90 results about "Robot environment" patented technology

The invention discloses a robot environment identification method based on multi-modal fusion, and the method comprises the following steps: collecting image data, point cloud data and acoustic data in a robot operation environment, and carrying out the preprocessing; inputting the structured multi-modal input sample into a modal perception type quantum encoder of a multi-modal quantum graph neural network; constructing a modal coupling quantum diagram based on the modal quantum state representation set; inputting the modal coupling quantum diagram into an entangled modal propagation unit, and performing cross-modal information propagation of nodes of the modal coupling quantum diagram through a parameterized quantum circuit; quantum measurement operation is executed on the fusion quantum graph embedded representation, and an environment recognition result is output; and inputting an environment identification result into a robot control module, and driving the robot to execute path adjustment, obstacle avoidance or action response. According to the method, the multi-mode quantum graph neural network is adopted, and autonomous recognition of the robot in a complex environment is achieved.

The invention relates to the field of mowers, and discloses a mowing robot full-coverage operation system and method based on multi-source information fusion, and the method comprises the steps: fusing the environment perception information of a mowing robot, multi-robot cooperation global sharing map information and historical operation data; through core steps of operation environment modeling, dynamic operation partitioning, multi-machine task allocation, collaborative path planning, operation execution monitoring, dynamic adjustment and the like, whole-process intelligent management and control of mowing operation is realized. Meanwhile, operation tasks are matched in combination with the operation state and capability parameters of the robot, partitions, tasks and paths are dynamically optimized according to the operation progress, the environment and the state change of the robot, and the matched system achieves automatic execution of all the processes through modular design. The adaptability and efficiency of multi-robot collaborative operation are improved, full coverage and low conflict of mowing operation are guaranteed, and the method is suitable for collaborative operation of mowing robots in various large-area and multi-obstacle scenes.

The invention provides a semantic scene reconstruction and interaction method and system based on a visual language model, and relates to the technical field of artificial intelligence and robots. The method comprises the following steps: acquiring image data shot by a camera; estimating a world pose of the camera; inputting the image data to a Visual Language Model (VLM) to generate a structured description containing an object semantic tag and quantitative spatial information thereof relative to the camera; calculating the world pose of the object according to the camera pose and the quantitative space information; a node representative of the object is created or updated in a hierarchical scene graph. The invention further provides a corresponding interaction system. According to the method, a rich and visual three-dimensional semantic map facing natural language interaction is constructed by deeply fusing the semantic understanding ability of the VLM and the geometric mapping ability of the SLAM, and the intelligent level of robot environment perception and man-machine interaction is greatly improved.

The invention relates to the field of robot environment perception, is used for solving the problem that an existing robot environment perception system lacks a unified and efficient hierarchical fusion architecture to systematically process multi-source heterogeneous data, and particularly relates to a robot real environment perception early warning system based on multi-sensor data fusion. Through cooperative work of the laser radar, the visual camera, the millimeter wave radar and the ultrasonic sensor, comprehensiveness and accuracy of environment perception are remarkably improved, confidence ranking and feature extraction are carried out on multi-source information through a progressive processing flow from space-time registration, cross verification and feature definition to deep fusion, and the accuracy of the environment perception is improved. And the heterogeneous feature fusion module generates an environment dynamic semantic map rich in texture, structure, motion and semantic information by means of a convolutional neural network, so that an accurate and comprehensive information basis is provided for early warning decision, and meanwhile, a dynamic hierarchical early warning mechanism provides reliable guarantee for safe and autonomous operation of the robot.

Provided is a robot, including: a chassis; a set of wheels coupled to the chassis; a plurality of sensors; a processor; and a non-transitory, machine-readable media storing instructions that when executed by the processor effectuates operations including: establishing, with the processor, a wireless connection with at least one of a computing device, a charging station, and a second robot; capturing, with at least one sensor, spatial data of an environment of the robot; generating or updating, with the processor, a map of the environment based on at least a portion of the spatial data; dividing, with the processor, the map into two or more rooms; storing, with the processor, the map in a memory accessible to the processor during a subsequent work session; and transmitting, with the processor, the map to an application of the computing device, wherein the application is configured to display the map.

The application provides a robot environment perception method based on multi-sensor fusion, comprising the following steps: performing space-time synchronization processing on original multi-sensor data to generate synchronized perception data; judging whether there is a semantic landmark matching the original or synchronized data in a topological map, wherein the matching condition is that the geometric primitive of the semantic landmark in the topological map and the same geometric primitive existing in the data are matched; when it is confirmed that there is a matching landmark, selecting the matching landmarks to form a set; based on the set, performing pose correction on the original multi-sensor data, fusing different pose constraints in the matching landmarks into the original data, and obtaining corrected data for robot path planning. The application can improve the consistency of multi-sensor data and the accuracy of robot environment perception, and provides reliable correction data for path planning.

This invention discloses a robot environment perception method and system, belonging to the field of intelligent robot technology. The method and system collect environmental perception data through multiple perception links; establish a dynamic confidence model for each perception link and calculate the dynamic confidence level in real time; construct an environmental complexity assessment module to calculate the environmental complexity index in real time; and dynamically adjust the perception mode and fusion strategy within an adaptive fusion framework based on the dynamic confidence level and environmental complexity index. The adaptive fusion framework includes a predictive mode switching layer, a confidence-weighted fusion layer, and an adaptive computational resource scheduling layer. A navigation module receives the fused data and performs path planning and motion control. This achieves predictive switching of perception modes and confidence-weighted fusion, enhances system robustness, optimizes computational resource utilization, and improves the robot's autonomous navigation capability in complex dynamic environments.

The invention discloses an environment sensing method for a photovoltaic module paving robot. The environment sensing method comprises the step of realizing full-direction scanning sensing of a construction environment ground in a peripheral area of the middle lower part of the photovoltaic module paving robot. When the photovoltaic module paving robot walks on the ground of a construction environment site, scanning sensing is carried out through front and rear side sensing areas and corner sensing areas of the left and right four corners of the photovoltaic module paving robot, and all-direction scanning sensing of the peripheral area of the lower middle portion of the photovoltaic module paving robot is achieved. The problem that the photovoltaic module paving robot collides with the photovoltaic panel module in the process of walking and paving the photovoltaic panel is solved.

The application discloses a robot environment perception method and system, a robot, a medium and a product. The method comprises the following steps: acquiring key frame pose data containing image data, scene structure data representing the association between an image frame and a target environment space, and semantic processing frames determined from the image frame; performing semantic perception processing on the semantic processing frames to obtain first semantic instance information, and / or matching and obtaining second semantic instance information from a semantic model; and performing fusion processing based on the obtained semantic instance information to generate or update semantic map data containing semantic road signs. The scheme decouples the geometric data acquisition and the semantic reasoning task in time sequence, so that the real-time positioning and tracking are not blocked by the semantic reasoning delay, the semantic reasoning can be triggered as needed, and the real-time positioning performance and the semantic map construction quality are considered under limited computing resources.

This invention provides a method and system for constructing a negative obstacle risk map, relating to the field of robot environmental perception technology. The method includes: acquiring multimodal sensing data of a legged robot in the current operating environment to obtain the types of negative obstacles, multiple candidate regions, and the corresponding confidence and uncertainty of each candidate region; using a risk diffusion algorithm, performing ink blurring processing based on the type, confidence, and uncertainty to generate a continuous risk field corresponding to each candidate region; dividing the continuous risk field to generate a continuous risk gradient distribution corresponding to each candidate region; performing terrain modeling based on laser point clouds, updating the continuous risk gradient distribution using Bayesian fusion to obtain a multi-level grid map, and then performing tactile closed-loop correction on the multi-level grid map to generate a risk cost map. This invention improves the accuracy of constructing a negative obstacle risk map.

The invention belongs to the technical field of intelligent cleaning robots, and relates to a fusion perception mapping method and device, a robot and a storage medium. Multi-modal fusion sensing data are obtained through the fusion sensing camera, information loss caused by shielding, illumination and material difference of a single sensor is made up, and comprehensive and stable sensing of the environment is achieved. A dynamic target is identified and processed through a preset auxiliary learning strategy, interference of the dynamic target on mapping is reduced, and map data stability and positioning precision are improved; an overlook semantic feature map is obtained based on an overlook semantic auxiliary mapping strategy, so that environment modeling has semantic understanding ability, and different regions or object categories can be distinguished; finally, the auxiliary mapping information and the overlook semantic feature map are fused to construct a high-precision overlook semantic map, and the robot environment understanding ability and task execution reliability are improved.

The application belongs to the technical field of agricultural robots, and discloses a path and height coordination planning system of a height-variable wheel-foot robot in weeding, which comprises a height-variable wheel-foot robot, an environment perception and weed identification module, a weed target tracking module, a navigation and path planning module, a height-path coordination planning module, an execution control module and a spraying execution module. On the basis of generating a basic navigation path, the height-path coordination planning module divides the navigation path into multiple sub-path segments by using weed spatial distribution, shielding indexes, spraying accessibility and stability constraints, and calculates a body running height for each sub-path segment, thereby forming a path-height coordination plan. In the execution stage, the height sequence is adaptively corrected according to online perception, and is coordinated with a low-level wheel-foot height control closed loop, so that the stability and accuracy of weeding operation in crop height variation and unstructured terrain are improved.

The present application belongs to the technical field of cleaning robots, and particularly relates to a cleaning robot environment recognition method and system based on visual images. The method acquires an image of a recognition area and recognizes a plurality of target objects to extract parameter information, outputs obstacle attributes for the plurality of target objects, outputs a final evaluation line of sight between the image acquisition device for obstacle target objects, judges whether the image acquisition device is in a surrounding state based on whether there are two or more final evaluation lines of sight that cross each other, and finally determines the interaction correlation state of the cleaning robot according to the judgment result and outputs a recognition result. The present application can accurately recognize complex enclosed spaces, accurately distinguish obstacle target objects, and flexibly decide cleaning tasks, thereby improving the reliability of environment perception and the accuracy of the environment model, and further improving the autonomous operation efficiency of the cleaning robot.

The invention relates to the technical field of robot environment perception, in particular to a real-time ground segmentation method and system. The method comprises the following steps: receiving and preprocessing laser radar point cloud data, and dividing the data into a plurality of concentric annular areas; carrying out adaptive ground fitting on each area and processing the multilayer structure through vertical plane fitting to obtain an initial ground point set; correcting an under-segmentation region of the current frame by using a time sequence backspacing mechanism and updating parameters; constructing an elevation map based on the initial point set, and obtaining a fine ground point set through adaptive resolution CSF simulation; and carrying out confidence evaluation on the two types of point sets, and generating a final result through multi-criterion fusion. Accurate segmentation of the complex terrain is realized, and robustness and real-time performance of a dynamic environment are improved.

The invention relates to the technical field of household cleaning robots, in particular to a household intelligent cleaning robot which comprises a cleaning robot body, an auxiliary escape structure is embedded in the top of the cleaning robot body, and an LDS module of the cleaning robot body protrudes out of the top of the auxiliary escape structure; the auxiliary escaping structure comprises a connecting end cover, the connecting end cover is arranged at the top of the cleaning robot body, a lifting ring is in bolting connection with the bottom of an LDS module of the cleaning robot body, a fixing ring is arranged at the bottom of the lifting ring, and the bottom of the fixing ring is in bolting connection with the top face of the cleaning robot body. The household intelligent cleaning robot provided by the invention has the advantages that the household intelligent cleaning robot can actively cope with a low limit space, effectively reduces the clamping stagnation risk on the premise of ensuring a core sensing function, and has the capability of actively adjusting the posture in the limited space to realize escape, so that the environmental adaptability of the cleaning robot is improved.

The motion planner generates a motion plan for a robot operating in a shared workspace, the motion plan including a nominal trajectory (e.g., a specified trajectory). An acceptable lag time is determined for the nominal trajectory, and adherence to the acceptable lag time during actual operation ensures self-collision-free operation. The actual motion of the robot (e.g., actual trajectory) is monitored for adherence to the respective acceptable lag time. Optionally, intervention is selected and / or taken as necessary (e.g., if the actual lag time approaches or exceeds, for example, a margin or threshold for the respective acceptable lag time). The motion planner can use the determined acceptable lag time to select a trajectory that provides more robust operation of the robot.

Some implementations relate to generating, based on processing captured vision data instances throughout an environment: regions of interest, and an estimated map location and region embedding(s) for each region of interest. Some implementations additionally or alternatively relate to determining, based on (1) a free form (FF) natural language (NL) instruction for a robot to perform a task and (2) generated region embedding(s) for identified regions of interest in an environment: object descriptors that describe objects that are relevant to performing the task and that are likely present in the environment. Some implementations additionally or alternatively relate to utilizing a subset of object descriptor(s), determined to be descriptive of object(s) that are relevant to performing the task of an FF NL instruction and likely included in the environment, in determining robotic skill(s) for robot(s) to implement in performing the task specified in the FF NL instruction.

This invention belongs to the field of robot environmental perception and navigation technology, and discloses a method and device for panoramic vision multi-sensor simultaneous localization and mapping based on 3D Gaussian splashing. In the multi-sensor fusion SLAM stage, multi-source observation information acquired by panoramic cameras, LiDAR, and inertial measurement units is used to achieve real-time estimation of the vehicle's pose through tightly coupled state estimation, constructing a globally consistent color point cloud map. In the 3D Gaussian splashing mapping stage, based on pose priors and the color point cloud, a continuous 3D Gaussian scene representation method is introduced to incrementally and densely model the environment in 3D. Differentiable rendering and geometric-photometric joint optimization are used to improve the geometric accuracy and visual consistency of the map. This invention achieves high-quality 3D mapping and representation in complex environments while ensuring system real-time performance and robustness, and can be applied to environmental perception and navigation in mobile robots, autonomous driving, augmented reality, and unmanned systems.

The embodiment of the invention provides a robot environment object recognition method and system, and relates to the technical field of robot visual recognition, and the method comprises the steps: obtaining a background diffuse reflection light spot, and collecting a multispectral image data stream which comprises a visible light image and a near-infrared image; analyzing the multispectral image data stream, and quantifying the response difference of the background diffuse reflection light spot and the coffee cup under different spectral bands to obtain a target spectral response difference; generating a de-interference image based on the target spectral response difference; performing contour segmentation on the interference-removed image to obtain the contour of the coffee cup; and calculating the spatial position and posture of the coffee cup based on the contour. The success rate of grabbing operation of the robot and the overall working efficiency can be improved.

Provided are an imaging position and orientation determination device and the like that make it possible to appropriately image a subject by using a robot. An imaging position and orientation determination device (10) comprises: a data acquisition unit (121) that acquires robot environment information and imaging information obtained when an operator imaged a subject by means of a first camera; an interference condition generation unit (122) that generates, on the basis of the robot environment information, an interference condition between a robot and an environment when the robot images the subject by means of a second camera; a position and orientation computation unit (123) that computes, on the basis of the imaging information and the interference condition, imaging position candidates and imaging orientation candidates when the robot images the subject by means of the second camera; an evaluation unit (124) that evaluates the imaging position candidates and the imaging orientation candidates; and a determination unit (125) that determines, on the basis of an evaluation result from the evaluation unit (124), an imaging position and an imaging orientation when the robot images the subject.

The application discloses a kind of based on hovering underwater vehicle's underwater unknown environment active mapping method, belong to robot environment exploration technical field.The underwater unknown environment active mapping method based on hovering underwater vehicle provided by the application utilizes hovering underwater vehicle to obtain the environmental data of unknown area of ocean in cooperation with sensor, determines the best next viewpoint according to information gain value, and determines the safe and feasible path to the best next viewpoint according to the cost of each adjacent node on the current tree as the parent node when the best next viewpoint is reached.Updating grid map in real time in this way, efficiently, quickly and accurately realize the construction and dynamic updating of map, and then assist underwater vehicle to realize the navigation of target area, and has broad application prospect in underwater environment exploration task and underwater structure inspection based on underwater vehicle and similar tasks.

The application discloses a kind of robot environment perception systems based on multi-radar fusion, it is related to robot environment perception field.The system ingeniously fuses the radar sensor data of multiple different types, different deployment locations, realizes the high-precision, high reliability, low power consumption perception to the three-dimensional environment around robot by advanced feature extraction, correlation matching, information fusion and dynamic environment modeling etc.Technologies.System can effectively identify and track static obstacles, dynamic targets, and accurately estimate its position, velocity, attitude and size, provide reliable environmental information for the autonomous navigation, obstacle avoidance, path planning, target interaction etc.of robot.The application greatly improves the environmental perception ability of robot in various complex scenes by fusion strategy, dynamic adaptive algorithm and effective use of redundant sensor information.

This invention discloses a robot cost map construction method based on information entropy and semantic weights, belonging to the field of robot environment perception and map construction technology. This invention introduces semantic weights w... class This technology enables differentiated handling of "walls" (requiring high passability) and "pedestrians" (requiring high safety). The robot can be relatively "aggressive" when squeezing through door gaps and relatively "conservative" when avoiding pedestrians, without compromising global parameters. It transforms semantic information extracted from deep learning into "potential field geometry information" understandable by the navigation layer, achieving deep integration of perception and decision-making, and improving the robot's adaptability in unstructured environments. The spatiotemporal uncertainty diffusion technology based on predicted trajectories allows the robot to naturally avoid obstacles based on their movement trends, filling the gap of traditional expansion layers lacking a time dimension. In densely populated scenarios, it significantly reduces the number of sudden stops and path replanning, improving traffic efficiency.

The invention discloses a robot environment recognition system based on multi-modal sensing, and aims to solve the problems of environment recognition precision reduction and insufficient robustness caused by single-modal sensing failure, insufficient multi-modal information fusion mechanism and dynamic motion interference in an extreme environment. The system comprises a multi-modal sensing module, a feature depth fusion module, an environment dynamic modeling module and an identification decision module, and finally outputs an environment identification result and an operation decision by synchronously acquiring multi-modal data, performing cross-modal feature alignment and adaptive weight fusion, constructing a dynamic three-dimensional semantic model and compensating motion distortion. According to the invention, the identification precision, the adaptive capability and the overall reliability of the system in extreme and dynamic environments are effectively improved.

The invention relates to the technical field of arena robots, in particular to an arena robot environment sensing method based on multi-sensor time sequence fusion, and mainly solves the technical problems of low sensing precision, poor robustness and unreasonable scheduling of the arena robot environment sensing method. The method is based on an arena robot carrying a visual sensor, an IMU sensor, an ultrasonic sensor and an infrared reflection type sensor and is iteratively updated according to a preset sensing period. In each sensing period, the method executes the following steps: S1, generating a synchronous observation sequence; s2, forming a feature sequence; s3, calculating a credibility index; s4, constructing a dynamic attention network, and outputting a fusion weight vector and a scheduling instruction set; s5, calculating a white line boundary risk degree; s6, performing coverage correction through a security policy layer; s7, carrying out multi-sensor time sequence fusion; and S8, feeding back a result and updating a historical time sequence window.

A kind of based on 5G air interface data's bistatic target detection and positioning method, by obtaining the original channel state information (CSI) data of each channel of bistatic receiving array and after being preprocessed, corrected time-delay-doppler spectrum is constructed by using reference radial method to eliminate sensing non-ideal factors;Then, the corrected time-delay-doppler spectrum is subjected to target detection and candidate point screening, to obtain stable target candidate points;Finally, the target parameters are estimated according to the stable target candidate points, and the target position coordinates are solved by combining the geometric relationship in the bistatic scene.The present application directly processes the original channel state information data of multiple channels to realize the stable detection and high-precision positioning of moving targets, which can not only be used in the bistatic target detection and positioning scene based on 5G air interface data, but also can be further extended to the sixth generation mobile communication (6G) sensing scene, distributed array sensing system and intelligent transportation, security monitoring and robot environment sensing application fields.

The present disclosure provides a robot environment perception method, device and system based on multi-sensor fusion. The method of the present disclosure can comprise: receiving raw observation data from each sensor configured on the robot body, obtaining equivalent observation data of each sensor at the system global time based on the time drift model of each sensor and the raw observation data of each sensor, calculating a health degree index of each sensor and generating a time-varying confidence weight of each sensor according to the health degree index, determining an active sensor subset according to the health degree index of each sensor, determining a state vector of the robot system according to the time-varying confidence weight and the equivalent observation data of each sensor in the active sensor subset, and generating environment representation data based on the state vector of the robot system, the time-varying confidence weight and the equivalent observation data of each sensor. The present disclosure can effectively avoid the influence of time synchronization error and spatial registration deviation between sensors, and improve the perception accuracy.