5929 results about "Obstacle avoidance" patented technology

A method for automated lane centering and/or lane changing purposes for a vehicle traveling on a roadway that employs roadway points from a map database to determine a reference vehicle path and sensors on the vehicle for detecting static and moving objects to adjust the reference path. The method includes reducing the curvature of the reference path to generate a reduced curvature reference path that reduces the turning requirements of the vehicle and setting the speed of the vehicle from posted roadway speeds from the map database. The method also includes providing multiple candidate vehicle paths and vehicle speeds to avoid the static and moving objects in front of the vehicle.

Embodiments of the invention provide systems and methods for obstacle avoidance. In some embodiments, a robotically controlled vehicle capable of operating in one or more modes may be provided. Examples of such modes include teleoperation, waypoint navigation, follow, and manual mode. The vehicle may include an obstacle detection and avoidance system capable of being implemented with one or more of the vehicle modes. A control system may be provided to operate and control the vehicle in the one or more modes. The control system may include a robotic control unit and a vehicle control unit.

The invention discloses a service robot control platform system and a multimode intelligent interaction and intelligent behavior realizing method thereof. The service robot control platform system comprises a voice control module, a binocular image acquisition module, an RFID (Radio Frequency Identification Device) receiving and transmitting module, a bottom wheel movement module, an obstacle avoidance module and a mechanical arm control module. The multimode intelligent interaction and intelligent behavior realizing method comprises the following steps of: 1, voice interaction; 2, independent navigation and location; 3, mechanical arm control; and face detection and recognition. According to the invention, a robot carries out intelligent interaction and intelligent behaviors, and the capacity of intelligent grabbing of the robot is enhanced. Except for special person recognition, distinguish of a picture face and an actual face is added, thus the influence to a program by the picture face is effectively eliminated. According to the invention, anthropomorphic mechanical arm grabbing is controlled in a specific anthropomorphic path grabbing manner, so that a service mode of the robot is more intelligent and humanized.

The invention discloses a picking method and picking robot device aiming at fruits which are in size of an apple and is similar to a sphere. The picking robot device comprises a mechanical actuating device, control system hardware and control system software. The mechanical actuating device comprises a picking mechanical arm, an underactuated manipulator, an electric sliding table and an intelligent mobile platform, wherein the control system hardware comprises an IPC (industrial personal computer), a motion control card, a data acquisition card, an AHRS (attitude and heading reference system), a coder, a monocular camera, a binocular camera, a force sensor, a slipping sensor and the like. During operation, the IPC fuses information of the coder, the AHRS, monocular camera components and an ultrasonic sensor to enable the mobile platform to independently navigate and avoid obstacles. A binocular vision system collects images of mature fruits and obstacles and extracts the characteristics of the images so as to realize obstacle avoidance of the mechanical arm and fruit positioning. Finally, the IPC fuses the information of the force sensor, the slipping sensor and the position sensor, thereby further reliably gripping the mature fruits and separating the fruits from fruit branches.

The invention discloses an automatic-navigation crawler-type mobile fruit picking robot which comprises a mechanical execution system and a control system and is characterized in that the mechanical execution system comprises an intelligent movable platform, a fruit picking mechanical arm and a two-finger type manipulator, wherein the intelligent movable platform comprises two crawler assemblies, an experimental facility fixing rack, a supporting stand column, a cross beam, a speed reducer and the like; and the control system comprises an industrial personal computer, a motion control card, a data collecting card, an image collecting card, an encoder, a GPS (global position system), a monocular zooming camera assembly, a binocular camera, a laser ranging sensor, a control circuit and the like. The automatic-navigation crawler-type mobile fruit picking robot integrates the fruit picking mechanical arm, the two-finger type manipulator, the intelligent movable platform and the sensor system, integrates multiple key technologies such as fruit identification, motion of the picking mechanical arm, grabbing of a tail-end executer, automatic navigation and obstacle avoidance of the movable platform, and the like, and really realizes automatic and humanized fruit picking.

An obstacle detecting unit detects an obstacle for a user wearing a head mounted display from an image of the outside world. A distance calculating unit calculates the distance from a detected obstacle to the user wearing the head mounted display. An obstacle replacing unit replaces the detected obstacle with a virtual object. A virtual object synthesizing unit generates a virtual object at a position within a virtual space displayed on the head mounted display, in which the position is determined according to the distance to the obstacle.

The invention discloses an unmanned aerial vehicle obstacle avoidance controlling method. An unmanned aerial vehicle subsystem and a ground station subsystem are arranged, the unmanned aerial vehicle subsystem comprises an embedded flight controller and an airborne terminal of a wireless data chain, a satellite positioning receiver and a height sensor are arranged in the embedded flight controller, the ground subsystem comprises an embedded monitoring computer and a ground terminal of the wireless data chain, and an electronic map containing geographic information of obstacles is arranged in the embedded monitoring computer. On the electronic map in the embedded monitoring computer of the ground station subsystem, the geographic information of the obstacles in a flight area is determined, virtual obstacle polygonal cylinders are established, shape data of the virtual obstacle polygonal cylinders are downloaded in the embedded flight controller which obtains the current position of the unmanned aerial vehicle and calculates space correlation between the unmanned aerial vehicle and the obstacle polygonal cylinders, track-shifting instruction of the unmanned aerial vehicle is generated, and automatic obstacle avoidance of the unmanned aerial vehicle is realized.

Framework may be implemented for transferring knowledge from an external agent to a robotic controller. In an obstacle avoidance/target approach application, the controller may be configured to determine a teaching signal based on a sensory input, the teaching signal conveying information associated with target action consistent with the sensory input, the sensory input being indicative of the target/obstacle. The controller may be configured to determine a control signal based on the sensory input, the control signal conveying information associated with target approach/avoidance action. The controller may determine a predicted control signal based on the sensory input and the teaching signal, the predicted control conveying information associated with the target action. The control signal may be combined with the predicted control in order to cause the robotic apparatus to execute the target action.

The invention relates to a method for planning a path for a mobile robot based on environmental modeling and a self-adapting window, which relates to a method for planning a real-time path for the mobile robot. The method comprises the following steps of: performing modeling and analysis on a multiple constraint local environment; performing passable analysis; performing safety analysis; performing motion smoothness analysis; performing goal-directed analysis; and performing path planning by adopting the self-adapting window. Because the method has better environmental suitability and obstacle avoiding capacity, the method obtains good safety and reachability, has high calculation real-time property, so the method solves the problem that the mobile robot generates an obstacle avoidance path in real time in an uncertain complex environment, provides a path selection method with optimized integration, better meets the requirements on obstacle avoidance for the mobile robot, realizes the real-time path planning and control of the robot, and provides an effective collision-less path planning method for the navigation application of the mobile robot.

An apparatus and method to determine the surface orientation of objects in a field of view is provided by utilizing an array of polarizers and a means for microscanning an image of the objects over the polarizer array. In the preferred embodiment, a sequence of three image frames is captured using a focal plane array of photodetectors. Between frames the image is displaced by a distance equal to a polarizer array element. By combining the signals recorded in the three image frames, the intensity, percent of linear polarization, and angle of the polarization plane can be determined for radiation from each point on the object. The intensity can be used to determine the temperature at a corresponding point on the object. The percent of linear polarization and angle of the polarization plane can be used to determine the surface orientation at a corresponding point on the object. Surface orientation data from different points on the object can be combined to determine the object's shape and pose. Images of the Stokes parameters can be captured and viewed at video frequency. In an alternative embodiment, multi-spectral images can be captured for objects with point source resolution. Potential applications are in robotic vision, machine vision, computer vision, remote sensing, and infrared missile seekers. Other applications are detection and recognition of objects, automatic object recognition, and surveillance. This method of sensing is potentially useful in autonomous navigation and obstacle avoidance systems in automobiles and automated manufacturing and quality control systems.

An autonomous navigation and man-machine coordination picking operating system of a picking robot comprises a travelling unit of the picking robot, a picking robot arm, an Agent, a wireless sensor network, a computer and a panoramic stereoscopic visual sensor. The Agent has functions of autonomous navigation, obstacle avoidance, positioning and path planning and the like, the wireless sensor network is used for completing man-machine coordination picking information interaction, the computer is used for providing long-distance intervention and management for picking administrators during man-machine coordination picking operation, and the panoramic stereoscopic visual sensor is used for acquiring panoramic stereoscopic video images in a picking area. The autonomous navigation and man-machine coordination picking operating system of the picking robot is fine in natural flexibility, simple in mechanism, low in control complexity, limited intelligent, high in picking efficiency, fine in environmental adaptation, and low in manufacturing and maintenance costs., Picking objects and crop are not damaged during picking.

The invention discloses a binocular vision-based robot target identifying and gripping system and method. The binocular vision-based robot target identifying and gripping system comprises a binocular image acquisition module, an RFID (Radio Frequency Identification) transceiving module, a chassis movement module, an obstacle avoidance module and a mechanical arm control module. The binocular vision-based robot target identifying and gripping method comprises the following steps of: (1) calibration of a binocular camera; (2) erection and conversion of a coordinate system; (3) target identification and location; (4) autonomous navigation and obstacle avoidance; and (5) control over mechanical arms for gripping a target object. According to the invention, a robot can be intelligently interactive with the environment, and the intelligent gripping capability of the robot is enhanced. According to the binocular vision-based robot target identifying and gripping system and method, except for identification and location to the target object, a navigation database is also established, and the robot can automatically reach an area where the object is located after the target object is positioned; and the binocular vision-based robot target identifying and gripping system and method ensures that a service mode of the robot is more intelligentized and humanized by controlling the human-simulated mechanical arms and adopting a specific personified path gripping manner.

An object detection system utilizing one or more thin, planar structured light patterns projected into a volume of interest, along with digital processing hardware and one or more electronic imagers looking into the volume of interest. Triangulation is used to determine the intersection of the structured light pattern with objects in the volume of interest. Applications include navigation and obstacle avoidance systems for autonomous vehicles (including agricultural vehicles and domestic robots), security systems, and pet training systems.

The invention discloses a mobile robot three-dimensional mapping and obstacle avoidance method based on a space bag of words model. The method comprises following steps: 1) collecting Kinect sensor data, and using a space bag of words model which fuses spatial relationships to describe scene image features 2) describing robot three-dimensional SLAM by means of the SDBoW2 model of the scene image to realize closed loop detection, three-dimensional point cloud registration, and graph structure optimization and therefore creating a global environmental three-dimensional point cloud density map; 3) the robot using the created global three-dimensional map and the Kinect sensor information to perform indoor real-time obstacle avoidance guiding. The method is aimed at low cost mobile robots without speedometers or laser distance measuring sensors; reliable real-time three-dimensional map creation and obstacle avoidance can be realized depending only on Kinect sensors; the method can be applied in long time mobile robot operation service at large area of indoor environment such as household places and office rooms, etc.

The invention discloses a multiple obstacle-avoidance control method of an unmanned plane used for electric wire inspection. The multiple obstacle-avoidance control method of the unmanned plane used for the electric wire inspection comprises an unmanned plane subsystem and a ground station subsystem. The unmanned plane subsystem comprises an embedded flight control device, a position detection module, an information processing module and an airborne terminal of a wireless data chain. The position detection module comprises a global navigation satellite system (GNSS) receiver, an electromagnetic field sensor and ultrasonic ranging sensors, wherein the ultrasonic ranging sensors are symmetrically arranged on the periphery of a machine body of the unmanned plane. The ground station subsystem comprises a ground terminal of the wireless data chain and an embedded supervisory control computer. The embedded supervisory control computer is loaded with a data base, wherein the data base contains an electric transmission line electromagnetic field distribution model and an electric transmission line space three-dimensional model. According to the multiple obstacle-avoidance control method of the unmanned plane used for the electric wire inspection, a security constraint area for the operation of the unmanned plane is built, the information processing module is adopted for integration of unmanned plane position information provided by an information detection module, relative distance between the unmanned plane and an electric transmission line is detected, and multiple obstacle-avoidance of the unmanned plane used for the electric wire inspection is achieved.

The invention discloses a multi-sensor fusion-based autonomous obstacle avoidance unmanned aerial vehicle system and a control method. The system includes an environment information real-time detection module which carries out real-time detection on surrounding environment through adopting a multi-sensor fusion technology and transmits detected information to an obstacle data analysis processing module, the obstacle data analysis processing module which carries out environment structure sensing construction on the received information of the surrounding environment so as to determine an obstacle, and an obstacle avoidance decision-making module which determines an obstacle avoidance decision according to the output result of the obstacle data analysis processing module, so as to achieve obstacle avoidance of an unmanned aerial vehicle through the driving of power modules which is performed by a flight control system. According to the multi-sensor fusion-based autonomous obstacle avoidance unmanned aerial vehicle system and the control method of the invention, binocular machine vision systems are arranged around the body of the unmanned aerial vehicle, so that 3D space reconstruction can be realized; and an ultrasonic device and a millimeter wave radar in an advancing direction are used in cooperation, so that an obstacle avoidance method is more comprehensive. The system has the advantages of high real-time performance of obstacle detection, long visual detection distance and high resolution.

The invention discloses a multifunctional intelligent routing inspection robot used in a power tunnel, which includes a vehicle body, wherein a driving wheel and a driven wheel are arranged at the top of the vehicle body; and an intelligent positioning module, an intelligent tracking module, an intelligent obstacle avoidance module, an intelligent imaging module, an intelligent distance measurement module, and an intelligent control module are arranged on the vehicle body. The multifunctional intelligent inspection robot used in the power tunnel has a rich inspection content, achieves a high inspection work efficiency, and can completely cover conditions in the tunnel; and in case of emergency, the site condition is clearly collected the first time and sent to a monitoring center, and an efficient treatment measure is taken for a specific part of the tunnel or a cable when necessary.

The invention relates to the technical field of robots, in particular to a robot path planning method and device in an indoor dynamic environment and a robot. Known obstacle environment information and obstacle expansion information are taken as reference in global path planning, a local path is generated according to dynamic obstacle information and obstacle expansion information in local path planning, is spliced with a global path with the minimum path cost value sum and replaces the global path with the minimum path cost value sum in a local window to obtain a target obstacle avoidance path, and the robot is controlled with a path fitting control algorithm to move along the target obstacle avoidance path so as to avoid dynamic obstacles. The obstacle environment information acquired in the global path planning is more complete, global environment is taken into consideration in the global path with the minimum path cost value sum, and the local path is generated according to the dynamic obstacle information and the obstacle expansion information in the local path planning, so that the robot can adapt to the changing environment during operating in the local window and is enabled to walk along the finally planned target obstacle avoidance path.

An autonomous obstacle avoidance and navigation method for unmanned aerial vehicle (UAV)-based field search and rescue provided by the invention includes global navigation based on GPS signals and anautonomous obstacle avoidance and navigation algorithm based on binocular vision and ultrasound. First, a UAV carries out search under the navigation of GPS signals according to a planned path. Aftera target is found, the autonomous obstacle avoidance and navigation algorithm based on binocular vision and ultrasound is adopted. A binocular camera acquires a disparity map containing depth information. The disparity map is re-projected to a 3D space to get a point cloud. A cost map is established according to the point cloud. The path is re-planned using the cost map to avoid obstacles. When there is no GPS signal, a visual inertial odometry (VIO) is used to ensure that the UAV flies according to the planned path, and an ultrasonic sensor is used to detect and avoid obstacles below the UAVto approach the rescued target. UAV autonomous obstacle avoidance and navigation in a field search and rescue scene is realized.

An obstacle avoidance path computing apparatus is provided with a preceding object detecting section, a host vehicle information detecting section, a preceding object arrival region estimating section and a preceding object avoidance path setting section. The preceding object detecting section detects a preceding object. The host vehicle information detecting section detects host vehicle information. The preceding object arrival region estimating section calculates an estimated arrival region within which the preceding object could arrive after a prescribed amount of time has elapsed since the preceding object was detected, based on an estimated attribute of the preceding object from the preceding object information. The preceding object avoidance path setting section calculates an avoidance path that will not encroach on the estimated arrival region based the preceding object information and the host vehicle information.

A method, medium, and apparatus of a self-propelled mobile unit with obstacle avoidance during wall-following. In the self-propelled mobile unit, a carrying unit may move the mobile unit by using a transmitted power, and a sensor unit can detect an obstacle and a wall, which may be respectively placed in front of and to the side of the mobile unit, with respect to a moving direction of the carrying unit. A controller may direct the carrying unit by generating a path along which the carrying unit moves according to a detection result from the sensor unit. Accordingly, the controller can direct the carrying unit to move while maintaining a predetermined distance (within a predetermined range) from the wall. If the sensor unit detects an obstacle, the controller directs the carrying unit to move in an obstacle free direction.