A control system includes at least one memory storing program code and at least one processor. The program code is configured to cause the at least one processor to estimate size of a short visibility airspace, based on a location of a first point at which an aircraft detects the short visibility airspace, a location of a second point at which the aircraft determines that the aircraft has entered the short visibility airspace, and sensing information acquired at the first point. The program code also causes the at least one processor to perform control to cause the aircraft to move to a safe airspace that is set based on the estimated size of the short visibility airspace when it is determined that flight continuation along a route passing through the short visibility airspace is impracticable.

A control system includes at least one memory storing program code and at least one processor. The program code is configured to cause the at least one processor to estimate size of a short visibility airspace, based on a location of a first point at which an aircraft detects the short visibility airspace, a location of a second point at which the aircraft determines that the aircraft has entered the short visibility airspace, and sensing information acquired at the first point. The program code also causes the at least one processor to perform control to cause the aircraft to move to a safe airspace that is set based on the estimated size of the short visibility airspace when it is determined that flight continuation along a route passing through the short visibility airspace is impracticable.

Disclosed herein are embodiments for providing contingency automation to emergency response. One embodiment of a method includes determining a route for a vehicle to a primary destination, and determining a first upcoming area along the route that has been previously identified as an emergency response zone, where the emergency response zone may be used as a secondary destination in a contingency route for the vehicle in an emergency. In some embodiments, the method may include reserving airspace for the contingency route, determining that the emergency response zone is no longer useful, based on an updated position of the vehicle, and canceling the contingency route from reservation.

A hull behavior control system for controlling behavior of a hull of a marine vessel includes a memory and at least one controller coupled to the memory. The at least one controller is configured or programmed to control a steering that changes the traveling direction of the marine vessel, obtain a water surface shape around the marine vessel, estimate movement of a wave based on the water surface shape, and when it is determined that the hull rides the wave whose movement has been estimated, control the steering so as to reduce an influence of the wave on the hull.

A hull behavior control system for controlling behavior of a hull of a marine vessel includes a memory and at least one controller coupled to the memory. The at least one controller is configured or programmed to control a steering that changes the traveling direction of the marine vessel, obtain a water surface shape around the marine vessel, estimate movement of a wave based on the water surface shape, and when it is determined that the hull rides the wave whose movement has been estimated, control the steering so as to reduce an influence of the wave on the hull.

A controller includes a control unit. The control unit is configured to detect a state of at least one point, and determine depending on the detected state whether or not to include, in a flight route of a flying object transporting a package, a position above the at least one point as a passing point for the flying object to pass.

Examples relate to a method for generating an Unmanned Aerial Vehicle (UAV) controller model for controlling an UAV, a system including an UAV, a wind generator, a motion-tracking system and a control module, and to an UAV. The method for training the UAV controller model includes providing a wind generator control signal to a wind generator, to cause the wind generator to emit a wind current towards the UAV. The method includes operating the UAV using the UAV controller model. A flight of the UAV is influenced by the wind generated by the wind generator. The method includes monitoring the flight of the UAV using a motion-tracking system to determine motion-tracking data. The method includes training the UAV controller model using a machine-learning algorithm based on the motion-tracking data.

Systems and methods for mission-based path modifications are presented herein. One or more processors may be coupled with memory and housed in a vehicle. The one or more processors may receive data indicative of an issue with at least one function of the vehicle during a mission defined by a type of cargo and a flight path comprising a plurality of segments. The one or more processors may determine, responsive to the issue with the at least one function, an action to perform for the vehicle based on the issue, a current segment of the plurality of segments, and the mission. The one or more processors may execute, during the current segment or a subsequent segment of the plurality of segments, the action on the vehicle.

An aircraft system of an aircraft may, while the aircraft is flying in a first flight mode, receive data from one or more sensors of the aircraft and, based on received data from one or more sensors, identify an actuation failure of an actuator of a flight control surface. The vehicle system may, subsequent to identifying the actuation failure of the actuator of the flight control surface, control the aircraft to fly in a modified flight mode. Controlling the aircraft to fly in the modified flight mode may include: determining a directional bias caused by the actuation failure, determining a modified flight plan based on the determined directional bias, and controlling the aircraft to fly based on the modified flight plan and the directional bias.

A drone (an aerial vehicle), able to maintain improved safety for operation by non-specialists, is provided. A farm field data stored in a cloud at take-off is compared to an environment data read by a sensor, and a control to prohibit take-off is performed if any danger is considered. In particular, it is desirable to prohibit if there is a traffic, where people and cars may pass, between the farm field and a current location, and if a direction of the drone, installed, does not point to a direction of an intrusion pathway to the target farm field. Furthermore, it is desirable to prohibit take-off if a predetermined maintenance is not performed by referring to a maintenance history.