A dynamic ground distance training system and method of training for pilots including one or more emitters facing downward direct signal of focused energy (or sound and/or light) towards the runway and said focused energy signal is reflected from the runway; the reflected signal is received by one or more receivers positioned generally downward; and, the energy signal is processed via microprocessor(s) and a distance based on the time intervals when sampling signal reflections is determined.

An aerial vehicle includes a communication unit configured to receive a wireless signal from a geo-fencing device, and a flight controller configured to generate one or more control signals that cause the aerial vehicle to operate in accordance with a set of flight regulations generated based on the wireless signal. The geo-fencing device is configured not for landing of the aerial vehicle. The set of flight regulations includes rules for controlling at least one of the aerial vehicle, a carrier carried by the aerial vehicle, or a payload of the aerial vehicle.

A method is provided for detecting and avoiding conflict along a current route of a robot. The method includes accessing a trajectory of the robot on the current route of the robot, and a predicted trajectory of a nearby moving object, and from the trajectory and predicted trajectory, detecting a conflict between the robot and the nearby moving object. Alternate routes for the robot are determined, each of which includes an alternative route segment offset from the current route, and a transition segment from the current route to the alternative route segment. Routes including the current and alternative routes are evaluated according to a cost metric, and a route from the routes is selected for use in at least one of guidance, navigation or control of the robot to avoid the conflict.

This disclosure relates to apparatuses, systems, and methods for controlling an aircraft. A computing system may identify a first command signal received via a flight control at a time point to control navigation of the aircraft through an environment. The computing system may attenuate the first command signal using a fade function over a time window relative to the time point to generate a second command signal. The computing system may input the second command signal to a model to generate predicted paths for the aircraft through the environment over the time window. The computing system may determine that at least one predicted path intersects with an obstacle in the environment during the time window. The computing system may generate a location to which to navigate the aircraft to avoid the obstacle. The computing system may perform an action to direct the aircraft to the location.

A method and a system for establishing a route of an unmanned aerial vehicle are provided. The method includes identifying an object from surface scanning data and shaping a space, which facilitates autonomous flight, as a layer, collecting surface image data for a flight path from the shaped layer, and analyzing a change in image resolution according to a distance from the object through the collected surface image data and extracting an altitude value on a flight route.

A task processing method includes obtaining a task data loading request; and searching for target task data in a task database according to the task data loading request, where the task database stores task data corresponding to one or more tasks, and the target task data includes a coordinate of a waypoint of a target route of a target task. The method further includes controlling a movable object to reproduce the target task corresponding to the target task data. Controlling the movable object to reproduce the target task includes controlling the movable object to move according to the target route corresponding to the target task data.

Techniques for facilitating an autonomous operation, such as an autonomous navigation, of an unmanned vehicle based on one or more fiducials. For example, image data of a fiducial may be generated with an optical sensor of the unmanned vehicle. The image data may be analyzed to determine a location of the fiducial. A location of the unmanned vehicle may be estimated from the location of the fiducial and the image. The autonomous navigation of the unmanned vehicle may be directed based on the estimated location.

A method is provided for supporting a robot in response to a contingency event. The method includes detecting the contingency event during travel of the robot on a route to a destination. In response, the method includes determining a position of the robot, and accessing information about alternate destinations associated with the route. The method includes selecting an alternate destination from the alternate destinations based on a time to travel from the position of the robot to the alternate destination, and the information. And the method includes outputting an indication of the alternate destination for use in at least one of guidance, navigation or control of the robot to the alternate destination.

Methods and systems are provided for assisting operation of an aircraft when diverting from a flight plan using a comparative vertical profile display. A vertical profile display includes a first graphical representation of a first vertical profile corresponding to a first lateral route defined by a flight plan for the aircraft and a second graphical representation of a second vertical profile corresponding to a modified lateral route different from the first lateral route displayed concurrently on the vertical profile display. The first vertical profile corresponding to the first lateral route is depicted on the vertical profile display in a first plane and the second vertical profile corresponding to the modified lateral route is depicted on the vertical profile display in a second plane different from the first plane.

A response system may be provided. The response system may include an autonomous drone. The autonomous drone may include a processor, a memory in communication with the processor, and a sensor. The processor may be programmed to build a virtual map of a coverage area, store the virtual map in the memory, receive a deployment signal, deploy the drone in response to the deployment signal, control movement of the drone within the coverage area using the virtual map, collect sensor data of the coverage area using the sensor, and/or analyze the sensor data to generate an inventory list of the coverage area, the inventory list including a personal article within the coverage area.