A mobile cleaning robot configured to clean an environment. The mobile cleaning robot can include a body and a drivetrain operable to move the body within the environment. The robot can include a sensor connected to the body and configured to generate a sensor signal based on interactions between the mobile cleaning robot and the environment. The robot can include an image capture device connected to the body and configured to generate an image stream based on an optical field of view of the image capture device. The robot can include a controller connected to the body and configured to determine whether the mobile cleaning robot is in a stasis condition based on the image stream and the sensor signal. The controller can also update a map of the environment based on the stasis determination.

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.

A controlling method of a cleaning robot is provided. The controlling method includes obtaining lidar data while a lidar sensor rotates multiple times, calculating a reference value which is the average value of distances from a subject according to angles, based on the obtained lidar data, after the reference value is calculated, comparing the obtained lidar data with the reference value, sensing a motion of an object based on a comparison result, and determining that the object is located in a rotation section where the motion has been detected.

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.

There is provided a mobile body that includes an imaging unit to capture an image of an environment around the mobile body, an estimation unit to estimate a position of the mobile body on the basis of the image captured by the imaging unit, a calculation unit 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 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.

Systems and methods use cameras to provide autonomous navigation features. In one implementation, a method for navigating a user vehicle may include acquiring, using at least one image capture device, a plurality of images of an area in a vicinity of the user vehicle; determining from the plurality of images a first lane constraint on a first side of the user vehicle and a second lane constraint on a second side of the user vehicle opposite to the first side of the user vehicle; enabling the user vehicle to pass a target vehicle if the target vehicle is determined to be in a lane different from the lane in which the user vehicle is traveling; and causing the user vehicle to abort the pass before completion of the pass, if the target vehicle is determined to be entering the lane in which the user vehicle is traveling.

Systems and methods provide for estimating a trajectory of a robot by fusing a plurality of robot velocity measurements from a plurality of robot sensors located within a robot to generate a fused robot velocity based on the accuracy of those robot velocity measurements and applying Kalman filtering to the fused robot velocity to compute a current robot location.

According to the embodiments described herein, system and methods for determining relative pose of materials handling vehicles in an industrial environment may include utilizing ultra-wideband (UWB) antenna array systems respective mounted on the materials handling vehicles to send mutually received information to determine the relative pose between the vehicles, determining one or more fields of each materials handling vehicle, and determining one or more overlapping fields between the materials handling vehicles based on the determined one or more fields and the relative pose. A vehicle control may be implemented based on the determined relative pose and the overlapping fields as a field enforcement, such as a control action to avoid collision between the vehicles.

A mobile cleaning robot configured to clean an environment. The mobile cleaning robot can include a body and a drivetrain operable to move the body within the environment. The robot can include a sensor connected to the body and configured to generate a sensor signal based on interactions between the mobile cleaning robot and the environment. The robot can include an image capture device connected to the body and configured to generate an image stream based on an optical filed of view of the image capture device. The robot can include a controller connected to the body and configured to determine whether the mobile cleaning robot is in a stasis condition based on the image stream and the sensor signal. The controller can also update a map of the environment based on the stasis determination.