In one embodiment, the invention can be a system for monitoring a product, system including a transponder configured to attach to a product in a store; a drone comprising a camera, the drone configured to wirelessly communicate with the transponder; follow the transponder to maintain the wireless communication with the transponder; utilizing the camera, take a photograph or a video of the product to which the transponder is attached; and transmit a wireless alert signal upon an indication of an excessive separation between the drone and the transponder; and an employee device having a user interface, the user interface configured to display an alert when the drone sends the wireless alert signal.

A system for controlling operation of unmanned assets. The system includes a graphical user interface displaying a map. The graphical user interface accepts graphical inputs drawn on the map. The system also includes a calculation unit having an input interpretation module that recognizes the graphical inputs and translates the graphical inputs into tasks and/or commands. The calculation unit also includes an operation planning module that generates operation instructions based on the tasks and/or commands; and a journey planning module that generates a journey plan for the unmanned assets based on the operation instructions. The system also includes a communications module that communicates with the unmanned assets to instruct the unmanned assets to operate according to the generated journey plan.

Systems and methods for use of autonomous mobile machine for blind spot coverage may include a security controller that determines a security coverage area based on data from a facility map indicating physical and functional representations of a facility and objects within the facility. The security controller may also determine a surveillance area based on the security coverage area. The security controller may also transmit, to an autonomous mobile machine, instructions for the autonomous mobile machine to deploy to the surveillance area and perform a security task at the surveillance area.

A robot control method, including: determining a target operation map according to target position information of a current operation of a robot; determining, based on a pre-established map connection relationship, a transfer position that the robot moves to; controlling the robot to move to the transfer position and switching maps, until the robot moves to the target operation map; and controlling the robot to move to an operation destination according to map information of the target operation map and the target position information.

Example implementations include a method, apparatus, system, and computer-readable medium of using a robot to validate a license plate recognition check at a parking facility, comprising identifying license plate information of a license plate. The implementations further include associating the license plate information with a parking spot. Additionally, the implementations further include storing the license plate information in association with the parking spot. Additionally, the implementations further include checking the license plate information in association with the parking spot against a parking facility monitoring system.

An apparatus and method for monitoring illegally parked/stopped vehicles using a movable camera device or a fixed camera device are provided. In an example of the monitoring method, the monitoring apparatus receives location information of a plurality of parking/stopping monitoring areas and generates a patrol route for a movable camera device to collect captured images based on the location information of the parking/stopping monitoring areas. The monitoring apparatus provides the patrol route to the movable camera device, receives, from the movable camera device, first captured images of the parking/stopping monitoring areas included in the patrol route and second captured images of the parking/stopping monitoring areas after a specified time has elapsed, and determines that a vehicle commonly detected in the first and second captured images is an illegally parked/stopped vehicle.

A computer generates historical time and velocity data for vehicles based on data from sensor(s) observing the vehicles. The computer determines, based on the historical time and velocity data, a control policy that controls movement of the vehicles. The control policy is represented as a weighted combination of a set of predefined policies. Determining the control policy comprises calculating weights or parameters for a weighted combination of the set of predefined policies that minimizes a residual error term. The residual error term is computed based on a difference between the historical time and velocity data and predicted time and velocity data associated with the weighted combination of the set of predefined policies. The computer determines an action plan based on the determined nonlinear control policy. The computer transmits, to a machine, a control signal causing the machine to perform or simulate at least a part of the determined action plan.

The present invention discloses a method for adaptively role-selection in coordinated multi-robot search task and system thereof, comprising: defining a role action space as two discrete values: [explore, cover]; acquiring and inputting local perception information o.sub.t.sup.i and joint perception information jo.sub.t.sup.i into a role policy, and outputting a role action .sub.t.sup.i; wherein, the local perception information comprises an obstacle map, an explored map, a covered map, and a position map; and, the joint perception information comprises a merged explored map and a merged covered map; inputting the local perception information o.sub.t.sup.i and the output role action .sub.t.sup.i into a primitive policy, and outputting a primitive action a.sub.t of robot to interact with the environment; then, a robot is controlled to execute corresponding output primitive action a.sub.t according to received specific role action .sub.t.sup.i.

A method for guiding an autonomous aircraft, the aircraft includes an automatic pilot, a plurality of sensors and an imaging unit, the aircraft being configured to fly over a geographic zone comprising overflight prohibited zones, the guidance method can advantageously comprise a phase of real flight of the autonomous aircraft by using a given guidance law, comprising the following steps: determining a current state of the autonomous aircraft; determining an optimum action to be executed by using a neural network receiving the current state; determining a plurality of control instructions compatible with the guidance law based on the optimum action to be executed; transmitting to the automatic pilot the plurality of control instructions, which provides a new state of the autonomous aircraft.

A distributed sensor network includes a first plurality of cooperatively acting unmanned autonomous vehicles, UAVs, spatially distributed to create a domain exclusion zone, DEZ. Each of the UAVs includes one or more first sensors configured to gather detection signals from any object entering the DEZ, a signal processor connected to the one or more first sensors and configured to process the detection signals gathered by the one or more first sensors, to perform object classification, discrimination, and identification, CDI, algorithms on the detection signals, and to output a CDI signal related to the object, and one or more communication modules coupled to the signal processor and configured to transmit the CDI signal to other UAVs in the first plurality of cooperatively acting UAVs.