A moving robot and a controlling method for the same are disclosed, in which mapping is performed along a wire provided in a boundary of a task area. According to various embodiments disclosed in the present disclosure, since the moving robot self-drives along the wire when setting the task area, a user may acquire map information corresponding to the task area without directly manipulating the moving robot.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, are described for performing privacy-aware surveillance with a security camera drone. The drone obtains image data using an imaging device and processes the image data to detect an object external to the drone. In response to processing the image data, the drone determines multiple viewing angles indicated in the image data with respect to the object. Based on the image data and the multiple viewing angles, a first section of the area for surveillance and a second, different section of the area to be excluded from surveillance are identified. The drone determines an adjustment to at least one of the multiple viewing angles to cause the second section to be excluded from surveillance. Based on the adjustment, the drone controls the imaging device to exclude the second section from surveillance imagery obtained by the drone.

A concrete surface processing machine (100) for processing a concrete surface, wherein the concrete surface processing machine is arranged to be supported on the concrete surface by one or more support elements (150) extending in a base plane (101) of the machine parallel to the concrete surface, wherein the concrete surface processing machine comprises a control unit (110) connected to at least one linear photo sensor (130) extending transversally to the base plane (101), and wherein the control unit (110) is arranged to detect a height (h) of an incoming laser beam (H) relative to the base plane (101), based on a point of incidence of the incoming laser beam (H) on the linear photo sensor (130).

A method for constructing episodic memory model based on rat brain visual pathway and entorhinal-hippocampal structure mainly applied to environment cognition and navigation of an intelligent mobile robot to complete tasks of environment cognition map construction and target-oriented navigation is provided. The image information of the environment, the head-direction angle and speed of the robot are collected, and then the head-direction angle and speed of the robot are input into the entorhinal-hippocampal CA3 neural computational model to obtain the robot's precise position. The visual information is input into the computational model of the visual pathway to obtain the scene information in the current vision of the robot. The above two kinds of information are fused and stored in a cognitive node with the topological relationship. Utilizing scenario information to correct the path integration errors during the exploration process of the robot, thereby constructing the episodic cognitive map representing the environment.

A method of localizing a mobile robot 100 with respect to a target object 601 using an initial map of a reference object which is a representation of the target object is disclosed herein. The method comprises obtaining LiDAR data 503 of the target object 601 captured by a LiDAR device 203 of the mobile robot 100 as the mobile robot 100 traverses in an environment associated with the target object 601 the LiDAR data including respective point cloud representations of the target object 501 and the environment in various sampling instances; iteratively updating pose data representing estimated pose of the mobile robot 100 with respect to a known reference location in corresponding sampling instances as the mobile robot traverses the environment; extracting a subset of LiDAR data points in the point cloud representation of a previous sampling instance, the subset of LiDAR data points corresponding to features of the target object 601 in the initial map based on the estimated pose of the mobile robot 100 associated with the previous sampling instance; obtaining desired LiDAR data points in the point cloud representation in a current sampling instance, the desired LiDAR data points including current LiDAR data points which correspond to the extracted subset of LiDAR data points in the previous sampling instance; and determining a localization pose of the mobile robot 100 with respect to the target object 601 in the current sampling instance based on the desired LiDAR data points. A system for localizing a mobile robot is also disclosed herein.

A buried person search device using a detachable module is disclosed. The device includes a ground drone 10 deployed at a site of a collapse disaster, the ground drone 10 having a communication device 80 and a first beacon, a camera device 40 mounted on the ground drone 10 to photograph surroundings of the ground drone 10, a storage 50 installed on the ground drone 10, a plurality of repeater modules 60 connected by a wireless communication network to relay wireless communications between the ground drone 10, a flying drone 32, and a command and control center 100, each of the repeater modules 60 having a second beacon, and a sensing device 70 installed on the ground drone 10 or the repeater modules 60 to collect a sound, wherein the storage 50 accommodates the repeater modules 60, and throws the repeater modules 60 in response to an operation signal.

A system and method are described that provide for queueing robot operations in a warehouse environment based on workflow optimization instructions. In one example of the system/method of the present invention, a control system causes certain robots to queue proximate to one another to permit resources to be obtained, transported, deposited, etc. without the robots crashing into one another (or into other objects), or forming traffic jams. A robot may remain at an assigned queue position at least until another position assigned to the robot becomes available.

A system and method are described that provide for mapping features of a warehouse environment having improved workflow. In one example of the system/method of the present invention, a mapping robot is navigated through a warehouse environment, and sensors of the mapping robot collect geospatial data as part of a mapping mode. A Frontend N block of a map framework may be responsible for reading and processing the geospatial data from the sensors of the mapping robot, as well as various other functions. The data may be stored in a keyframe object at a keyframe database. A Backend block of the map framework may be useful for detecting loop constraints, building submaps, optimizing a pose graph using keyframe data from one or more trajectory blocks, and/or various other functions.

Disclosed are a map exploration method for exploring an unknown region by a robot, a chip, and the robot. The map exploration method includes: step S1: acquiring frontier points that meet a preset passing condition by means of a frontier detector based on a rapid exploration random tree algorithm; step S2: filtering out frontier points for exploring the unknown region among the frontier points acquired in step S1; step S3: on the basis of navigation costs of frontier points explored by the robot at a current position and income information corresponding to the navigation costs, and by considering a passable condition of the frontier points, selecting a frontier point with the highest revenue from the frontier points filtered out in step S2, configuring the frontier point as a target point, and then controlling the robot to move from the current position to the target point, thereby building a local map.

A portable robotic platform system and method for automatically detecting, locating, and marking underground assets are provided. The portable robotic platform includes a housing with a sensor module including ground penetrating radar (GPR), LiDAR, and electromagnetic (EM) sensors. The robotic platform automatically collects GPR and EM data and uses onboard post-processing techniques to interpret the sensor data and identify the location(s) of underground infrastructure. The portable robotic platform can be deployed to apply paint to a ground surface to identify the located underground assets.