Abstract: A self-moving robot and a method of automatically determining an accessible region are provided. The self-moving robot performs a setting process of 2D obstacles to generate a goal map based on an exploration map, performs a setting process of 3D obstacles on the goal map to update the accessible region of the goal map when a 3D obstacle is detected, performs an avoidance action, and, moves within the accessible region of the goal map. The disclosure prevents the self-moving robot from colliding with obstacles or being trapped.

Abstract: An interactive control method is provided. The interactive control method includes the following steps. When robot equipment is located outside elevator equipment, the robot equipment is connected to a first control module disposed outside the elevator equipment to call, by the first control module, an elevator car of the elevator equipment. The first control module determines whether the elevator car arrives at a current floor to return an arrival signal to the robot equipment. The robot equipment is connected to a second control module disposed inside the elevator car according to the arrival signal to control, by the second control module, an elevator door of the elevator car to open. The robot equipment is driven to move into the elevator car. When the robot equipment is located within a working range of the elevator car, the second control module stops controlling the elevator door of the elevator car to open.

Abstract: An artificial marker combination to be read by an autonomous mobile device comprises at least two artificial markers arranged in order. Each artificial marker is different and the artificial marker combination is used as a group of artificial markers. A computer vision positioning system used in connection with the artificial marker combination utilizes the individual meanings in the multiple markers as instructions for the mobile device markers along its route. A method for the computer vision positioning system is also provided.

Abstract: An autonomous mobile device includes a map interpretation module, an image collection module, an image collection module, an artificial marker identification module, a personal guidance module, a voice input module and a control module. The image collection module collects an image in front of the autonomous mobile device and form an image signal. The artificial marker identification module receives the image signal and identifies the artificial marker in the image to achieve a positioning of the autonomous mobile device. The personal guidance module activates a personal guidance mode of the autonomous mobile device, and stores a location of a guidance point and an actual movement information of the autonomous mobile device to form a guidance path. The voice input module inputs a name of the guidance path. The control module controls the autonomous mobile device to move according to the guidance path stored by the map interpretation module.

Abstract: An elevator automatic guide assistant system comprises a user assistant system, an elevator outside assistant system, and an elevator inside assistant system. The user assistant system is configured to send a taking elevator instruction and target floor information to the elevator outside assistant system, receive and send the feedback information to a user. The elevator outside assistant system is configured to guide the user to arrive at an elevator doorway. The elevator inside assistant system is configured to send floor information to the elevator outside assistant system and the user assistant system, receive instructions from the user assistant system, and guide the user to arrive at the target floor. The present disclosure relates also to a guide method of the elevator automatic guide assistant system.

Abstract: The disclosure relates to an interconnecting device and system of IOT (Internet of Things). The interconnecting device is configured to interconnect different IOT platforms or interconnect a IOT platform with a robot. The interconnecting device comprises a hardware and a software comprising a control module, a communication module and a conversion module. The conversion module is configured to convert the message between different IOT platforms or between the IOT platform and the robot. The system of IOT comprises at least one IOT platform and the interconnecting device. The interconnecting device can interconnect different IOT platforms or interconnect a IOT platform with a robot conveniently.

Abstract: The disclosure relates to a self positioning system and an autonomously mobile device using the same. The self positioning system is installed on the autonomously mobile device at work. The self positioning system includes a controlling module, an image acquisition module configured to acquire an image of the surrounding environment of the autonomously mobile device, an image dealing module configured to deal the image of the surrounding environment by selecting the feature point, and a calculating module configured to calculate position information of the autonomously mobile device according to the position change of the same feature point in different images. The self positioning system further includes a spraying module configured to spray liquid or paste to form a feature point in the surrounding environment.

Abstract: The disclosure relates to a face recognition system. The face recognition system includes a camera module configured to acquire face recognition information of a target object; a feature point recognition module configured to select facial feature points; a displacement output module configured to output a displacement and azimuth of the camera module during acquiring the face recognition information at different positions; a distance calculation module configured to calculate depth distances between the facial feature points and the displacement between the different positions; and a face recognition module configured to judge whether the target object is the target user. A face recognition method is also related.

Abstract: An automatic guiding system for analyzing pavement curvature in or on an autonomous mobile device comprises an image acquisition module, a pavement curvature analysis module, a posture sensing module, and a pavement curvature database. The image acquisition module collects pavement curvature images as the autonomous mobile device moves. The pavement curvature analysis module processes the pavement curvature images, and extracts contour information of the pavement curvature images. The posture sensing module continuously senses the posture of the autonomous mobile device. The pavement curvature database is configured to stores the contour information and the posture.

Abstract: An automatic guiding system for analyzing ground texture in or on an autonomous mobile device comprises an image acquisition module, a ground texture analysis module, a posture sensing module, and a ground texture database. The image acquisition module collects ground images as the autonomous mobile device moves. The ground texture analysis module processes the ground images, and extracts texture information of the ground images. The posture sensing module continuously senses the posture of the autonomous mobile device. The ground texture database is configured to stores the texture information and the posture.

Abstract: An autonomous mobile device includes a map interpretation module, an image collection module, an image collection module, an artificial marker identification module, a personal guidance module, a voice input module and a control module. The image collection module collects an image in front of the autonomous mobile device and form an image signal. The artificial marker identification module receives the image signal and identifies the artificial marker in the image to achieve a positioning of the autonomous mobile device. The personal guidance module activates a personal guidance mode of the autonomous mobile device, and stores a location of a guidance point and an actual movement information of the autonomous mobile device to form a guidance path. The voice input module inputs a name of the guidance path. The control module controls the autonomous mobile device to move according to the guidance path stored by the map interpretation module.

Abstract: An apparatus for autonomously modifying environment information comprises an autonomous mobile device, a data storage module, an image collection module, an artificial marker identification module, a data recording module, a data analysis module, a data modification module, and a plurality of artificial markers. The image collection module is used to collect an image and transmit the image to the artificial marker identification module. The artificial marker identification module receives the images and identifies a plurality of artificial markers of the image to achieve a positioning. The data recording module records a path surface environment change information and a movement information. The data analysis module analyzes the movement information to achieve an optimal movement information. The data modification module receives the path surface environment change information and the optimal movement information to modify the map and the map description file stored in the data storage module.

Abstract: The disclosure relates to a self positioning system and an autonomously mobile device using the same. The self positioning system is installed on the autonomously mobile device at work. The self positioning system includes a controlling module, an image acquisition module configured to acquire an image of the surrounding environment of the autonomously mobile device, an image dealing module configured to deal the image of the surrounding environment by selecting the feature point, and a calculating module configured to calculate position information of the autonomously mobile device according to the position change of the same feature point in different images. The self positioning system further includes a spraying module configured to spray liquid or paste to form a feature point in the surrounding environment.

Abstract: An autonomous mobile device with computer vision positioning system comprises a map interpretation module, an image collection module, an artificial marker identification module, a path planning module, and an obstacle dodging module. The map interpretation stores a map of a desired moving area and a map description file corresponding to the map. The image collection module collects an image in front of the autonomous mobile device during movement in the desired moving area and form a image signal. The artificial marker identification module receives the image signal outputted by the image collection module and identifies the plurality of artificial markers of the image to achieve a positioning. The path planning module plans an optimal movement information of the autonomous mobile device moving between the plurality of artificial markers. The obstacle dodging module controls the autonomous mobile device to dodge an obstacle autonomously.

Abstract: The disclosure relates to an interconnecting device and system of IOT (Internet of Things). The interconnecting device is configured to interconnect different IOT platforms or interconnect a IOT platform with a robot. The interconnecting device comprises a hardware and a software comprising a control module, a communication module and a conversion module. The conversion module is configured to convert the message between different IOT platforms or between the IOT platform and the robot. The system of IOT comprises at least one IOT platform and the interconnecting device. The interconnecting device can interconnect different IOT platforms or interconnect a IOT platform with a robot conveniently.

Abstract: The disclosure relates to a face recognition method. The face recognition method includes: providing a face recognition system, the face recognition system includes a database module, a camera module, and a feature point compare module, wherein the database module stores a plurality of data-photos of a plurality of users; turning on the face recognition system to a searching motion, and searching person faces by the camera module to get a target person face of a target person; and, turning on the face recognition system to a recognition motion to judge whether the target person is one user.

Abstract: A computer vision positioning system for a computer-controlled autonomous mobile transportation device in a warehouse for example comprises an image collection module, an artificial marker identification module, a two-dimensional code identification module, a data storage module, and a control module. The image collection module collects images, and transmits the images to the artificial marker identification module and the two-dimensional code identification module. The artificial marker identification module reads an ID of the artificial marker. The two-dimensional code identification module reads a URL of the artificial marker. The data storage module stores a map, a map description file, and location and object-disposal information. The control module controls a autonomous mobile device to move and make a disposal.

Abstract: An intelligent parking system includes a first three-dimensional detecting module configured to obtain size information of a vehicle, a second three-dimensional detecting module configured to obtain space information of the parking lot, a spatial calculation module configured to obtain parking space requirement information, a parking space displaying module configured to adjust and display a coordinate position and a size of the parking space, a path planning module configured to obtain navigation planning path information, a vehicle guiding module configured to guide the vehicle, and a space warning module configured to judge whether the vehicle enters the corresponding parking space.

Abstract: The disclosure relates to a face recognition system. The face recognition system includes a database module, a camera module, a feature point compare module and a judge module. The database module is configured to store material of a plurality of users. The camera is configured to take a scene-photo of a target person and provide a group of scene-parameters of the scene-photo. The feature point compare module is configured to compare the group of scene-parameters with the material stored in the database. The judge module is configured to compare the scene-photo and the data-photo to judge whether the target person is a user. A face recognition method is also related.

Abstract: The disclosure relates to a face recognition system. The face recognition system includes a camera module configured to acquire face recognition information of a target object; a feature point recognition module configured to select facial feature points; a displacement output module configured to output a displacement and azimuth of the camera module during acquiring the face recognition information at different positions; a distance calculation module configured to calculate depth distances between the facial feature points and the displacement between the different positions; and a face recognition module configured to judge whether the target object is the target user. A face recognition method is also related.