225 results about "Autonomous robot" patented technology

The invention relates to the technical field of tunnel defect monitoring, in particular to a tunnel monitoring system based on an autonomous robot, which realizes autonomous inspection and multi-modal data acquisition in a tunnel through a multi-module collaborative autonomous robot system and an integrated image acquisition and ground penetrating radar device, and improves the defect detection efficiency and continuity. Compared with a traditional mode depending on image recognition, a radar scanning image and a representation image are fused, feature extraction and defect reasoning are carried out through a deep learning model, the type and the extension area of the structural hidden defect are recognized, and the recognition capacity of deep structure abnormity is enhanced. Meanwhile, in combination with defect types and extension prediction, the robot is guided to execute supplementary scanning, and data integrity is ensured. Furthermore, cause classification analysis is carried out through a plurality of complete defect detection data, intelligent closed-loop monitoring from defect identification to cause diagnosis is realized, the monitoring cost is effectively reduced, and the accuracy and automation level of cause judgment of tunnel structure diseases are improved.

A method for greatly reducing data storage requirements of autonomous robots capable of product inventory is described. Instead using high resolution images for label identification and position tracking, a lower resolution map can be searched. Human or machines can identify position of labels, and via a reverse mapping, the corresponding position of a label on the high resolution image can be identified. Except for those portions of the high resolution image showing a label, most of the high resolution image can be discarded. Further processing on the limited image subset can be used to read the labels.

The invention discloses a robot automatic obstacle avoidance target recognition system and method, and relates to the technical field of autonomous navigation, and the method comprises the steps: obtaining and fusing an obstacle boundary map, a passable region mask and a dynamic target hot area map, and outputting an environment feature map; inputting the environment feature map into a pre-trained deep reinforcement learning model, and generating an obstacle avoidance instruction set according to the real-time state of the robot and a global path planning result; and acquiring actual path data of the robot, comparing the actual path data with an expected path point by point, and if the continuous deviation feature exceeds a preset continuous deviation feature threshold, inputting the environment feature map, the obstacle avoidance instruction set and the continuous deviation feature into the deep reinforcement learning model for updating. According to the invention, the obstacle avoidance capability of the autonomous robot in a dynamic complex environment is improved through multi-modal sensing fusion and a closed-loop dynamic optimization mechanism.

The invention provides a robot operation method and system based on a visual language model and reinforcement learning, and relates to the technical field of robot operation, a mechanical arm of a robot operates a target object according to task description, and the robot operation method comprises the steps that to-be-executed task description, a real-time task observation image and a real-time environment observation image are acquired; based on the task description and the environment observation image, decomposing the task description into executable subtask sequences represented by semantic key points by utilizing a visual language model; performing space mapping on the executable subtask sequence; and generating a reference trajectory by taking the mapped coordinates and directions as targets, and finely adjusting the reference trajectory by using the task observation image to obtain a final task operation trajectory. According to the method, robustness is shown in a long sequence task, diversified objects can be flexibly operated, the potential of combination of multi-modal reasoning and self-adaptive control learning is fully excavated, and a new path is developed for constructing an autonomous robot system with a higher capability level.

Thus, there is disclosed an autonomous robotic construction system comprising at least one frame comprising at least one substantially horizontal track and at least one set of substantially vertical track; at least one nozzle; at least one movable device coupled to the frame and configured to move said nozzle around said frame; at least one pump configured to pump building materials through said at least one nozzle; and at least one computer system comprising at least one microprocessor configured to control said at least one nozzle, said at least one movable device and said at least one pump to control deposition of any building materials on a site.

The invention relates to the field of robot technology and artificial intelligence, and discloses a robot fine operation method and system based on a visual language model and semantic key point characterization, a closed-loop multi-body Agent system is constructed, GPT-4o and Ground DINO are combined to accurately understand and disambiguate a natural language instruction of a user, an innovative bridging layer is introduced, and the robot fine operation method and system based on visual language model and semantic key point characterization are obtained. Abstract decision information is converted into bottom execution parameters represented by three-dimensional semantic key points, the problem of decision transfer distortion is solved, meanwhile, a KMP algorithm is improved, and the stiffness and contact force of trajectory tracking are dynamically optimized by introducing a learning-based time-varying hybrid impedance / force control strategy, so that the accuracy of trajectory tracking is improved. According to the method, the generalization success rate of fine operation tasks of the robot and the stability of physical interaction are remarkably improved, seamless connection from high-level semantic understanding to low-level action generation is achieved, and an efficient and reliable technical scheme is provided for autonomous operation of the robot in a complex scene.

An autonomous robot drive assembly includes a force sensing assembly. The force sensing assembly is a force sensing handlebar that is mounted in a specific orientation to allow for a user to manipulate the robot. The handlebar is configured to allow a user to manipulate the handlebar by providing force to the handlebar to move the handlebar from a neutral position. The manipulation of the handlebar causes instructions to be determined for operation of the robot. Based on the manipulation of the handlebar, a drive assembly of the robot moves the robot in accordance with the instructions.

In an embodiment of the invention, a sensor suite for an autonomous robotic kitchen system includes a thermometer, a sensor suite housing, a sensing chamber within the sensor suite housing having an intake port and an output port, at least one fan configured to control flow of gas through the sensing chamber, and a plurality of gas sensors mounted within the sensing chamber.

Some aspects include a method for operating an autonomous robot, including: capturing, with a first sensor disposed on the robot, data of an environment of the robot; generating, with the processor, a map of the environment based on at least the data of the environment; localizing, with the processor, the robot within the environment; capturing, with a second sensor disposed on the robot, data of a floor surface; determining, with the processor, a floor type of areas of the environment based on the data of the floor surface; and determining, with the processor, settings of the robot based on at least the floor type of the floor surface, wherein the settings comprise at least an elevation of each of at least one component of the robot from the floor surface.

Robots, systems, methods, and computer program products for training and operating (semi-)autonomous robots to complete work objectives are described. A robot accesses a library of reusable work primitives from a catalog of libraries of reusable work primitives, each reusable work primitive corresponding to a respective basic sub-action that the robot is trained to autonomously perform. A work objective is analyzed to determine a sequence of reusable work primitives that complete the work objective, and the robot executes the sequence to complete the work objective. A robot can be deployed with access to an appropriate library of reusable work primitives, based on expectations for the robot. The robot is trained to perform reusable work primitives in multiple libraries, by generating control instructions which cause the robot to perform each reusable work primitive. Training is performed by real-world robots performing reusable work primitives, or simulated robot instances performing the reusable work primitives.

The present application relates to a kind of self-robot multi-task operation planning method and system driven by embodied cognitive large model, wherein the method comprises: S1, based on the real-time acquisition RGB diagram and depth diagram are encoded to obtain embodied visual representation;S2, obtain natural language instruction and and embodied visual representation are fused across modalities, obtain fusion feature, and based on fusion feature multi-task decomposition scheme;S3, based on multi-task decomposition scheme, utilize diffusion strategy to generate the continuous action trajectory of robot end effector;S4, obtain the second RGB image and second depth diagram after robot executes according to continuous action trajectory, and it is used as closed loop feedback signal, system is used to realize the above-mentioned method.Compared with prior art, the present application is based on embodied cognitive large model, and the depth of visual and language modalities is deeply fused, and combined with diffusion strategy action decision module, accurate action trajectory is predicted, and the multi-task autonomous planning and accurate execution capability of robot in dynamic complex environment is significantly improved.

The invention relates to the technical field of the Internet of Things, in particular to a multi-autonomous-robot collaborative operation control method and system based on the Internet of Things. The method comprises the following steps: acquiring multi-point leaf surface micro-wind speed detection data of plants in a greenhouse environment; analyzing plant stomata opening and closing state change characteristics according to blade surface micro wind speed detection data, and determining plant health state grades; detecting a plant stomata activity time distribution rule according to plant health state grading; determining detection time window data of the autonomous robot according to the plant stomata activity time distribution rule; and determining a time sequence collaborative operation scheme based on the plant stomatal activity time distribution rule and the detection time window data. According to the invention, airflow disturbance generated by movement of the robot is converted into a reference signal for environmental perception, and intelligent compensation of environmental disturbance is realized through comparative analysis of expected disturbance and an actual detection result.

Autonomous robot guidance systems and related methods are disclosed. An example autonomous robot includes processor circuitry to detect a first fiducial based on image data collected while the robot is in a first drive mode, the first drive mode corresponding to an autonomous drive mode; cause the robot to switch from the first drive mode to a second drive mode responsive to the detection of the first fiducial, the robot to move in the second drive mode in a direction corresponding to a position of the first fiducial relative to the robot; generate navigation data for the robot, the navigation data indicative of a position of the robot in an environment; and cause the robot to switch from the second drive mode to the first drive, the robot to travel in the first drive mode based on the navigation data after the switch from the second drive mode.

Facilitating navigation localization for a mobile robot operating autonomously can include a deployment of a retractable landmark in a work area in response to an operational threshold not being met for a mobile robot to autonomously operate traversing the work area. The operational threshold not being met can be based on a determination of when landmarks in the work area for the mobile robot to use as perimeter markers do not meet the operational threshold.

The disclosed techniques provide an elevator car with a passenger compartment and a robot-dedicated compartment that includes a charging interface, a containment structure with fire suppression, and an access control assembly with a multi -panel door to admit and release an autonomous robotic device without exposing the passenger compartment. A control unit monitors sensors and interlocks to verify car-to-landing alignment and safe transfer, operates the door system, and coordinates car travel and deployment of the autonomous device to designated locations. The techniques include staging and recharging the autonomous device in the robot-dedicated compartment, monitoring environmental and battery conditions using the containment structure, commanding verified transfer through the access control assembly, coordinating movement of the car and the autonomous device, and recording status and exceptions while transporting the device to a designated location.

A system for receiving and delivering packages to a building having a package receiving station, a delivery robot and a control system. The package receiving station having a display, an input device, a package receiving area, a robot loading area, at least one package manipulation devices, and a computing device, and the delivery robot capable of interacting with the package receiving station to receive packages to be delivered, such that the package receiving station is capable of receiving a package at the package receiving area, moving it to the robot loading area and loading the package onto the delivery robot and the control system is capable of notifying a recipient of the arrival of the package, coordinating a delivery time, and initiating a package delivery whereby the package is loaded onto the delivery robot and delivered to a location within the building and delivers the package.

Multiple workpiece transfer holders for autonomous robots can be stacked and stored, and the stacked workpiece transfer holders for autonomous robots can be automatically unloaded. [Solution] By inserting the lowest ends of the four support columns of the upper work transport holder 10 into the openings at the uppermost ends of the four support columns of the lower work transport holder, it becomes possible to stack, for example, eight work transport holders, each with a pallet on it. An autonomous mobile robot (AGV) enters the work transport holder magazine 20 in the direction indicated by the solid arrow, raises the eight stacked work transport holders at once, and transports them from the work transport holder magazine in the direction indicated by the dashed arrow to a predetermined stock position.

An autonomous robot includes a robot body, a movement component, a collision sensor, an edge sensor, and a controller. The robot body includes a front collision body case, and a peripheral contour of the front collision body case is rectangular. The collision sensor is configured to generate a collision detection signal. The edge sensor is configured to transmit and receive the edge detection signal. The controller is configured to control the robot body to perform an edgewise adjustment mode. In the edgewise adjustment mode, the controller gradually adjusts a forward direction of the movement component towards the same target deflection direction according to the collision detection signal, in order to gradually reduce a relative distance between the edge sensor and an obstacle until the relative distance is within a preset edge detection distance range. Thus, a problem of low efficiency in edgewise movement is improved.

An autonomous collaborative robotic system for automatically detecting, locating, and mapping underground assets is provided. The collaborative robotic system includes two autonomous robots, each robot including a housing with extendable legs, a camera module, a laser module designed to project a structure light pattern on a pipe interior, a sensor module designed to collect images from the camera module, and a processing module designed to process and stitch data to generate a network of underground assets and identify one or more features within the asset network.

An autonomous robot localization system for managing a plurality of autonomous robots is disclosed. The autonomous robot localization system includes a plurality of rail paths including a plurality of markers, a plurality of autonomous robots, and a system controller. The plurality of rail paths is used for guiding the plurality of autonomous robots. The plurality of autonomous robots include a plurality of readers, and a plurality of robot controllers. The plurality of readers include a plurality of sensors The plurality of robot controllers generate a plurality of marker patterns for corresponding marker based on assigned presence and absence status of the plurality of marker bits. The system controller manages movements of the plurality of autonomous robots based on a plurality of commands sent to the plurality of robot controllers.

This invention discloses a method for autonomous robot operation in industrial assembly, relating to the field of robot control technology. The method is applied to a system containing multiple RGB-D cameras, a robotic arm, and an edge computing platform. It simultaneously acquires images and depth information from multiple perspectives, unifying them to a base coordinate system through coordinate reconstruction. Based on dual-view deviation and a fusion threshold, it determines whether to merge point clouds or downgrade single-view images, and performs PCA-OBB solving to obtain target geometric parameters and generate a grasping pose. It receives natural language commands, generates voxel operation code from a large language model to construct a 3D voxel cost map, and plans the path using a weighted A* algorithm. During robotic arm operation, it calculates pose error in real time and achieves closed-loop control through judgment, PID correction, or local replanning. This invention solves problems such as poor robustness of multi-view perception, poor real-time performance of large models, and large assembly errors, improving the efficiency and stability of autonomous operation in industrial assembly, and is suitable for complex assembly scenarios.

This item is an autonomous robot capable of two-way communication with the user. The item propels itself using wheels attached to the bottom of its body. The body is equipped with communication functions, cameras, and other sensors, allowing it to autonomously converse and change its facial expressions and arm movements in response to the user's voice and facial expressions. The user can also remotely control the item using a controller. The item aims to enrich people's lives by autonomously communicating its will, emotions, and thoughts with the user, just like a real person, building a relationship of trust. The purpose and use of the item is up to the user. For example, it can be used as a nursing companion for the sick, or as a pet or toy for children to play with, and is not suited to being limited to a specific industrial use.

A computer-implemented method of evaluating a goal planner of an autonomous robot in a real or simulated scenario, the method comprising: receiving evaluation data for evaluating performance of the goal planner in the scenario, the evaluation data being generated by applying the goal planner at incremental planning steps to compute a series of autonomous plans in response to changes in the scenario, the series of autonomous plans being implemented in the scenario to cause changes in autonomous states; the evaluation data comprising: an autonomous plan computed by the goal planner at one of the planning steps, and a scenario state at a time instant of the scenario; wherein the evaluation data is used to evaluate the goal planner by: computing a reference plan for the time instant based on the scenario state, the scenario state comprising autonomous states at time instants caused by one or more previous autonomous plans in the series of autonomous plans computed by the goal planner, and computing at least one evaluation score for comparing the autonomous plan to the reference plan.

A system for removing trash items from a space includes employing an autonomous robot to detect trash within the space, to approach the trash item upon detection and to remove the trash item from the space. The autonomous robot may have two or more trash bins with each trash bin being designated to be filled with a particular category of trash. The categories of trash may be different from one another. The autonomous robot is configured to detect the trash category of the trash item that will be picked up and, after picking up the trash item, to insert it in the appropriate trash bin. This way, the autonomous robot can collect and sort trash items according to their kind. A pan with sweeper arms can be used by the robot for collecting a trash item. In another approach, a trash item can be suctioned by the robot using vacuum.

The invention relates to a system comprising at least one autonomous mobile robot (service robot) and at least one mobile terminal for operating the robot. According to an embodiment of the invention, the robot and the mobile terminal are configured to communicate via at least one first wireless connection. The robot is configured to detect information about a stay position of a user (e.g. based on a stay position of the terminal) and, depending on the detected information about the stay position, to decide whether to send information to the mobile terminal and, if so, which information, and / or to decide whether to execute, interrupt, continue or terminate activities by the robot and, if so, which activities.

The invention discloses an SVG valve hall autonomous inspection robot control method and system, and the method and system achieve the intelligent inspection of an SVG valve hall through an autonomous robot carrying a depth camera, a panoramic camera, a vibration sensor and an infrared camera. The robot scans an environment by using a depth camera, matches a preset three-dimensional template to accurately position equipment coordinates, and dynamically plans a full-coverage inspection path. And then, the robot runs along an inspection path and synchronously acquires common images, vibration data and infrared temperature data to form a multi-dimensional information set, and a fault position is identified through parameter comparison. For the fault position, the robot continuously monitors the image sequence, the temperature fluctuation and the vibration change trend, predicts possibly-affected associated equipment in combination with the topological relation of the equipment, and outputs the name and the coordinate of the associated equipment. According to the method, three-dimensional positioning, multi-sensor cooperation and dynamic trend analysis are fused, the limitation of traditional single-point detection is broken through, fault spreading prediction is achieved, and the intelligent level of inspection diagnosis is improved.

An autonomous robotic cart includes a chassis, sensors coupled with the chassis, visible light cameras, and a handlebar unit coupled with the chassis. The handlebar unit includes a handlebar and a force sensor configured to detect a translational force and a rotational force exerted on the handlebar. The autonomous robotic cart also includes a holonomic and omnidirectional mechanical drive unit coupled with the chassis. The autonomous robotic cart is configured to autonomously navigate a physical environment to execute one or more navigation goals determined based on communication with a remote computing system configured to manage a fleet of robots including the autonomous robotic cart and also to cause the autonomous robotic cart to move translationally and rotationally in a direction corresponding to a output force vector determined based on sensor data.

The invention relates to an intelligent robot integration device based on direct digital-analog fusion and a welding method, and the method comprises the steps: directly reading a three-dimensional model and / or a two-dimensional drawing, and carrying out the geometric processing of the model; generating a welding seam, a scanning point, a welding gun angle and a robot running path through a generative path algorithm; optimizing a path track through dynamic virtual simulation; and the welding track and the technological parameters are compiled into codes which can be executed by the welding robot, and the welding robot is controlled to complete automatic welding operation. The method has the beneficial effects that the three-dimensional model of the steel structure and an intelligent welding system are integrated, the three-dimensional model is directly read, the intermediate drawing processing link is omitted, and the model directly drives the welding process operation; a welding process expert database, intelligent welding path programming, whole-process virtual simulation and an autonomous robot trajectory optimization algorithm are integrated.

Methods and systems for autonomous robot motion planning are disclosed. The method and system using the method includes obtaining, by an electronic processor, robot configuration training data, the robot configuration training data including a start configuration and a target configuration; applying, by the electronic processor, the start configuration and the target configuration to the neural network model to obtain a time field; determining, by the electronic processor, a predicted velocity indication based on the start configuration, the target configuration, and the time field; obtaining, by the electronic processor, a reference true speed indication; and training, by the electronic processor, a neural network model based on a loss between the predicted velocity indication and the reference truth velocity indication. Other aspects, embodiments, and features are also claimed and described.

Autonomous robotic systems for ultrasound examination are provided. The robotic systems include a robotic arm having an end effector for connecting an ultrasound tool. The arm is connected to a base station having a computing and control assembly disposed therein configured to process inputs from a vision module and control the robotic arm to position the tool to perform an ultrasound procedure on a patient. The end effector can include a load cell for measuring forces and torques experienced by the tool. A haptic device can be used to control articulation of the robotic arm to position the tool and provide force feedback to an operator. The robotic system can be configured to generate a 3D model of the patient, receive input of a feature region, and control the robotic arm to position the tool to perform an ultrasound procedure on the feature region. Various cleaning devices for cleaning the ultrasound tool are described.