28results about How to "Realize autonomous navigation" patented technology

The invention discloses a fault detection method and system for a mine unmanned vehicle, and relates to the field of positioning navigation, and the method comprises the steps: collecting the data of each sensor; according to the laser radar data, a slam point cloud map is constructed through a cartographic algorithm; according to the IMU data and the vehicle speed data, performing fusion through a Kalman filtering algorithm to obtain odometer data; the preprocessed laser radar data, IMU data, vehicle speed data, camera data and odometer data are converted to a slam point cloud map coordinate system through coordinate conversion, fusion is carried out through a Kalman filtering algorithm, and state estimation of the unmanned vehicle is obtained; taking the state estimation of the unmanned vehicle as a predicted value, comparing the predicted value with laser radar data, IMU data, vehicle speed data and camera data, and calculating an observation residual error; and performing unmanned vehicle fault detection according to the observation residual error. Aiming at the low positioning precision of the mine unmanned vehicle in a complex environment in the prior art, the positioning precision is improved through multi-sensor fusion, fault-tolerant switching and the like.

The invention discloses an underwater robot dynamic environment sensing and navigation device and method, and relates to the technical field of underwater robos.The device comprises an underwater robot, a sensor system, a data processing system, a control system, a navigation system and a power system, and the sensor system, the data processing system, the control system, the navigation system and the power system are arranged on the underwater robot; the surrounding environment can be comprehensively sensed in real time, and the optimal path is calculated through the path planning module according to the real-time environment data; performing obstacle detection according to the environment image to obtain an obstacle detection result; when an obstacle is detected, the control system sends out an obstacle avoidance decision instruction, after the path planning module receives the obstacle avoidance decision instruction, an adaptive algorithm is used for adjusting the optimal path according to the information of the obstacle to be detected, the adjusted path is obtained, and autonomous navigation for a complex underwater environment and autonomous navigation are achieved. And the autonomous navigation and operation capabilities of the underwater robot are obviously improved.

The invention relates to the technical field of unmanned aerial vehicles, in particular to a limited space cable unmanned aerial vehicle inspection system based on a GPS signal-free condition, which comprises an unmanned aerial vehicle navigation module, a cable detection module, an obstacle avoidance module, an environment monitoring module, a data processing and recording module and a power management module, autonomous navigation of the unmanned aerial vehicle in a limited space is realized, the cable detection module monitors the surface state of a cable based on a convolutional neural network model, the obstacle avoidance module dynamically detects obstacles and adjusts a flight path, and the environment monitoring module monitors temperature, humidity and air quality and gives an early warning when the temperature, humidity and air quality exceed a safety threshold. The data processing module processes data in real time in combination with an edge computing technology and uploads the data to a control center, and the power management module regulates and controls energy consumption through an intelligent algorithm and automatically returns or emergently lands when the electric quantity is insufficient; the method is suitable for a cable inspection task in a GPS-signal-free environment.

The invention discloses a construction engineering construction site robot safety inspection and early warning method based on machine vision, and the method employs a tracked robot, integrates a plurality of sensors, employs an improved autonomous navigation and positioning technology, and combines a deep learning driven machine vision detection technology. And real-time monitoring and potential safety hazard identification of the construction site are realized. The system further comprises a real-time early warning system, a cloud platform data analysis and user customization interface, and a complete automatic safety monitoring solution is formed. According to the method, the inspection efficiency and accuracy are remarkably improved, the safety risk of manual inspection is reduced, and innovative technical support is provided for safety management of a building engineering construction site.

The invention belongs to the technical field of crimping tube X-ray detection. The invention provides a power transmission line crimping pipe X-ray detection system and an autonomous detection method. A laser radar and a binocular vision sensor are both in communication connection with a control terminal; the robot body comprises a walking rack, an X-ray detection mechanism fixed on the walking rack and a driving wheel located at the bottom of the walking rack, the walking rack, the X-ray detection mechanism and the driving wheel are both of a wave-shaped structure, and the wave-shaped structure at least comprises two concave parts; the driving wheels are driven by a driving motor, the control terminal is in communication connection with the driving motor to control the robot body to move forwards or backwards, and the control terminal is used for conducting navigation control over the X-ray robot according to data of the laser radar and the binocular vision sensor; the detection precision and comprehensiveness of the crimping pipe with the reinforcing wire are improved, and the walking stability of the robot on the power transmission line is improved.

The invention discloses a method and a device for unmanned aerial vehicle edge intelligent visual inspection in a remote area, relates to the technical field of unmanned aerial vehicle inspection, is applied to areas with complex terrains, lack of RTK base stations or areas with weak network signals, and is used for inspecting a target power grid area comprising power grid facilities and power lines by an unmanned aerial vehicle. The method comprises the following steps of S1, performing three-dimensional reconstruction on a power tower and a power line of a target power grid region in real time based on monocular vision of airborne edge equipment; s2, the unmanned aerial vehicle autonomously and dynamically plans an inspection route based on machine vision; and carrying out real-time detection and analysis based on the key photographing part information of the airborne edge equipment. According to the method, the unmanned aerial vehicle inspection technology is not limited by the coverage range of the RTK base station any more, the dependence on external facilities is reduced, the flexibility and adaptability of inspection are improved, and the method has great research and application value for unmanned aerial vehicle autonomous inspection in remote areas of Xinjiang.

The invention discloses a deep learning-based unmanned ship cluster water surface partition collaborative classification salvage method, and aims to solve the problem of large-area water area suspended matter recovery. A water area three-dimensional map is constructed by using an SLAM technology, environment perception is realized, areas are reasonably divided, overlapping compensation is performed, and the operation coverage rate and efficiency are improved; and performing full-coverage path planning on each partition by using an A * algorithm, and optimizing the path length. During cruising, the unmanned ship cluster obtains dynamic environment information, and obstacle avoidance is performed by using a DWA algorithm; and meanwhile, a deep learning algorithm is applied to monitor and classify suspended solids. And tracking the identified suspended matter through a DeepSORT algorithm, calculating an equivalent moving speed and a position offset of the suspended matter, predicting a future position, planning a shortest path by using a DWA algorithm according to a priority, and salvaging. After salvage, the operation state is judged according to hull data, and tasks are dynamically adjusted.

The invention discloses a mobile robot for autonomously mapping in an unknown closed space and a working method. The mobile robot comprises a robot main body, a three-dimensional laser radar, a two-dimensional laser radar, an inertial measurement unit and a wheel type odometer, according to the method, a mathematical model of scene information density is constructed, so that the relationship between the information density and navigation and map construction performance of a robot main body is obtained, quantification of the sparse degree of point clouds in an unknown environment space is realized, and autonomous navigation and decision making of the mobile robot in the unknown environment are realized; then, determining a target point based on the point cloud information density, constructing a three-dimensional point cloud map of the unknown closed environment by using a three-dimensional laser radar, and performing path planning and navigation on a two-dimensional grid map based on the two-dimensional laser radar to realize autonomous mapping of the mobile robot in the unknown environment; according to the invention, full-automatic path planning and obstacle avoidance in an unknown closed environment can be realized without relying on manual intervention, so that accurate mapping is carried out on the unknown environment, and the efficiency is high.

The present application provides a method for autonomous navigation and obstacle avoidance in a dynamic scene of a robot. An initial navigation path is obtained, and an environmental image of a moving target point is obtained through the robot's sensor equipment; the environmental image is input into a pre-trained image recognition model to obtain obstacle information and road information; the obstacle information includes the obstacle category corresponding to the obstacle, and the obstacle category includes static obstacles and dynamic obstacles; the obstacle avoidance algorithm corresponding to the obstacle is determined according to the obstacle category; during the navigation process, the initial navigation path is adjusted based on the obstacle information, road information and obstacle avoidance algorithm to obtain the target navigation path. Through the above method, the obstacle categories are divided into dynamic obstacles and static obstacles, and the corresponding obstacle avoidance algorithms are set respectively, which improves the adaptability and efficiency of the navigation system when facing different types of obstacles, and realizes the autonomous navigation and obstacle avoidance of the robot in a dynamic environment.

The invention relates to a shoreline identification and course keeping method, system and device based on vision. The method comprises the following steps: acquiring an image of a water area in front of a ship in real time through a shipborne camera; processing the image by using an improved Spatial CNN model to extract a left shoreline and a right shoreline, fitting the left shoreline and the right shoreline into straight lines, and calculating an intersection point of the two fitted straight lines; taking the intersection point as a far-end piloting target point, and calculating a heading adjustment value according to the field angle of the shipborne camera and the position relation of the ship relative to the piloting target point; and inputting the heading adjustment value into a PID controller for PID adjustment so as to generate a steering engine control instruction, and driving the ship to automatically adjust the heading according to the steering engine control instruction. The method is suitable for a complex river environment without map and support of positioning equipment, and autonomous navigation and course control of the ship are realized.

The utility model discloses a tray type cargo carrying four-way shuttle vehicle, which belongs to the technical field of cargo carrying, and is characterized in that the tray type cargo carrying four-way shuttle vehicle comprises a base, a shell is arranged at the top of the base, an adjustable tray assembly is arranged in the shell, and a telescopic rotary detection assembly is arranged on the front side of the shell; the adjustable tray assembly comprises a main tray fixedly connected to the top of the shell, and the problems that an existing tray type cargo carrying four-way shuttle vehicle is generally fixed in tray size and inconvenient to adapt to carrying of cargos of different specifications, so that trays need to be replaced when the cargos of different sizes are carried or the carrying efficiency is low due to the fact that the trays cannot adapt to the cargos can be solved. The problems that the existing tray type cargo carrying four-way shuttle vehicle generally has a fixed detection range and is inconvenient to comprehensively sense long-distance and short-distance obstacles in a complex storage environment, so that the obstacle avoidance is not timely or a detection blind area exists, and the running safety of the shuttle vehicle is influenced are solved in order to solve the problems that the existing tray type cargo carrying four-way shuttle vehicle is inconvenient to comprehensively sense the long-distance and short-distance obstacles in the complex storage environment.

The invention provides a guided projectile model / inertial autonomous navigation method and system, which are suitable for guided projectile whole-process autonomous navigation positioning, the framework mainly comprises two parts of filtering model construction and filtering method design, a system state equation is constructed according to a trajectory dynamics model, and the system state equation is calculated based on a parameter coupling relationship between an inertial device and a trajectory model. Constructing a measurement equation by taking the output angular rate of the gyroscope and the output specific force of the accelerometer as observed quantities; on the basis of filter innovation information and historical data, an innovation UKF based on data driving is designed, a measurement variance matrix can be adjusted in real time, the model / inertial parameter fusion precision is improved, and high-precision guided projectile autonomous navigation is achieved. The method is suitable for guided projectile navigation in a high-dynamic environment.

The visual navigation unmanned dining car comprises a dining car body and a visual navigation mechanism, access doors are hinged to the front side and the right side of the dining car body through hinges, and a mounting base is arranged in the middle of the front side of the lower end of the dining car body; the visual navigation mechanism comprises a protective cover, a two-dimensional code scanner, limiting grooves, limiting columns, a scraping strip, a cross scraping strip and a visual navigation assembly, the movable protective cover is arranged at the lower end of the outer surface of the mounting base, the two-dimensional code scanner is slidably connected to the middle of the interior of the mounting base through a fixing frame, and the limiting grooves are formed in the middles of the left side wall and the right side wall of the protective cover correspondingly; according to the unmanned dining car with the visual navigation function, linkage of opening and closing of the protective cover and lifting of the two-dimensional code scanner can be achieved through movement of the protective cover, self-cleaning of the scraping strip and the cross-shaped scraping strip is matched, and precise and reliable operation of the unmanned dining car is achieved in combination with multi-source visual navigation.

The application discloses a parking path planning method and device, a vehicle and a storage medium. The method comprises the following steps: after determining that the vehicle enters a target parking lot, acquiring environmental data of the vehicle, wherein the environmental data of the vehicle comprises spatial position information of a sensing target and spatial position information of an obstacle; acquiring an electronic map of a ramp region in the target parking lot based on the spatial position information of the sensing target, and acquiring an electronic map of a flat region in the target parking lot based on the spatial position information of the sensing target; and performing path planning based on the electronic map of the ramp region, the electronic map of the flat region and the spatial position information of the obstacle, so as to obtain a planned parking path of the vehicle. The technical scheme provided in the application embodiment can realize autonomous navigation and automatic parking in the target parking lot with the ramp region, and the intelligence of automatic driving is improved.

The invention relates to the technical field of unmanned aerial vehicles, in particular to an air-ground dual-purpose multi-rotor self-walking unmanned aerial vehicle which comprises a lower chassis, steering wheels are arranged on the four sides of the lower chassis, the four steering wheels are distributed in a rectangular array mode, a driving mechanism is installed in the lower chassis, the four steering wheels are all installed on the driving mechanism, a controller is installed on one side of the lower chassis, and the controller is connected with the controller. The upper end of the lower chassis is fixedly connected with an upper chassis, one side of the upper chassis is provided with a pan-tilt camera, and the upper end of the pan-tilt camera is provided with a laser radar. Through cooperation of the laser radar and the pan-tilt camera, the equipment realizes autonomous navigation and real-time obstacle avoidance, and meanwhile, real-time image data collected by the pan-tilt camera is transmitted to an unmanned aerial vehicle management platform; remote monitoring of the operation state and the working environment of the unmanned aerial vehicle is realized; in a scene with limited airspace, the controller can remotely issue a control instruction to drive the unmanned aerial vehicle to be automatically switched to a ground running mode, and accurate tail end delivery of goods is completed by means of the ground moving mechanism.

The invention relates to the technical field of ship pipeline detection, and discloses a ship pipeline detection robot and a detection method thereof.The ship pipeline detection robot comprises a robot body, a mechanical arm, a controller and a detection system, and the robot body, the mechanical arm and the detection system are electrically connected with the controller; the detection system comprises a detection sensor, a navigation positioning module and an obstacle avoidance sensor. The detection sensor comprises a visual sensor and an ultrasonic sensor. The robot body comprises a chassis, a magnetic attraction block, a driving part and wheels, the driving part is in transmission connection with the wheels, the wheels are rotationally connected to the bottom of the chassis, the wheels are electrically connected with the controller, the magnetic attraction block is connected to the bottom face of the chassis, and the navigation positioning module and the obstacle avoidance sensor are connected to the front end of the chassis; the detection sensor is connected to the upper end of the mechanical arm. The robot for ship pipeline detection and the detection method thereof can adapt to variable bending of ship pipelines, and it is ensured that the detection coverage is wide and accurate.

This utility model provides a delivery robot, including a chassis with a front and rear side on both sides along its length, and wheels mounted on opposite sides along its width. The wheels are driven by motors installed inside the chassis, and the motors are configured to drive the wheels on both sides of the chassis to rotate in opposite directions along its width. The chassis contains a driver, a power module, an embedded computing module, and a main control board, which is connected to the driver, the power module, and the embedded computing module. This delivery robot uses a depth camera to sense road conditions ahead, avoiding collisions. The motor-controlled wheel steering enables the depth camera to capture images horizontally. Since the depth camera has a large field of view in the vertical direction, the combination of the depth camera and wheel steering allows for comprehensive, blind-spot-free imaging.

The utility model discloses an anti-explosion inspection robot, and relates to the technical field of inspection robots. The anti-explosion inspection robot comprises an anti-explosion carriage, a four-wheel drive system, an information acquisition system, an autonomous navigation system and a wireless charging system. The explosion-proof carriage is used for protecting the battery assembly and the control module; the four-wheel drive system is arranged below the explosion-proof carriage; the information acquisition system is used for video information acquisition, laser methane detection and infrared thermal imaging; the autonomous navigation system adopts multi-sensor fusion and combines a map and a positioning algorithm to realize autonomous navigation of the robot; and the wireless charging system is arranged in the explosion-proof carriage and connected with the battery assembly, and the wireless charging system is used for wirelessly charging a battery in the battery assembly. The explosion-proof inspection robot provided by the utility model can autonomously detect the environment and equipment of a production area, realizes the inspection of equipment in a key area and a high-risk area, and promotes the digital and intelligent management level and development process of a chemical plant more quickly.

This utility model provides a delivery robot. Wheels driven by motors are mounted on opposite sides of the chassis in the width direction. The motors are configured to drive the wheels on both sides of the chassis to rotate in opposite directions. An openable takeout box frame is located at the upper end of the chassis. An inner takeout box frame is located inside the outer frame and extends through the opening. A touch screen is located on the outer side of the movable takeout box. This delivery robot uses a fusion of depth camera and LiDAR to sense the surrounding road conditions. The depth camera, combined with wheel steering, enables omnidirectional shooting. Navigation and positioning are achieved through a BeiDou positioning module for precise location. Users can retrieve their takeout by inputting a pickup code via a voice interaction module and the touch screen, meeting the needs of different users. After receiving the correct pickup code, the inner takeout box frame rises, allowing the user to easily retrieve the takeout.

The present application discloses a method and device for unmanned aerial vehicle navigation, including: when the confidence of GNSS posture is in a high confidence interval, the GNSS posture at this time is used for positioning flight, and the visual map construction step is performed based on the posture of the image frame; when the confidence of GNSS posture is in a medium confidence interval, the credibility of the current GNSS posture is judged by the constructed visual map, if it is credible, the visual map is updated based on the GNSS posture to obtain the updated visual map, if it is untrustworthy, the GNSS posture is discarded, and the positioning flight is performed based on the posture of the current image frame obtained by the visual map; when the confidence of GNSS posture is in a failure interval, the visual map is used to match the feature points in the current image frame and the visual map to estimate the current posture of the unmanned aerial vehicle. The problem of how to achieve positioning and navigation by combining GNSS and visual information when the unmanned aerial vehicle cannot receive or trust GNSS signals is solved.

The application discloses a kind of full-automatic carrying robot of potted flower flowerpot, including walking and moving mechanism, flowerpot clamping mechanism, sensing and positioning system, navigation system and flowerpot placing system;Walking and moving mechanism is used to drive robot to move and adapt to topography fluctuation;Flowerpot clamping mechanism includes mechanical arm and clamping device, for performing the grabbing, lifting and releasing operation to flowerpot;Sensing and positioning system includes camera and laser radar, for identifying and positioning target flowerpot;Flowerpot placing system is electrically connected with walking and moving mechanism, flowerpot clamping mechanism and sensing and positioning system respectively, for controlling walking and moving mechanism and flowerpot clamping mechanism according to the information of sensing and positioning system, complete the autonomous grabbing and placing of flowerpot;Navigation system combines boundary line identification to carry out path planning and decision-making, realize the autonomous navigation of robot.The application realizes the full autonomous identification, grabbing, transport and placing of potted flower flowerpot by multi-sensor fusion and visual navigation technology.

The invention provides an automatic ground cleaning robot for a coal conveying trestle, and relates to the technical field of pulverized coal conveying, the automatic ground cleaning robot comprises a support frame, symmetrically arranged driving tracks are mounted on two sides of the support frame, a spray washing unit is mounted on the support frame, a pulverized coal collecting and sweeping unit is arranged on the front side of the support frame, and an image monitoring device is mounted on the support frame. The problems that in the prior art, a worker usually holds a high-pressure water gun to conduct flushing, time and labor are wasted, meanwhile, the physical health of workers is affected due to the severe working environment, or a robot holds the high-pressure water gun to conduct flushing on the ground of the coal conveying trestle, but pulverized coal falling on the ground is only flushed away from the coal conveying trestle, and the working environment is poor are solved. When water is evaporated, the water is accumulated on the coal conveying trestle or the ground of the coal conveying trestle again under the action of wind power, and meanwhile, the water is only washed and cleaned, so that the situation of resource waste caused by loss of pulverized coal is often caused.

The invention discloses an indoor navigation map generation method and device, computer equipment and a storage medium, and the method comprises the steps: responding to an operation instruction for at least one vector tile, configuring the at least one vector tile, and generating a tile map of an indoor scene, the tile map comprising semantic information and traffic rules for the indoor scene; obtaining a vector map of the indoor scene; and responding to a received mapping instruction, performing coordinate mapping on the tile map and the vector map, and generating a navigation map of the indoor scene. A navigation map generated after coordinate mapping is completed can reflect the actual situation in the current environment, and the robot can achieve autonomous navigation in the actual environment. The navigation map further comprises semantic information and traffic rules of the indoor scene, and the semantic information and the traffic rules can also control the robot to move and work, so that the robot completes work in the indoor environment according to service requirements, and the purposes of automatic navigation and work completion of the robot are achieved.

This invention discloses a vision-based agricultural robot control system and navigation path extraction method, comprising: a vision processing module, a controller, a power supply module, a motor drive module, an infrared reflection sensor, an ultrasonic sensor, and a peristaltic pump module. Crop information is collected by a camera and transmitted to the vision processing module. The vision processing module processes the data to obtain the navigation centerline and transmits the required navigation parameters to the controller, thereby controlling the motor speed and achieving motion control of the agricultural robot. Simultaneously, the infrared reflection sensor detects crops, and the detected parameters are transmitted to the controller. The controller then controls the peristaltic pump module to spray pesticides, and the ultrasonic sensor performs obstacle avoidance to ensure the safety of the agricultural robot. This invention exhibits high accuracy, low processing time, good adaptability, and robustness in various crop environments.

The invention relates to the technical field of agricultural automation, in particular to an orchard-oriented graph-free autonomous navigation method based on laser radar, a plant protection method and a robot. By designing a navigation method based on laser radar point cloud and GNSS positioning attitude information, a vehicle can stably run along fruit trees without a global map and global path planning. Plant protection operation variable spraying control based on point cloud information: performing preprocessing and pre-segmentation on point cloud data acquired by a laser radar in combination with an autonomous navigation system, performing identification and segmentation on crops based on a three-dimensional semantic segmentation model, extracting crop canopy structure data, and further generating variable spraying control parameters, and precise pesticide application control in the plant protection operation process is realized.

The utility model provides a control module of a mobile device and the mobile device, the control module of the mobile device comprises a battery management circuit board, a motor drive circuit board and a flight control circuit board which are stacked in sequence, and each board is cascaded through a communication interface and a communication bus, thereby realizing expansion of interfaces, computing power and the like. A control module of the mobile device is high in integration level, simple in wiring and low in error rate, a battery management circuit board is responsible for power-on and power-off control, a main processor and a flight controller are arranged on a flight control circuit board, and the main processor serves as a computing power platform for autonomous navigation route planning and obstacle avoidance control. The mobile device can be flexibly connected with different sampling devices and finally output corresponding flight control signals according to data acquired by the sampling devices, and the motor driving circuit board is used for driving the motor according to the flight control signals, so that the attitude, position and speed of the mobile device are controlled, and autonomous navigation and obstacle avoidance are realized.

The invention discloses a mobile CT (Computed Tomography) and an intraoperative mobile CT positioning and navigation method based on laser guidance. The method comprises the following steps: projecting a laser ray on the ground by a laser emitting device, calculating the relative pose of the mobile CT relative to the laser ray, and controlling the advancing distance of the mobile CT and the steering angular velocity of a chassis based on the relative pose. According to the method, the high brightness and high coherence characteristics of the laser rays are utilized, the dependence on traditional positioning marks is effectively reduced, and the complexity and the cost of the system are remarkably reduced. In addition, application requirements in a complex medical scene are particularly considered, and reliability and accuracy in a variable environment are ensured. Therefore, the invention provides a new and efficient solution for precise positioning and navigation of the intraoperative mobile CT platform.

The utility model relates to a multi-sensor wheel type inspection robot for an underground coal mine environment, which comprises a walking main body, a control unit explosion-proof cabin arranged at the top of the walking main body, a wireless charging unit arranged on the front side wall of the walking main body, and a left ultrasonic sensor and a right ultrasonic sensor respectively arranged at two ends of the bottom of the rear side wall of the walking main body, a hydrogen sulfide sensor, a smoke concentration sensor, a temperature and humidity sensor and a GD4 sensor are arranged on the front side wall of the control unit anti-explosion bin, a sound wave detector is arranged on one side wall of the control unit anti-explosion bin, a supporting plate is arranged on the top of the control unit anti-explosion bin, a fixed bin is arranged on the top of the supporting plate, a telescopic cover is arranged on the top of the fixed bin, and a holder is arranged on the top of the telescopic cover. And a macro camera is arranged on one side. According to the utility model, the underground environment can be monitored in an omnibearing and multi-level manner, the accuracy and response speed of hidden danger investigation are improved, the safety risk of the underground coal mine is greatly reduced, and a powerful guarantee is provided for the safety production of the coal mine.