A lawnmower is instructed to move from a reference point along the boundary wire and to follow the boundary wire along a boundary path back to the reference point, using data from at least one wire sensor of the lawnmower. One or more elements are determined along the boundary path using distance data from at least one distance sensor of the lawnmower and using angular velocity data from at least one direction sensor of the lawnmower. The one or more elements are identified as one of at least three different types of elements. The mowing area is calculated from the identified types of the one or more elements and the distance data and angular velocity data received for the one or more elements. Other important features are obtained from the calculation of the mowing area including, but not limited to, multiple starting points and a parallel mowing pattern.

A robot includes a torso, a head coupled to the torso so as to be rotatable with respect to the torso, and at least one processor. The at least one processor determines whether the torso is inclined from a horizontal direction and, in a case where a determination is made that the torso is inclined from the horizontal direction, controls an actuator to rotate the head with respect to the torso such that the head faces the horizontal direction.

A method for detecting anomalies in a swarm system comprises: collecting first movement data from multiple vehicles moving as a swarm in a first scenario; generating first training data based on positioning data and second training data based on multi-channel inertial sensor data from the first movement data; training a first learning model using the first training data and multiple second learning models using the second training data for each vehicle; receiving real-time second movement data from vehicles moving as a swarm in a second scenario; generating first input data based on positioning data from the second movement data; inputting the first input data into the first learning model to detect abnormal vehicles in real-time; generating second input data for abnormal vehicles based on inertial sensor data from the second movement data; and inputting the second input data into the corresponding second learning model to identify abnormal channels in the inertial measurement unit of abnormal vehicles.

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.

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.