Provided are a mobile body, an information processing method, and a computer program. A mobile body of the present disclosure includes: an imaging unit configured to capture an image of an environment around the mobile body; an estimation unit configured to estimate a position of the mobile body on the basis of the image captured by the imaging unit; a calculation unit configured to calculate the position of the mobile body on the basis of a control command for controlling movement of the mobile body; and a wind information calculation unit configured to calculate information regarding wind acting on the mobile body on the basis of a first position that is the position of the mobile body, which is estimated by the estimation unit, and a second position that is the position of the mobile body, which is calculated by the calculation unit.

In order to more accurately white balance an image, weightings can be determined for pixels of an image when computing an illuminant color value of the image and/or a scene. The weightings can be based at least in part on the Signal-to-Noise Ratio (SNR) of the pixels. The SNR may be actual SNR or SNR estimated from brightness levels of the pixels. SNR weighting (e.g., SNR adjustment) may reduce the effect of pixels with high noise on the computed illuminant color value. For example, one or more channel values of the illuminant color value can be determined based on the weightings and color values of the pixels. One or more color gain values can be determined based on the one or more channel values of the illuminant color value and used to white balance the image.

The present invention discloses a path planning method, including the following steps: establishing an empirical map and a corresponding episodic cognitive map using a RatSLAM algorithm based on an episodic memory model; extracting a road edge in a historical memory image with a Canny operator; performing conversion to a world coordinate system from a pixel coordinate system based on the road edge, and preliminarily judging connectivity according to slope of the road edge; continuously injecting energy into the potential path detection network according to continuous observation of a potential path, so as to further judge the road connectivity; fusing the detected potential path and the original episodic cognitive map, and correspondingly updating the empirical map; and planning a path based on the updated episodic cognitive map. The potential safe path in an environment may be detected, and a better path may be planned based on the updated episodic memory model.

Lighting control systems may be commissioned for programming and/or control with the aid of a mobile device. Design software may be used to create a floor plan of how the lighting control system may be designed. The design software may generate floor plan identifiers for each lighting fixture, or group of lighting fixtures. During commissioning of the lighting control system, the mobile device may be used to help identify the lighting devices that have been installed in the physical space. The mobile device may receive a communication from each lighting control device that indicates a unique identifier of the lighting control device. The unique identifier may be communicated by visible light communication (VLC) or RF communication. The unique identifier may be associated with the floor plan identifier for communication of digital messages to lighting fixtures installed in the locations indicated in the floor plan identifier.

An unmanned aerial vehicle (UAV) or drone executes a neural network to assist with detecting and responding to attacks. The neural network may monitor, in real time, the data stream from a plurality of onboard sensors during navigation and may communicate with a high-altitude pseudosatellite (HAPS) platform. For example, if the neural network detects a cyber-attack but determines that it does not interfere with external communications, it may shift navigation control of the drone to the HAPS.

Disclosed are a method for detecting motion information of a target, a device and memory. The method includes: performing target detection on a first image to obtain a detection box of a first target; acquiring depth information of first image in a corresponding first camera coordinate system and determining depth information of the detection box therefrom, and determining first coordinates of first target in first camera coordinate system based on a location of the detection box in an image coordinate system and the depth information thereof; transforming second coordinates of a second target in a second camera coordinate system corresponding to the second image into third coordinates in the first camera coordinate system based on pose change information of an image capturing device; and determining motion information of the first target based on the first and third coordinates. The disclosure avoids abundant computational processing and improves processing efficiency.

Lighting control systems may be commissioned for programming and/or control with the aid of a mobile device. Design software may be used to create a floor plan of how the lighting control system may be designed. The design software may generate floor plan identifiers for each lighting fixture, or group of lighting fixtures. During commissioning of the lighting control system, the mobile device may be used to help identify the lighting devices that have been installed in the physical space. The mobile device may receive a communication from each lighting control device that indicates a unique identifier of the lighting control device. The unique identifier may be communicated by visible light communication (VLC) or RF communication. The unique identifier may be associated with the floor plan identifier for communication of digital messages to lighting fixtures installed in the locations indicated in the floor plan identifier.

A HAPS platform may execute a neural network (a HAPSNN) as it monitors air traffic; the neural network enables it to classify, predict, and resolve events in its airspace of coverage in real time as well as learn from new events that have never before been seen or detected. The HAPSNN-equipped HAPS platform may provide surveillance of nearly 100% of air traffic in its airspace of coverage, and the HAPSNN may process data received from a drone to facilitate safe and efficient drone operation within an airspace.

A robot control system according to an embodiment is a robot control system that controls a mobile robot configured to be able to move autonomously inside a facility. When the mobile robot moves from a first area inside the facility to a second area through a partition configured to be openable and closable, a moving route of the mobile robot is set based on the mobile robot that is determined from an image captured by a first camera that photographs the first area and an obstacle that is determined from an image captured by a second camera that photographs the second area.

A monitoring method includes identifying a monitoring target and a warning object in space according to data collected by a data collection apparatus, obtaining position information of the monitoring target and the warning object, determining a warning area based on the position information of the monitoring target, and generating warning information based on a position relationship between a position of the warning object and the warning area.