There is provided a technology capable of, when a remote operation target through a remote operation apparatus is switched from one work machine to another work machine, making it easy for an operator to respond to an environmental change of the remote operation target. A first work environment image based on first picked-up image data acquired through a work machine image-pickup device 412 of a first work machine which is a current remote operation target of a remote operation apparatus 20 is outputted on a remote output interface 220. In addition, a second work environment image based on second picked-up image data acquired through a work machine image pickup apparatus of a second work machine which is the next remote operation target of the remote operation apparatus 20 is outputted on the remote output interface 220.

A method carries out a remote-controlled parking maneuver with a vehicle. A user with a mobile terminal is located outside the vehicle. The method plans a trajectory for the parking maneuver by way of a driver assistance system of the vehicle, maneuvers the vehicle along the planned trajectory by the driver assistance system, detects image data which describes the vehicle and the surroundings of the vehicle by a camera of the mobile terminal, and provides a display on a display device of the mobile terminal using the image data. The maneuvering process along the planned trajectory is carried out as long as an operating input which is carried out by the user on the mobile terminal and which describes a movement of the vehicle during the maneuvering process along the planned trajectory is detected by way of the mobile terminal, otherwise the maneuvering process is interrupted.

Currently teleconferencing robotic systems available are not smart and unable to navigate based on specific path and fail to focus on presenter based on overall environment. This disclosure relates to method of dynamic localization of a telepresence robot based on plurality of live markers. A plurality of images is received from an image capturing device connected to the telepresence robot. The plurality of images is processed to identify the plurality of live markers in a path of the telepresence robot. A binary matrix is decoded to identify at least one identifier (ID) associated with the at least one live marker from the plurality of live markers. A plurality of parameters is identified based on the at least one ID associated with the at least one live marker. A further path is dynamically localized to navigate the telepresence robot based on the plurality of parameters and the plurality of live markers.

A flight aiding method for an unmanned aerial vehicle includes receiving a receiving, from a mobile terminal that controls the unmanned aerial vehicle, a flight aiding instruction to execute a flight aiding function. The flight aiding method further includes in response to receiving the flight aiding instruction, controlling, regardless of a head direction that a head of the unmanned aerial vehicle is pointing, the unmanned aerial vehicle to fly by controlling both a velocity of the unmanned aerial vehicle along a reference direction and a velocity of the unmanned aerial vehicle perpendicular to the reference direction. The reference direction is defined for the unmanned aerial vehicle based on a position of a point of interest and a current location of the unmanned aerial vehicle.

A system 100 for remote assistance of an autonomous agent can include and/or interface with any or all of: a sensor suite 110, a computing system 120, a communication interface 130, and/or any other suitable components. The system can further optionally include a set of infrastructure devices 140, a teleoperator platform 150, and/or any other suitable components. The system 100 functions to enable information to be exchanged between an autonomous agent and a tele-assist. Additionally or alternatively, the system 100 can function to operate the autonomous agent (e.g., based on remote inputs received from a teleoperator, indirectly, etc.) and/or can perform any other suitable functions.

Embodiments of the present disclosure may include a method to augment pilot control of a drone, the method including receiving a planned flight route. Embodiments may also include receiving sensor information from an at least one environment sensor along the planned flight route. In some embodiments, the at least one environment sensor may be located at a predefined location. Embodiments may also include estimating a drone location from the sensor information. Embodiments may also include receiving a speed vector of the drone. Embodiments may also include comparing the drone location to an expected drone location along the planned flight route. Embodiments may also include deriving a flight control command and a speed vector command to return the drone to a point along the planned flight route.

The present invention provides a system and method of automatic detection of structure integrity threats. A threat detection engine detects integrity threats in structures, such as underwater structures, and segments the structures in an image using convolutional neural networks (“CNN”). The threat detection engine may include a dataset module, a CNN training module, a segmentation map module, a semi-supervision module, and an efficiency module. The threat detection engine may train a deep learning model to detect anomalies in videos. To do so, a dataset module with videos may be used where the dataset module includes annotations detailing at what timestamps one or more anomalies are visible.

A vehicle that is an automatic driving vehicle acquires a control program for an ECU from a control center by wireless communication. A server includes a keyboard and mouse to accept handling by a remote monitoring person that monitors the vehicle from the outside of the vehicle, a display to present information to the remote monitoring person, a communication IF configured to communicate with the vehicle, and a processor. The processor controls the display such that the server presents the change content of the control program to the remote monitoring person, when the control program for the vehicle has been updated. The processor controls the communication IF such that a notice of permission of traveling is given to the vehicle, when the keyboard and the mouse have accepted a remote monitoring person's handling for permitting the vehicle to travel in accordance with the control program after the update.

Implementations of the disclosed subject matter provide a mobile robot that moves within an area and captures image data by an image sensor. A position of text, an image, and/or video on a display screen of a display mounted to the mobile robot may be adjusted based on the image data captured by the mobile robot that includes one or more persons that are within the area. The text, the image, and/or the video may be at the adjusted position in the display screen of the display and audio via a speaker of the mobile robot to the one or more persons based on their heights, eye level, whether they are seated, or the like.

A control device that controls a work vehicle including work equipment includes: a route acquisition unit that acquires a traveling route of a transport vehicle; an area setting unit that sets a limit area for limiting entry of the work equipment along the traveling route; and a signal output unit that outputs a signal for controlling the work vehicle or the transport vehicle on the basis of a relationship between the limit area and the work equipment.