An autonomous moving system according to the present disclosure is an autonomous moving system including an autonomous moving body that moves autonomously. The autonomous moving system includes a control unit that executes control of movement of the autonomous moving body including collision control, a setting unit that sets a defense space around the autonomous moving body, for executing the collision control, a detecting unit that detects obstructions in a vicinity of the autonomous moving body, and a classifying unit that classifies obstructions that are detected. The setting unit changes a range of the defense space, based on the obstructions that are classified by the classifying unit, and the control unit executes control of movement of the autonomous moving body including the collision control in at least one of when the obstruction is inside the defense space and when the obstruction is predicted to enter the defense space.

Disclosed is a navigation method and system using color codes. The navigation method according to an embodiment of the present disclosure includes: dividing an aircraft path at a vertiport into a plurality of sub-paths based on a branch point; and selectively combining at least some of the plurality of sub-paths and generating a guiding path for an urban air mobility (UAM) to travel at the vertiport based on a departure point and a destination point scheduled for the UAM.

A moving object movable by remote control comprises: a moving object communication unit for receiving a request for driving control from outside the moving object; a driving controller capable of implementing driving control over the moving object in response to the request for driving control during a course of manufacture in a factory for manufacture of the moving object; a signal detection unit for detecting an disablement signal at a predetermined place along a moving route of the moving object; and an disablement implementation unit that performs an disablement process for disabling the remote control if a first condition is fulfilled. The first condition includes an event that the disablement signal is detected or an event that the disablement signal having been detected becomes no longer detected.

First and second movers can make communication with each other by means of a communication system in which a signal-receiving unit of the first mover receives from a satellite a signal indicative of a position coordinate of itself, a control unit of the first mover makes a pattern indicative of a position coordinate of the first mover, a display unit of the first mover displays the pattern, an image pickup unit of the second mover photographs the pattern, and a control unit of the second mover deciphers the thus photographed pattern to thereby control a position of the second mover in accordance with the thus deciphered pattern.

Disclosed is an unmanned platform with bionic visual multi-source information and intelligent perception. The unmanned platform is equipped with a bionic polarization vision/inertia/laser radar combined navigation module, a deep learning object detection module and an autonomous obstacle avoidance module; the bionic polarization vision/inertia/laser radar combined navigation module is configured to position and orient the unmanned platform in real time; the deep learning object detection module is configured to sense an environment around the unmanned platform according to RGB images of a surrounding environment collected by the bionic polarization vision/inertia/laser radar combined navigation module; and the autonomous obstacle avoidance module determines whether there are any obstacles around the unmanned platform during running according to the objects identified by the target, and performs autonomous obstacle avoidance in combination with the carrier navigation and positioning information. Concealment, autonomous navigation, object detection and autonomous obstacle avoidance capabilities of the unmanned platform are thus improved.

Disclosed is a marker allocation method. According to an airport shape and an airport size of an unmanned aerial vehicle airport and a standard shape and a standard size of a takeoff and landing point, a target layout of an unmanned aerial vehicle airport that includes takeoff and landing points is determined. Further, an initial takeoff and landing point is determined from the takeoff and landing points included in the target layout. Markers respectively allocated to the takeoff and landing points are determined from a predetermined marker set that includes markers of different image content, by using the initial takeoff and landing point as a start point, according to a predetermined search algorithm, and with a constraint that similarity between a marker of any one of the multiple takeoff and landing points and markers of other takeoff and landing points in a specified neighborhood thereof is the lowest.

A system comprises a GNSS sensor onboard an aerial vehicle; a monitor warning system (MWS) that determines whether the vehicle is in a GNSS denied environment; and a flight management system that includes a landing guidance module, and a database having location coordinates of landing sites. Onboard vision sensors and a radar velocity system (RVS) communicate with the guidance module. When the MWS determines that the vehicle is in a GNSS denied environment, the guidance module calculates an optimal flight path by receiving image data from the vision sensors; receiving position, velocity and altitude data from the RVS; receiving location coordinates of a landing site; processing the image data, and the position, velocity and altitude data, to determine a location of the vehicle and provide 3D imaging of a route to the landing site; and calculating a flight path angle to the landing site, using vehicle and landing site coordinates.