411 results about "Autonomous robot" patented technology

The invention relates to the technical field of autonomous robot decision-making systems, in particular to an autonomous robot decision-making system and method based on multi-modal sensing fusion, and the system comprises a multi-modal data collection module which is used for obtaining multi-modal sensing data of an environment; the data preprocessing module is connected with the multi-modal data acquisition module and is used for preprocessing the multi-modal sensing data; the feature extraction module is connected with the data preprocessing module and used for extracting features from the preprocessed multi-modal sensing data; the dynamic space-time diagram construction module is connected with the feature extraction module and is used for constructing a dynamic space-time diagram based on the features; the multi-modal data fusion module is connected with the dynamic space-time diagram construction module and is used for fusing the multi-modal data in the dynamic space-time diagram; through adaptive time alignment and a cross-modal space attention mechanism, the system can effectively process space-time inconsistency of data of different sensors, and centimeter-level positioning precision is realized.

An autonomous robotic vehicle with dynamic path planning capabilities, including: a dynamic path planning module configured to generate and adapt navigation paths in real time based on environmental changes and mission objectives; a sensor fusion module that integrates data from multiple sensors, including LiDAR, radar, cameras, ultrasonic sensors and GPS, to create a comprehensive environmental model; an obstacle avoidance module capable of detecting and predicting both stationary and moving obstacles and modifying the navigation path to avoid collisions; an artificial intelligence-based decision-making module for analyzing environmental data and learning from past navigation scenarios for improved performance; a decentralized control module that enables the independent operation of the vehicle in areas with interrupted or no connection to a central server; an energy management module configured to optimize battery usage by dynamically adjusting operating parameters based on real-time data and mission requirements; a cooperation module that enables communication and coordination between multiple autonomous robot vehicles for fleet management or the synchronized execution of tasks; a fail-safe module to ensure uninterrupted operation and data security in the event of communication failures or hardware malfunctions.

A method for operating a robot, including: capturing images of a workspace; capturing data indicative of movement of the robot; capturing LIDAR data as the robot moves within the workspace; generating a map of the workspace based on the LIDAR data; actuating the robot to drive; discriminating between an object on a floor surface along a path of the robot and the floor surface based on the captured images; actuating the robot to drive until determining all areas of the workspace are discovered and included in the map; and executing a cleaning function.

A method for operating a robot, including: capturing images of a workspace; capturing data indicative of movement of the robot; capturing LIDAR data as the robot moves within the workspace; generating a map of the workspace based on the LIDAR data; actuating the robot to drive; discriminating between an object on a floor surface along a path of the robot and the floor surface based on the captured images; actuating the robot to drive until determining all areas of the workspace are discovered and included in the map; and executing a cleaning function.

Various examples are provided related to generating a map of an environment. In one example, a method includes obtaining a coarse global mapping of an environmental space; determining a robotic traversal path within the environmental space using the coarse global mapping, the robotic traversal path maintaining a targeted distance to a nearest structure within the environmental space; initiating traversal of a robot along the determined robotic traversal path, the robot obtaining depth sensor measurements of the nearest structure during traversal along the determined robotic traversal path; and refining the robotic traversal path during traversal by the robot along the determined robotic traversal path based upon the depth sensor measurements, where the robotic traversal path is refined online to achieve targeted resolution and local accuracy of the depth sensor measurements. A refined map of the environmental space can be generated using the depth sensor measurements.

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.

The disclosure provides a communication delay compensation method and a communication delay compensation system based on an autonomous robot, where the method includes the following steps: establishing a state equation based on a system model of an AUV positioning system; acquiring an included angle between a direction vector of AUV to an observation station and a velocity vector of AUV based on the system model; establishing an observation equation according to the state equation and the included angle; establishing an extended Kalman filter equation based on the system model, the included angle and the observation equation; and calculating a position information predicted value at the current time by using the extended Kalman filter equation to complete communication delay compensation.

Systems and methods for autonomous robotic surgery are provided to control a movement of a robotic device so as to perform a surgical procedural step, wherein controlling of the movement is based on information including a spatial position and orientation of at least a part of the robotic device. A trigger information may cause the system to pause or stop the surgical procedural step and cause the system to receive a projection image. Further, the projection image may be processed so as to determine a spatial position and orientation of at least the part of the object or of the robotic device.

The invention discloses an ocean induced polarization and natural potential combined acquisition device and a data processing method, and solves the technical problem that the sulfide ore form cannot be accurately represented in the prior art. The system comprises a first underwater autonomous robot and a second underwater autonomous robot, the first underwater autonomous robot is provided with an induced polarization emission device, and the second underwater autonomous robot is provided with a natural potential acquisition device; the induced polarization emission device comprises an induced polarization control bin, a first watertight cable, an emission end positive pole and an emission end negative pole, and the natural potential acquisition equipment comprises a natural potential acquisition bin, a second watertight cable and a plurality of pairs of Ag / AgCl non-polarized electrodes. According to the method, the distribution characteristics of submarine sulfides can be more accurately explained, the multiplicity of solutions is reduced, and the exploration accuracy and reliability are improved.

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.

An autonomous robot drive assembly includes a plurality of drive units. The plurality of drive units may allow for movement and control of the autonomous robot drive. Each of the plurality of drive units are configured to be oriented independent of the other drive units. Each drive unit may include a plurality of independently operable driven wheels. Each drive unit may further include a drive unit coupling, allowing for the drive unit to rotate independently of other portions of the autonomous robot. The drive unit coupling may not be driven and may be configured to freely rotate.

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.

The invention relates to a multi-task operation planning method and system for an autonomous robot driven by a body cognition large model. The method comprises the steps that S1, coding is carried out based on an RGB image and a depth image which are obtained in real time, and body visual representation is obtained; s2, acquiring a natural language instruction, performing cross-modal fusion on the natural language instruction and the body visual representation to obtain a fusion feature, and performing multi-task decomposition on the basis of the fusion feature; s3, on the basis of a multi-task decomposition scheme, a diffusion strategy is utilized to generate a continuous action track of a robot end effector; and S4, acquiring a second RGB image and a second depth map after the robot executes according to the continuous action track, and taking the second RGB image and the second depth map as closed-loop feedback signals. The system is used for realizing the method. Compared with the prior art, the multi-task autonomous planning and accurate execution capability of the robot in a dynamic complex environment is remarkably improved by deeply fusing vision and language modalities based on the biont cognition large model and predicting an accurate action trajectory in combination with the diffusion strategy action decision module.

A system that uses autonomous robots for 3D manufacturing an object where the robots have different tool heads for performing different manufacturing operations.

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.

An androgynous fastener for autonomous robotic assembly of high performance structures is disclosed herein. The androgynous fastener is lightweight and facilitates assembly through simple actuation with large driver-positioning tolerance requirements. The androgynous fastener provides a high-strength, reversible mechanical connection and may be used in high strength-to-weight ratio structural systems, such as lattice structure systems. The androgynous fastener resists tensile and shear forces upon loading of the lattice structure system thereby ensuring that the struts of the lattice structure system govern the mechanical behavior of the system. The androgynous fastener eliminates building-block orientation requirements and allows assembly in all orthogonal build directions. The androgynous fastener may be captive in building-block structural elements thereby minimizing the logistical complexity of transporting additional fasteners. Integration of a plurality of the androgynous fasteners into a high performance, robotically managed, structural system reduces launch energy requirements, enables higher mission adaptivity and decreases system life-cycle costs.

A scheduling system includes first circuitry and second circuitry. The first circuitry stores, in a memory, information on a plurality of tasks to be executed by at least one mobile device. The information on the plurality of tasks includes information on an estimated amount of battery consumption of the at least one mobile device in executing each of the plurality of tasks. The second circuitry receives designation of the plurality of tasks to be executed by the at least one mobile device. The second circuitry further causes a display to display a screen having a schedule in which the plurality of tasks is arranged for the at least one mobile device based on the information on the estimated amount of battery consumption.

A system for generating improved notifications relating to events is disclosed. The system includes a sensing network including an event sensor configured to sense events. The system further includes a sensed-event processing system configured to append meta data to information associated with a sensed event. The system further includes a human-machine interface and at least one communication unit configured to communicate amongst the human-machine interface, the sensing network, and the sensed event processing system, the information communicated from the sensing device via the at least one communication unit. The system further includes a notification generation system configured to generate a notification to a user of the human-machine interface, the notification content including at least one user-selectable action option. The communication unit configured to communicate the notification via the human-machine interface and select the notification content, the notification type, or the means of notification based on the meta data.

The invention relates to a super-extensible underwater robot cluster system based on natural spinning and implicit cooperation and a coordination method of the super-extensible underwater robot cluster system, and aims to solve the challenges of a traditional underwater robot cluster system in the aspects of communication, expansibility and adaptability through a simple design and an innovative cooperation mechanism. The system comprises a plurality of autonomous robot monomers, cooperates with adjacent robots through a visual guidance form information interaction mechanism, and transmits state information by using an optical beacon system to realize implicit cooperation and self-organization. According to the method, a coordination mechanism based on consensus initiative is adopted, the robot adjusts behaviors only through local sensing and does not need to depend on global positioning or a complex communication network, the system has high self-healing capacity, and if an individual loses efficacy, other robots can automatically recover cluster stability and task execution. The underwater robot cluster system is low in cost and high in efficiency, and a brand new solution is provided for practical application of large-scale autonomous underwater robot clusters.

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 discloses a visual odometer method, system and device based on point-line features and a medium. Visual odometers are crucial to navigation and planning of autonomous robots, but weak light conditions, violent illumination changes and low texture scenes make a significant challenge to odometer estimation. Therefore, according to the visual odometer method based on the point-line features, the point-line features and deep learning are integrated, the point features and the line features are obtained through a self-supervised learning model for interest point detection and a line detection mode combining line light flow tracking and cross constraint, and the influence of complex lighting conditions is overcome. Meanwhile, the key frame is selected based on point feature counting and line feature overlapping angle constraint, and sparse matching is performed by integrating the convolutional neural network and the graph neural network, so that the accuracy and the calculation efficiency are improved.

The invention relates to a visual inertial fusion positioning method and system based on feature optimization, and belongs to the technical field of visual inertial navigation of autonomous robots. According to the method and the system, on the basis of a Geometric Feature Track Selection Visual Native Navigation System (GFT-VINS), through a feature trajectory selection strategy based on normal epipolar geometry, high-quality feature trajectories are screened out, low-quality trajectory interference is avoided, the positioning precision is improved, error accumulation caused by environmental changes and feature anomalies is reduced, and the system stability is enhanced. Meanwhile, in combination with a multi-state Constraint Kalman filter (MSCKF) framework, the efficient calculation capability is kept, the requirements of the autonomous robot for high precision, high stability and efficient calculation in a complex environment are met, and wide application of the autonomous robot is promoted.

A robotic fruit picking system includes an autonomous robot that includes a positioning subsystem that enables autonomous positioning of the robot using a computer vision guidance system. The robot also includes at least one picking arm and at least one picking head, or other type of end effector, mounted on each picking arm to either cut a stem or branch for a specific fruit or bunch of fruits or pluck that fruit or bunch. A computer vision subsystem analyses images of the fruit to be picked or stored and a control subsystem is programmed with or learns picking strategies using machine learning techniques. A quality control (QC) subsystem monitors the quality of fruit and grades that fruit according to size and / or quality. The robot has a storage subsystem for storing fruit in containers for storage or transportation, or in punnets for retail.

Systems and methods described herein relate to generating perception data. In one embodiment, a system extracts, in an offline processing environment, first features from a time sequence of perceptual sensor data to generate a first set of bird's-eye-view (BEV) feature images. The system also extracts second features from the first set of BEV feature images using a BEV feature extractor that performs bidirectional feature-level temporal aggregation to generate a second set of BEV feature images. The system also consumes the second set of BEV feature images using one or more neural-network heads to perform one of: (1) generating automatically labeled perception data to train one or more of an online perception model, an online prediction model, and an online planning model used to control an autonomous robot and (2) validating the performance of an online autonomous stack used to control an autonomous robot.

A robot according to the present invention is mounted on a vehicle which is capable of autonomous travelling, said robot comprising: an arm, orientation and the position of the tip end of which can be discretionarily changed; a vision sensor which is attached to the tip end of the arm; a detection unit which detects the amount of deviation of the vehicle from a specified path on the basis of a marker included in an image that has been captured by the vision sensor; and a travel instruction unit which, on the basis of the amount of deviation, instructs the vehicle to correct the travelling position or the travelling orientation of the vehicle.

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.

Systems and methods for estimating performance of an autonomous robot system that is configured to fulfil multiple line orders is described herein. The autonomous robot system comprises a plurality of autonomous guided vehicles configured to transport one or more cases within an environment so as to fulfil the multiple line orders. In some variations, a method includes obtaining first input data, generating a simulation model based at least in part on the first input data, determining a travel duration for each of the plurality of autonomous guided vehicles based on an execution of the simulation model, generating an analytical model based at least in part on the travel duration, and estimating the performance of the autonomous robot system based on an execution of the analytical model. The first input data includes data associated with operation of each of the plurality of autonomous guided vehicles and data representing a layout of the environment.

The invention discloses a low-carbon post-disaster rescue system based on quantum computing and an autonomous robot technology, and belongs to the technical field of post-disaster rescue. The system comprises a post-disaster information acquisition module, a data acquisition and analysis module, a quantum computing decision optimization and resource scheduling module, an autonomous robot task scheduling and execution module and a low-carbon technology model module. Performing ultra-high-speed processing and multi-target optimization decision on post-disaster environment data by adopting a mixed quantum annealing algorithm, and generating a resource scheduling and path planning scheme; the autonomous robot executes post-disaster building evaluation, material delivery and personnel search and rescue tasks based on an AI algorithm, so that the manual risk is reduced; and by combining low-energy-consumption hardware, renewable energy power supply and real-time carbon emission monitoring technologies, the carbon emission in the rescue process is reduced by more than 30%. The intelligent low-carbon rescue system solves the problems of low rescue efficiency, poor safety and high carbon emission in the prior art, is suitable for extreme post-disaster scenes with communication interruption, and provides an innovative solution for intelligent low-carbon rescue.

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 belongs to the field of autonomous robot trajectory planning and decision making, and particularly discloses a robot dynamic obstacle avoidance method and system based on sequence prediction and dynamic mask.The method comprises the steps that MDP modeling is conducted on a dynamic obstacle avoidance problem, and a simulation environment is built; designing a sensor data prediction method based on an improved long-short-term memory network, and predicting sensor data in a next period of time by inputting sensor data in a current period of time and sensor data in a past period of time; designing a sensor dynamic data mask based on the spatial attention network, performing data fusion on mask data and original data, and expanding the sensor data; designing a strategy function and a reward function, and training the decision network based on the simulation environment. Through integration of reinforcement learning algorithms, an obstacle avoidance strategy can be continuously optimized, and the navigation and obstacle avoidance capabilities of the robot in a complex environment are improved.