A method implemented by a processor associated with a movable object includes receiving a first parameter value from a user interface communicatively coupled to the movable object; determining a horizontal acceleration based on the first parameter value, wherein the horizontal acceleration is substantially zero when the first parameter value is zero; and controlling the movable object to accelerate or decelerate in accordance with the determined horizontal acceleration.

A monitoring method includes identifying a monitoring target and a warning object in space according to data collected by a data collection apparatus, obtaining position information of the monitoring target and the warning object, determining a warning area based on the position information of the monitoring target, and generating warning information based on a position relationship between a position of the warning object and the warning area.

Systems, methods, and devices are provided herein for controlling one or more movable objects via a graphical user interface. A method for controlling a movable object may be provided. The method may comprise obtaining one or more parameters of a target object, and generating a motion path for the movable object based on the one or more parameters of the target object. The motion path may comprise a plurality of spatial points that are defined relative to the one or more parameters of the target object. The plurality of spatial points may be configured to be on one or more planes.

A system and method is disclosed for configuring a group of mobile field devices using a configuration device (an HMI) is provided. In particular, the HMI is programmed to configure identically programmed field devices that are arbitrarily arranged in an application-dependent formation by defining and providing configuration parameters to the devices via wired and/or wireless communication. In particular, the HMI assigns a unique identifier to respective robots as a function of the position of the robot within the formation or the layout of the environment. Accordingly each robot can be efficiently configured by the HMI to operate independently yet as a coordinated member of the group and without requiring the robots to be placed in specific positions during the initial deployment. This obviates the need for constant independent control commands for each robot by a central controller or providing a customized control program to each robot during deployment.

This specification discloses a marker allocation method and apparatus in an unmanned aerial vehicle airport and an unmanned aerial vehicle landing method and apparatus. According to an airport shape and an airport size of an unmanned aerial vehicle airport and a standard shape and a standard size of a takeoff and landing point, a target layout of an unmanned aerial vehicle airport that includes multiple takeoff and landing points is determined. Further, an initial takeoff and landing point is determined from the multiple takeoff and landing points included in the target layout. Markers respectively allocated to the multiple takeoff and landing points are determined from a predetermined marker set that includes markers of different image content, by using the initial takeoff and landing point as a start point, according to a predetermined search algorithm, and with a constraint that similarity between a marker of any one of the multiple takeoff and landing points and markers of other takeoff and landing points in a specified neighborhood thereof is the lowest. In this method, a position and a correspondence between each takeoff and landing point and each marker in a range of the airport do not need to be manually determined, thereby improving efficiency of allocating a marker to an unmanned aerial vehicle.

An unmanned device control method and apparatus, a storage medium, and an electronic device. An unmanned device is controlled to move according to a preplanned target path; current environment information of the unmanned device is obtained; according to the current environment information of the unmanned device, a target subpath on which the unmanned device is located is determined, from target subpaths included in the target path, as a designated subpath; and a control strategy is then determined according to a scenario type corresponding to the designated subpath, and a determined control strategy is used to control the unmanned device.

Cellular devices such as smartphones, smart watches, tablets, phones that are not smart and any form of devices that use cellular communications can be located using cellular triangulation. Current commercial cellular communication devices provide accurate information within a range of 10 or more meters. Also, sometimes due to improper coverage of an area by a commercial cellular network, the location information would be bad to zero. Also in some emergency situations, cellular towers may be destroyed or malfunction or cannot be used. In case of emergency situations, if emergency personnel use drones enabled with cellular technology from a close range using triangulation, this can provide highly accurate location information of the victims and can also help communicate with the victim and help them out of danger in a safe manner.

The present application provides an unmanned aerial vehicle (UAV)-assisted federated learning resource allocation method for an UAV-assisted federated learning wireless network scenario, which takes into account the effect of altitude of the UAV on the coverage range in order to achieve an equilibrium between the total energy consumption of the user and federated learning performance. The method simultaneously considers the total energy consumption of the user and the federated learning performance, defines the total cost function of the system. The total cost function consists of weighting of the total energy consumption of the user and the inverse of the number of users participating in federated learning, and forms the optimization problem with a minimization of the total cost function.

A method for controlling a movable platform includes obtaining a trajectory of the movable platform and controlling the movable platform to move along the trajectory, obtaining a reference sensing orientation of a sensing apparatus of the movable platform, and adjusting the sensing orientation of the sensing apparatus from the reference sensing orientation to a target sensing orientation. When a sensing orientation of the sensing apparatus is the reference sensing orientation, first one or more trajectory points between a current location of the movable platform and a first location are within a sensing range of the sensing apparatus, and second one or more trajectory points after the first location are outside the sensing range of the sensing apparatus. When the sensing orientation of the sensing apparatus is the target sensing orientation, the first one or more trajectory points and the second one or more trajectory points are within the sensing range.

A return method or device for an unmanned aerial vehicle (UAV), a UAV and a storage medium are provided. The method includes: detecting whether a sensor for obstacle avoidance fails; if the sensor fails, determining a return path of the UAV based on a first return strategy; if the sensor operates normally, determining the return path of the UAV based on a second return strategy; the first return strategy includes controlling the UAV to fly to a return altitude; the second return strategy includes determining the return path of the UAV based on detection data from the sensor. The combination of these two return strategies can achieve a balance between the return efficiency and safety of the UAV.