This disclosure provides a monitoring system, a base station, and a control method of drones. The drone includes a battery that supplies electric power for the drone and that connects with a charging connector. The base station includes a charging device, and the charging device includes a power supply connector, a power supply, and a power controller. The power supply connector is used for connecting to the charging connector. The power supply provides electric power. The power controller is coupled to the power supply and the power supply connector. The power controller is used to determine the battery specification of the battery and charge the battery from the power supply according to the battery specification. Thereby, the charging efficiency can be improved and the charging abnormality can be avoided.

Methods and systems for attaching and detaching an item satchel from an autonomous delivery unit. An attachment system includes an attachment system frame, and a satchel comprising a plurality of external pins. The attachment system also includes a plurality of hooks, mechanically attached to the frame, each hook including a first engagement surface and a second engagement surface, one or more electric actuators, mechanically attached to the frame, and mechanically connected to the plurality of hooks. Each of the first engagement surfaces engage a corresponding pin of the plurality of external pins at a first position between the horizontal and vertical positions, and each of the second engagement surfaces engage the corresponding pin of the plurality of external pins at the vertical position to secure an item satchel to an autonomous delivery unit.

Disclosed are systems and methods to determine preferred delivery points within a parcel that are available to receive an aerial delivery of an item. For each delivery point a plurality of criteria scores may be determined for various criteria based on a processing of parcel data, image data, and/or sensor data corresponding to the parcel. The criteria may be aerial navigation related criteria or user preference criteria. The criteria scores may then be used to determine a suitability score for each delivery point. In some implementations, a user may specify a preferred delivery point and/or indicate one user criteria as more important than another criteria.

Aircraft systems and methods that determine, based on location data from a navigation system, whether conditions have been met enabling arming of an approach mode of an autopilot system. The systems and methods, when the one or more conditions enabling arming of the approach mode are determined to be met, start a first timer of a first period of time and, at the same time, provide a first message to alert a pilot to arm the approach mode via manual input to a user interface. When a determination has been made that the approach mode continues to have not been armed via manual input to the user interface, the approach mode is automatically armed.

A system for localizing an autonomous vehicle to a target area can include a position indicator adapted for association with the vehicle in a three dimensional configuration, a detection device configured to detect the position indicator, a computation device configured to compute a position of the vehicle based on the detected position indicator and the relationship of the configuration to the vehicle orientation, a transmitter configured to receive information from the computation device and produce a signal carrying the information, a receiver configured to receive the signal from the transmitter and filter the information therefrom, and a control system configured for association with and control of one or more directional control components of the vehicle, the control being based on the information received from the receiver relating to localizing the vehicle to the target area. A method of for localizing a vehicle to a target area is also disclosed.

A station recognition and a landing method are disclosed. More specifically, an unmanned aerial robot includes a camera sensor configured to capture a first pattern that is marked on a station cover and is used for a station identification and a second pattern that is marked inside a station and is used for a precision landing; a transceiver configured to transmit and receive a radio signal; and a processor functionally connected to the camera sensor and the transceiver, wherein the processor is configured to determine a landing station for landing based on the first pattern captured by the camera sensor, control the transceiver to transmit a radio signal that indicates the landing station to open the station cover, and perform the precision landing at the landing station based on the second pattern of the landing station.

A method, system, and device for planning a path for a forced landing of an aircraft based on image recognition are provided. The method includes: calculating an endurance distance of an aircraft based on sensor data and meteorological information; obtaining an alternative landing area by a satellite image containing contour information and a terrain image recognition model; obtaining a current satellite image of the alternative landing area and determining a landing area; and selecting a landing site by a landing site decision model and generating a path for a forced landing, such that the aircraft completes a forced landing task according to the path for the forced landing. The method, system, and device can automatically recognize image information, select a best landing site, and generate a path for a forced landing to assist a pilot in performing a forced landing task.

A spacecraft including: an engine; a thrust vector control device controlling a thrust vector as a direction of a thrust acting on the spacecraft; and a main control device configured to acquire state quantities of the spacecraft in a powered descending in which the spacecraft is guided to a target point while the engine generates the thrust, and generate a throttling command by which combustion of the engine is controlled and an operation command by which the thrust vector control device is operated. The state quantities contain a first acceleration parameter and a second acceleration parameter. The first and second acceleration parameters are calculated as coefficients A and B obtained by fitting based on acceleration of the spacecraft previously detected, supposing the following equation is satisfied between a reciprocal number 1/a of the acceleration a of the spacecraft and time t:
1/a=−At+B  (1).

Embodiments of the present invention are an unmanned aerial vehicle (UAV) severe low-power protection method and a UAV. The method includes: first acquiring ground environment information when the UAV is in a severe low-power protection state, and then obtaining landing safety judgment information according to the ground environment information, and further controlling a flight state of the UAV according to the landing safety judgment information to realize a safe landing of the UAV. The foregoing method reduces the probability of explosion of the UAV, avoids injury accidents, and improves flight safety when the UAV is in a severe low-power state.

Disclosed are a precise landing method using a multi-pattern in an unmanned aerial control system and an apparatus therefor. In an aspect of the present invention, a precise landing method using a multi-pattern of an unmanned aerial robot in an unmanned aerial control system includes receiving an image value from an outside and recognizing a multi-pattern in which control information for precise landing control has been coded based on the image value, obtaining control information when an ID value included in the control information indicates a landing point, moving to the landing point based on the control information, and performing landing at the landing point, and recognizing the multi-pattern again if the landing is not completed. A landing area for the landing of the unmanned aerial robot may include the landing point and the multi-pattern. The multi-pattern may include a first multi-pattern and a second multi-pattern. The first multi-pattern may have a greater size than the second multi-pattern.