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.

A self-moving device, including: a moving module, a task execution module, a control module. The control module is electrically connected to the moving module and the task execution module, controls the moving module to actuate the self-moving device to move, controls the task execution module to execute a working task. The self-moving device further includes a satellite navigation apparatus, electrically connected to the control module and configured to receive a satellite signal and output current location information of the self-moving device. The control module determines whether quality of location information output by the satellite navigation apparatus at a current location satisfies a preset condition, controls, if the quality does not satisfy the preset condition, the moving module to actuate the self-moving device to change a moving manner, to enable quality of location information output by the satellite navigation apparatus at a location after the movement to satisfy the preset condition.

Aerial vehicles, their structures and methods of locomotion are described. An aerial vehicle may include a fuselage having an x-axis, a plurality of flexible structures emanating from the fuselage that take the form of a feather, wing and/or tentacle, at least one motor, and at least one propeller driven by one or more motors. Each flexible structure may extend from a fuselage in any direction and are used to enhance the observability of the aircraft by moving and/or oscillating within a frequency band and at a magnitude that is more easily observed by and catches the human eye.

A system for a boat includes a first guideway installed under a main deck of the boat, a first battery pack coupled to the first guideway under the main deck, and a first actuator configured to move the first battery pack along the first guideway. A controller is electrically and/or signally coupled with the first actuator. A user input device is electrically and/or signally coupled with the controller. The controller is configured to control the first actuator to move the first battery pack along the first guideway in response to an input to the user input device so as to relocate the weight of the first battery pack under the main deck.

A method for determining a motion path on a surface in an environment, along which motion path a mobile appliance, in particular a robot, preferably a domestic robot or a robot vacuum cleaner, is intended to move. The method includes obtaining environment information and determining a region of the surface intended to be covered by the motion of the mobile appliance; determining, while taking into account the environment information, whether within the region there is at least one uneven area in the surface that can be negotiated by the mobile appliance; and determining the motion path while taking into account the at least one uneven area, if there is one. A mobile appliance is also described.

Device, system, and method for Skydiving Robots? which can skydive using customized or off-the-shelf parachutes and deliver civilian or military payloads. The Skydiving Robots can freefall, open the parachute and steer toward the target, carry payloads, operate in the daytime or the pitch black at night using GPS guidance to land precisely. If they exited the plane at up to or over 30,000 feet above ground level (AGL) the final target could be miles away. They are the ideal reconnaissance scouts with a wide array of sensors such as cameras. They can carry payloads and precisely land within a few feet of a target.

The present disclosure relates to a method for straight edge detection by a robot and a method for reference wall edge selection by a cleaning robot. The method for straight edge detection by the robot includes that: position coordinates of detection points are determined according to distance values detected by a distance sensor of the robot and angle values detected by an angle sensor of the robot, and then a final straight edge is determined according to a slope of a straight line formed by adjacent two of the detection points.

Some embodiments relate to a manned vertical take-off and landing (VTOL) aerial vehicle (AV) and to methods relating to such VTOL AVs. An example vehicle comprises: a body comprising a cockpit; a propulsion system carried by the body to propel the body during flight; pilot-operable controls accessible from the cockpit; a sensing system configured to generate sensor data associated with a region around the manned VTOL AV; a control system configured to enable control of the manned VTOL AV to be shared between a pilot and an autonomous piloting system, wherein the control system may utilise the sensor data; and a three-dimensional model of the region; and program instructions to: determine a state estimate and a state estimate confidence metric; generate a three-dimensional point cloud of the region; generate a plurality of virtual particles within the three-dimensional model; compute a plurality of scores, each score being associated with one of the plurality of virtual particles; and update the state estimate based at least in part on the computed scores, thereby determining an updated state estimate.

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.

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.