation method, which comprises the steps of collecting hardware performance data and key environment stress data of a fire-fighting reconnaissance robot, and constructing basic evaluation data; extracting a core evaluation factor evolved along with the change of the fire scene condition, and determining reliability index data according to the core evaluation factor; determining a subjective weight and an objective weight according to the real-time reliability index data in the current environment; performing dynamic adjustment on the subjective weight and the objective weight of the fire-fighting reconnaissance robot index to obtain a self-adaptive comprehensive weight vector; and constructing a fuzzy relation matrix by using the core evaluation factor and the comprehensive weight vector, and performing synthesis operation to obtain a real-time updated system reliability level. According to the reliability evaluation method, real-time, dynamic and accurate evaluation of the reliability of the fire-fighting reconnaissance robot hardware can be realized, and intelligent decision support is provided for dynamic task decision and imminent maintenance of the robot.

The invention relates to the fields of robot technologies, intelligent hardware and the like, and aims to provide a design scheme of the modular hardware architecture and the hot plug functional component. The core design content is as follows: a modular hardware main architecture adopts a layered and partitioned design, comprises a core control module, a sensing module and the like, and realizes plug and play through a standardized interface; according to the hot plug function assembly, a camera, an emotion sensor and other modules support safe plugging in the operation process, and automatic recognition and drive loading can be achieved. And module management and monitoring: monitoring the state of the module in real time, and supporting remote upgrading, fault isolation and hot backup. And safety protection: multi-stage electrical protection, a hot plug protection circuit and a physical locking mechanism are provided. The method has the advantages that the robot hardware flexibility, maintainability and expansibility are improved, the upgrading and maintaining difficulty is lowered, the downtime is shortened, application of the robot in multiple fields is promoted, the cost is lowered, and the life cycle is prolonged.

The invention discloses a hidden agricultural product detection method based on a progressive aggregation network, and belongs to the technical field of computer vision and agricultural robots. Aiming at the problems of low detection precision and poor robustness of hidden agricultural products in a complex agricultural environment, the method comprises the following steps: firstly, constructing a hidden agricultural product visual detection training sample data set suitable for a complex agricultural scene; the invention further provides an end-to-end real-time encoder-decoder structure, namely a progressive aggregation network, the network comprises an image pyramid encoder, a progressive internal aggregation module and a progressive guide aggregation decoder, and multi-level feature extraction and fusion are achieved through cooperation of the image pyramid encoder, the progressive internal aggregation module and the progressive guide aggregation decoder. And then, training the network model on a GPU cluster, and finally completing deployment and application on a picking robot hardware platform. The method is high in systematicness and detection efficiency and can be directly integrated in an existing agricultural product picking robot system, and the detection precision and picking operation efficiency of hidden agricultural products are effectively improved.

The invention provides a robot task scheduling method and device, equipment, a medium and a product. The method comprises the steps of obtaining a target task; constructing a robot atomic action library for a robot hardware module of the target robot according to the target task to form an atomic action layer; obtaining a logic step layer according to the atomic action and predefined action logic; according to the target task and the logic steps, obtaining an executable task layer of the target robot for the target task; constructing a multi-level priority queue scheduling structure according to the atomic action layer, the logic step layer, the executable task layer and a preset priority rule; and dynamically scheduling the robot according to the multi-level priority queue scheduling structure. A multi-level priority queue structure is constructed, a task dynamic scheduling mechanism is established, planning and scheduling of the robot are carried out at a finer-grained atomic action level, and execution coherence and scheduling instantaneity of the robot to long-time-sequence tasks are improved.

A method and a device for planning and controlling a leg robot, as well as a robot and a storage medium, wherein the method comprises: determining a foot-end reference state of each leg based on the motion state and swing parameter of the leg of a leg robot in the next instant; planning a collision-aware swing leg trajectory based on the foot-end dynamics model and the discrete collision model for the leg that will begin to swing in the next instant; performing whole-body control of the leg robot based on the motion state, the reference state sequence, and the foot-end reference state, wherein for the leg that was intended to be a support leg during planning and that did not touch the ground, collision-aware whole-body control is performed based on a whole-body dynamics model and a discrete collision model.This solves the problems of limited passive flexible buffering of collisions in leg robots in related technology, the large computational volume of the control algorithm, the low control accuracy, the high wear of the robot hardware, the short lifespan and the inability to meet the needs of practical applications.

The utility model relates to a kind of low-pressure four-axis robot hardware circuit, comprising: core board, mainboard, drive board, safety relay, switching power supply and AC contactor;The core board is connected with the mainboard, the mainboard is connected with the drive board, and the mainboard is connected with safety relay by relay control line, and the safety relay is connected with the AC contactor by contact control line, and the AC contactor is connected with the drive board by switching power supply;The core board, mainboard and drive board constitute electronic circuit part, optimize the controller multi-core heterogeneous computing architecture, realize software and hardware collaborative processing, provide powerful computing power for controller;With modularization thought design relevant PCB, guarantee the multiplicity of PCB;Using safety relay and AC contactor designs double-loop structure, through hardware redundancy, real-time monitoring and high-level safety certification, complete fault tolerance to single point is realized, while giving consideration to quick response and maintainability.

The embodiment of the invention provides a robot control method, device and equipment, the architecture design of a robot at least comprises a data layer and a decision-making layer, the data layer is configured with various task background data, task types supported by the robot and various meta-actions supported by the robot, and the decision-making layer deploys a large language model. The robot control method comprises the following steps: acquiring real-time sensing information of a task scene; in the decision-making layer, a meta-action sequence is generated through a large language model on the basis of cue words including data, real-time perception information, task types and various meta-actions, and the data is obtained by searching from various task background data according to initial perception information of a task instruction and a task scene; and controlling the robot to execute the meta-action sequence. Therefore, the expandability and compatibility of a robot control mode to various task scenes and various robot hardware systems are effectively improved by utilizing personalized information configuration of the data layer and the model capability of a large language model in the decision-making layer.

This application discloses a companion robot interaction method and system, relating to the field of multimodal interaction technology and applied to a cloud service platform. The method includes: acquiring interaction events reported by a user through a companion robot hardware terminal or mobile terminal; converting the interaction events into interaction commands; and sending the interaction commands to the companion robot hardware terminal, so that the companion robot hardware terminal can trigger game plots or develop personality attributes according to the interaction commands, and interact with the user according to the game plots or personality attributes. Through the interwoven personality attribute development and game plot-triggered interaction process, the playability and personalization of human-computer interaction are both enhanced.

The invention relates to a multi-mode synchronous control method for driving a robot to narrate, which comprises the following steps: receiving to-be-described text data, and standardizing the to-be-described text data to obtain preprocessed text data; performing hierarchical decoding on the preprocessed text data based on a large language model to obtain structured feature annotation data; generating a multi-modal expression sequence according to the structured feature annotation data, and performing timestamp alignment synchronization on the multi-modal expression sequence to obtain a collaborative multi-modal instruction; and carrying out bandwidth allocation and execution scheduling on the collaborative multi-modal instruction by adopting a dynamic priority strategy, and driving robot hardware to execute a corresponding multi-modal expression action. The diversity of robot emotion expression content is improved, and interaction delay is reduced.

The application provides a trajectory error diagnosis method and system for a robot hardware interface, comprising: offline recording of planning trajectory data and original actual feedback data containing original time stamps during robot movement; replaying the planning trajectory data according to the original time stamps and control cycles to reproduce the original instruction sequence; in each control cycle of the replay, synchronously collecting the replay instruction values; based on the replay instruction values and the original actual feedback data, calculating the joint space error curve cycle by cycle; finally, based on the time sequence characteristics of the joint space error curve, automatically diagnosing and outputting the candidate error causes. The application improves the error reproduction capability through offline recording and replay, realizes accurate positioning of the error through cycle-by-cycle monitoring, and realizes standardized and efficient problem positioning through automatic diagnosis based on error characteristics, which can effectively improve the stability and precision of robot motion control.

This invention relates to the field of heterogeneous robot technology, specifically to a method and system for adaptive dynamic computing power allocation for heterogeneous robots. The method includes: collecting typical task information of heterogeneous robots; classifying tasks into different computing power requirement levels based on task complexity, real-time requirements, and data processing volume; extracting key information to construct a preset task computing power library; receiving robot task instructions, parsing the tasks, setting task real-time weights, collecting robot hardware status parameters in real time, and calculating the appropriate computing power; calculating the matching degree between the appropriate computing power and the computing power currently provided by the hardware; when the matching degree is lower than a preset threshold, performing structured channel pruning and fine-tuning the model through knowledge distillation; when the computing power requirement still cannot be met after pruning, identifying non-core tasks and initiating a cloud-based task allocation process. This technical solution can improve the task execution efficiency and hardware adaptability of heterogeneous robots.

The application discloses a kind of robot simulation scene and trajectory automatic generation method and system, receive natural language operation instruction, and through semantic analysis, visual asset generation, three-dimensional reconstruction and physical credible layout generation three-dimensional simulation scene that can be used for robot operation training;Utilize video generation model to synthesize the continuous image sequence of target operation task, and extract two-dimensional visual motion trajectory in target object area;By depth estimation, three-dimensional motion fusion, grasping posture generation and inverse kinematics mapping, two-dimensional visual motion trajectory is converted into robot end effector trajectory and corresponding robot joint trajectory, the simulation scene and expert trajectory generated are paired, form training data set for robot imitation learning strategy training, and support the strategy obtained by training is deployed to simulation environment or real robot hardware.The application can reduce the cost of robot training data acquisition and artificial trajectory design, improve the degree of automation of simulation scene and operation trajectory generation.

The application provides a kind of automatic inspection robot traffic inspection method based on urban traffic scene, it is related to intelligent traffic technology field, the method comprises: obtaining original multi-modal sensor data from inspection robot hardware platform and pre-processing, standard multi-modal sensor data is constructed BEV space representation learning module, simultaneously constructs VLA model module, constructs traffic event detection and anomaly identification module, and constructs end-to-end motion planning and navigation module and man-machine interaction and remote management module, define model training and deployment strategy to carry out model training and deployment control automatic inspection robot traffic inspection to multiple modules.Solve the technical problems that the prior art has insufficient perception, semantic understanding and navigation planning capability, which leads to the difficulty of efficient and reliable autonomous inspection of the inspection robot in complex traffic environment.The technical effects of autonomous perception, intelligent decision-making and efficient inspection of the inspection robot in complex traffic environment are realized.

The invention discloses a robot cluster scheduling system and method based on cloud side-end collaboration and an electronic device, and the system comprises a cloud platform, an edge cloud server and a plurality of robot hardware terminals, and the robot hardware terminals are used for determining a corresponding decision level according to the time delay demand and the computing power demand of a current robot task. Local decision making is carried out on the end-side hierarchical task according to the end-side AI chip, and scheduling control is carried out on the corresponding robot body according to a task decision making result; the edge cloud server is used for performing near-field collaborative decision-making on the edge level task and returning a task decision-making result to the robot hardware terminal; and the cloud platform is used for performing global task decision-making on the cloud level task based on the global digital twinborn model, and issuing a task decision-making result to the robot hardware terminal through the edge cloud server. The real-time performance and reliability of robot cluster scheduling are improved, and the method can be widely applied to the technical field of communication.

The invention provides a ubiquitous humanoid robot motion controller capable of being quickly deployed and a deployment method, and aims to solve the problem of quickly realizing a walking function of a humanoid robot, the motion controller comprises controller hardware and control software, the control software comprises a model training system, a motor parameter self-adaptive setting part, a real-time communication part and a control software programming interface, and can be conveniently butted with third-party simulation and training software through the designed interface to perform reinforcement learning training and simulation of a robot decision model; and the acquisition of related control parameters, the tuning of the related control parameters and the adaptation of humanoid robot hardware can be automatically carried out, the rapid deployment basically irrelevant to the robot hardware body is realized, and the software adaptation efficiency is greatly improved.

The invention relates to the technical field of computer vision and robot environment perception, in particular to a wheeled robot trafficability judgment algorithm under a pavement defect condition, which comprises the following steps: S1, designing a wheeled robot hardware system which comprises a carried camera for collecting pavement images in real time; s2, constructing an image data set containing a plurality of pavement defects, and marking the image data set; s3, based on a YOLO11n-seg model, developing a YOLO11n-RVD target detection segmentation model, and carrying out pavement defect identification and segmentation on the input image; s4, tracking the identified defects by using a multi-target tracking algorithm, and outputting a trafficability risk level in combination with a risk judgment algorithm; the problems of light weight of a target detection segmentation model and small target detection and positioning in the prior art are solved.

The invention belongs to the field of robot positioning, and particularly relates to a multi-sensor fusion cross-modal matching positioning method and system based on depth estimation. Pixel-level depth estimation is carried out based on a deep learning technology in a mode of collaborative fusion of a camera and a millimeter-wave radar to obtain a pixel point cloud; through matching the pixel point cloud with the high-precision map point cloud, cross-modal matching positioning is completed, accurate positioning of the robot in an operation environment is realized, and a positioning result with relatively high accuracy can be obtained while the hardware cost of the working robot is greatly reduced.

Embodiments include systems and methods for processing sensor data and generating operational instructions for ego (e.g., autonomous vehicle, robot) hardware. The ego includes any number of machine learning architectures, often neural network architectures, for processing the sensor data, perceiving the ego's surrounding environment, and making decisions regarding the ego's behavior. The ego's neural network architecture ingests the sensor data and uses the sensor data to perform any number of operations related to a particular domain or task, such as object recognition or path planning. A graph partitioner is trained to assign functions in the neural network's software and sensor data to specific hardware processing units. Some compilers are used to generate instructions based on the assigned type of processing unit.

According to the task planning and executing method and system of the heterogeneous robot, the storage medium and the computer equipment, the method is applied to the universal control system, the universal control system is in communication connection with different types of robots through the adapter, and unified management of the different types of robots can be achieved. Specifically, by introducing a meta-action / meta-skill library decoupled from robot hardware and a standardized task instruction, abstraction of robot hardware types by task planning and execution logic is realized, and repeated work of independently developing control codes for different robots is avoided; and the development cost and the maintenance difficulty of the heterogeneous robot system are obviously reduced. Meanwhile, according to the target planning scheme, the reasonability of task logic is ensured through manual verification, the same set of standardized instructions can adapt to different types of target robots through a hardware-instruction conversion mechanism of the adapter, and the compatibility of the system to heterogeneous hardware and the flexibility of task execution are greatly improved.

The invention discloses a communication method and device between dual systems of a robot, the robot, a medium and a product, and the method comprises the steps: responding to a target communication system network distribution instruction triggered on a robot hardware body, and controlling an Android terminal and the robot to establish a network connection of a target communication system. After the Android terminal and the robot are successfully networked, communication connection between the Android terminal and an MQTT server in a main system is established, and communication connection between the target Android application program and the MQTT server and communication connection between the robot control programs and the MQTT server are established in advance. And when a target control instruction sent by the Android terminal is received through the MQTT server, the target control instruction is issued to the target Android application program and / or the target robot control program to execute and / or display a control operation and / or a processing result matched with the target control instruction. According to the technical scheme, flexible and efficient communication of the Android application program and the robot control program in the dual systems of the Android terminal and the robot is realized.

The application provides a heterogeneous robot task planning and execution method, system, storage medium and computer device. The method is applied to a general control system. The general control system is connected with different types of robots through an adapter and can realize unified management of different types of robots. Specifically, by introducing a meta-action / meta-skill library decoupled from the robot hardware and standardized task instructions, the application realizes the abstraction of the task planning and execution logic to the robot hardware type, avoids the repeated work of developing control codes for different robots, and significantly reduces the development cost and maintenance difficulty of the heterogeneous robot system. Meanwhile, the target planning scheme of the application is verified by artificial verification to ensure the rationality of the task logic. The 'hardware-instruction' conversion mechanism of the adapter can adapt different types of target robots to the same set of standardized instructions, greatly improving the compatibility of the system to heterogeneous hardware and the flexibility of task execution.

The invention discloses reimbursement robot equipment based on AI large model bill discrimination, and belongs to the technical field of financial reimbursement automation equipment. The equipment integrates deep linkage of a printing execution module, a reimbursement robot hardware system and an AI recognition auditing system, and realizes integrated operation of paper bill scanning recognition, electronic bill generation, auditing result associated printing and filing through cooperative control of a mechanical arm and closed-loop connection of a printing process and a reimbursement full link. Closed-loop circulation of bills in the bill delivery bin, the scanning position, the temporary storage bin and the filing bin is achieved through the multi-axis mechanical arm; adopting a ViT + Qwen multi-mode large model to realize end-to-end structured extraction of the bill; and in combination with a background AI auditing engine and a multi-dimensional analysis module, full-chain automation from bill physical storage, intelligent identification and compliance auditing to prediction and archiving is completed.

The application relates to a wheel-leg robot trajectory planning method and system based on a diffusion strategy and belongs to the technical field of robots, which comprises the following steps: based on a wheel-leg robot hardware configuration and target task requirements, observation information is fused to generate multi-modal perception fusion data; in combination with the target task requirements, a multi-target constraint function and an energy optimization function are dynamically constructed, and constraint weights and optimization index matching ratios are adjusted according to task priorities; a trajectory planning model is constructed with the diffusion strategy as the core, and the trajectory planning model is trained until a model output matches a cooperative action instruction of the target task requirements and the robot motion characteristics; and the multi-modal perception fusion data is input into the adapted trajectory planning model to generate a continuous trajectory action sequence containing multiple time steps. The application applies the trajectory planning model based on the diffusion strategy to the trajectory planning of the wheel-leg robot in a complex environment, and has important engineering value.

This invention provides a hardware-software integrated collaborative system and method for robot ecosystems. The system includes: a business determination module for acquiring the business processes of the robot ecosystem, determining a set of business items within the ecosystem based on the business processes, and extracting attribute information for each business item; a function determination module for determining the action type and execution purpose of each business item performed by the robot hardware based on the attribute information, determining a target low-code combination based on the action type and execution purpose, and binding the target low-code combination to the robot hardware based on a preset controller; and an execution module for real-time monitoring of work instructions based on the binding results, and controlling the robot hardware to execute the target action according to the target low-code combination based on the preset controller upon detecting a work instruction. This ensures the high efficiency and accuracy of the hardware-software integrated collaboration, ensures more stable robot collaborative operations, and improves the collaborative operation effect.

The application relates to the technical field of path planning, in particular to an end-to-end real-time path planning method and system, which comprises the following steps: based on a global depth image, a preset ViT backbone network is used for feature extraction to obtain global features of an environment; target information and the global features are spliced to obtain spliced features, a key point sequence and a collision probability are obtained; and a path is planned according to the key point sequence. The global environment features are extracted through the ViT backbone network, and the spliced features are generated in combination with the target information, so that the planning network can simultaneously perceive the environment structure and the task intention, can adaptively focus on the navigation key area, and can thus exhibit stronger scene understanding capability in an unknown environment; the generated trajectory is more efficient and smoother, the control cost is significantly reduced, the robot shakes less in the movement process, the movement is more coherent and smooth, the navigation efficiency is improved, and the mechanical wear of the robot hardware is reduced.

This invention provides a thin film preparation system and its control method based on a formula language model-driven enclosed intelligent robot box. The thin film preparation system includes: a robot hardware execution device comprising a consumable storage mechanism, a gantry transfer mechanism, a capping and clamping mechanism, a spin coating mechanism, a vacuum-induced crystallization mechanism, an annealing mechanism, and an in-situ spectral detection mechanism; an intelligent cognitive device deploying a formula domain vertical model, a language intelligent agent module, a literature learning closed-loop module, and an experimental learning closed-loop module, wherein the language intelligent agent module is configured to coordinate the data interaction between the literature learning closed-loop module and the experimental learning closed-loop module; and an intelligent agent isolation device that, after confirming that the recommended formula instructions conform to physical boundary constraints, parses them into low-level control instructions and sends them to the robot hardware execution device. This invention achieves autonomous acquisition and transformation of formula knowledge, secure verification of semantic instructions, and self-evolution of underlying mechanisms and self-optimization of formulas during the thin film preparation process.

This utility model discloses a robot domain controller and robot hardware system based on a modular system. The robot domain controller includes a core computing module and a carrier board integrating the following functional circuits: a multi-rate Ethernet interface circuit, a wireless communication module interface, a storage device interface, an audio output interface with integrated power amplifier circuitry, an onboard inertial measurement unit (IMU), and a converged expansion interface. The converged expansion interface is used to connect to external expansion boards to provide robot-specific buses and signals. Adopting the above technical solution can improve integration, interface adaptability, product design level, and reduce costs.

The invention discloses a model sharing method and system for a heterogeneous robot, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a trained target model, and packaging the trained target model into a corresponding meta-skill; obtaining a target task and a corresponding heterogeneous robot; reading the skill description file of the meta-skill based on the target task to obtain the lowest capability configuration; the capacity of the heterogeneous robot is judged on the basis of the minimum capacity configuration, and a meta-action sequence is obtained on the basis of the skill description file; converting into a native control instruction which can be executed by the heterogeneous robot based on the meta-action sequence; and inputting the native control instruction to the heterogeneous robot to execute the target task. The shackle binding between an algorithm model and robot hardware is broken through, and lossless migration and sharing of the intelligent ability are achieved.

A kind of lactation sow and piglet behavior integrated detection system based on inspection robot, including inspection robot hardware system, sow and piglet behavior detection system;Inspection robot hardware system: utilize track type inspection robot to collect sow and piglet behavior image and video, storage, analysis and transmission are carried out;Sow and piglet behavior detection system: analyze sow static image and piglet dynamic short video, on the core computing unit of inspection robot, through YOLOv8, the posture of sow is detected, through lightweight TSM algorithm, piglet group behavior is detected, and the result is transported to database storage.Compared with traditional manual inspection, the present application based on inspection robot and computer vision avoids the intervention of artificial to lactation sow and piglet, maximally reduces the risk of zoonosis, improves inspection efficiency, reduces the cost of artificial input in farm.At the same time, the platform information management system is convenient to form effective production and breeding experience, with the characteristics of automation and intelligentization.