Systems, methods and apparatuses for inspecting a tank containing a flammable fluid are provided. The system includes a vehicle having a propeller, a latch mechanism, a pressure switch, and an inspection device. The system includes a control unit in communication with the propeller, the latch mechanism, and the inspection device, and electrically connected to the pressure switch. The control unit powers on responsive to the pressure switch detecting an ambient pressure greater than a minimum threshold. The control unit receives, from the latch mechanism, an indication of a state of the latch mechanism. The control unit determines that the cable used to lower the vehicle into the tank containing the flammable fluid is detached from the vehicle. The control unit commands the propeller to move the vehicle through the flammable fluid. The control unit determines a quality metric of a portion of the tank.

An imaging system includes: an unmanned flight vehicle; an imager that is mounted on the unmanned flight vehicle and takes an image of a robot which performs work with respect to a target object; a display structure which is located away from the unmanned flight vehicle and displays the image taken by the imager to a user who manipulates the robot; and circuitry which controls operations of the imager and the unmanned flight vehicle. The circuitry acquires operation related information that is information related to an operation of the robot. The circuitry moves the unmanned flight vehicle such that a position and direction of the imager are changed so as to correspond to the operation related information.

Flight guidance systems and methods that provide an airport selection in response to an EO condition in a single engine plane. The airport selection takes into consideration factors such as optimal approach type, runway length, weather, terrain, remaining battery time, and the like. Additionally, various also generate and display a visual indication of a remaining glide range when the EO condition is happening; the remaining glide range determination is based, at least in part, on terrain.

A system for aiding formation flying of a follower aircraft with respect to a wake vortex from a leader aircraft comprises a controller and at least one accelerometer installed on the follower aircraft. The controller receives acceleration measurements performed by the at least one accelerometer and processes the measurements to obtain a value representative of vibrations generated by the wake vortex from the leader aircraft. The controller compares the value with at least one predetermined threshold representative of excessive vibrations with regard to location of the at least one accelerometer on the follower aircraft. One or more notifications, such as alerts, are generated based on the result of the comparison. It is easier to position the follower aircraft in formation flying to benefit from a rising airflow phenomenon brought about by the wake vortex from the leader aircraft.

Stability of an unmanned aerial vehicle is sought by using a flight controller of an unmanned aerial vehicle control system for controlling flying by an unmanned aerial vehicle based on an instruction from a first operator. A determiner is used to determine whether a second operator visually recognizes the unmanned aerial vehicle based on a predetermined determination method. A switcher is used to switch, based on a result of the determination obtained by the determiner, from a first state, in which the unmanned aerial vehicle flies in accordance with an instruction from the first operator, to a second state, in which the unmanned aerial vehicle flies in accordance with an instruction from the second operator.

A device and method for remotely controlling an unmanned aerial vehicle based on reinforcement learning are disclosed. An embodiment provides a device for remotely controlling an unmanned aerial vehicle based on reinforcement learning, where the device includes a processor and a memory connected to the processor, and the memory includes program instructions that can be executed by the processor to determine an inclination direction corresponding to the hand pose of a user, the movement direction of the hand, and the angle in the inclination direction based on sensing data associated with the pose of the hand or the movement of the hand acquired by way of at least one sensor, and determine one of a movement direction, a movement speed, a mode change, a figural trajectory, and a scale of the figural trajectory of the unmanned aerial vehicle according to the determined inclination direction, movement direction, and angle.

A smart container yard includes systems for intelligently controlling operations of vehicles in the container yard using teleoperation and/or autonomous operations. A remote support server controls remote support sessions associated with vehicles in the container yard to provide teleoperation support for loading and unloading operations. Aerial drones may be utilized to maintain positions above a teleoperated vehicle and act as signal re-transmitters. An augmented reality view may be provided at a teleoperator workstation to enable a teleoperator to control vehicle operations in the smart container yard.

A system for producing a control signal of an electric vertical take-off and landing (eVTOL) aircraft includes a flight controller configured to obtain a requested aircraft force, generate an optimal command mix, wherein the optimal command mix includes a plurality of commands to a plurality of actuators as a function of the requested aircraft force, wherein generating further comprises receiving an ideal actuator model includes at least a performance parameter, producing a model datum as a function of the ideal actuator model, and generating the optimal command mix as a function of the request aircraft force and the model datum, and produce a control signal as a function of the optimal command mix.

An unmanned aerial vehicle (UAV) task cooperation method based on an overlapping coalition formation (OCF) game includes: constructing a sequential OCF game model for a UAV multi-task cooperation problem; using a bilateral mutual benefit transfer (BMBT) order that is biased toward the utility of a whole coalition to evaluate a preference of a UAV for a coalitional structure; optimizing task resource allocation of the UAV under an overlapping coalitional structure by using a preference gravity-guided Tabu Search algorithm to form a stable coalitional structure; and optimizing a transmission strategy based on the current coalitional structure, an updated status of a task resource allocation scheme of the UAV, and a current fading environment, so as to maximize task execution utility of a UAV network. The method quantifies characteristics of resource properties of the UAV and a task, and optimizes the task resource allocation of the UAV under the overlapping coalitional structure.

An exchange network comprising a plurality of exchange stations, in which each of the exchange stations comprises at least one drone operative to function as a flying crane, a temporary storage space, and at least one designated stopping area for on-road vehicles operative to carry cargo. Each of the exchange stations uses the respective local crane-drones to unload cargo from certain vehicles arriving at one of the respective local designated stopping areas, temporary store the cargo, and then load the cargo onboard certain other vehicles arriving at one of the respective local designated stopping areas, thereby exchanging carriers, and thus generating a transport route for the cargo which is the combination of different parts of transport routes of different carriers. The exchange network may use predetermined routes of many scheduled carriers to plan a routing scheme for the cargo, thereby propagating the cargo between exchange stations in a networked fashion.