Described in detail herein is an automated fulfilment system including a computing system programmed to receive requests from disparate sources for physical objects disposed at one or more locations in a facility. The computing system can combine the requests, and group the physical objects in the requests based on object types or expected object locations. Autonomous robot devices can receive instructions from the computing system to retrieve a group of the physical objects and deposit the physical objects in storage containers.

Systems and methods for automatically identifying and ascertaining an estimated amount of damage at a location by utilizing one or more autonomous vehicles, e.g., “drone” devices, to autonomously capture data of the location and utilizing Artificial Intelligence (AI) logic modules to analyze the captured data and construct a 3-D model of the location.

Disclosed is a coverage path planning method for multiple unmanned surface mapping vehicles, comprising: simultaneously creating submaps and an overall map; outputting its own position information and obstacle information, transmitting to BL.sub.l.sup.i and updating BL.sub.l.sup.m; defining a behavior strategy list (BS); determining the BS with priority for path planning, outputting a to or th state if any criterion is satisfied; when trapped in a local optimum, updating map layers layer-by-layer going upwards, searching for tp in the corresponding layers, performing a BS determination, and outputting a tr instruction; if no target point is found even at the highest layer, checking each CS.sub.P.sub.i∈{FN.sub.i,UFN.sub.i}, and determining a termination. As such, the coverage rate and coverage effect of multiple unmanned surface mapping vehicles in a complex environment can be increased, thus increasing the operational efficiency of the unmanned surface mapping vehicles.

Drone-based inventory management method and systems. One embodiment provides a drone-based inventory management system including one or more unmanned aerial vehicles (UAVs), and a central management system having an electronic processor, and a transceiver configured to communicate with the one or more UAVs. The electronic processor is configured to determine a discrepancy in inventory and select a UAV for verification. The electronic processor is also configured to determine whether weather permits UAV operation and operate the UAV in a pre-determined route when the weather permits UAV operation. The electronic processor is further configured to capture images using the UAV and determine new inventory based on captured images. The electronic processor is also configured to update inventory based on the new inventory.

An information technology system for a distributed manufacturing network includes an additive manufacturing platform configured to manage workflows for a set of distributed manufacturing network entities associated with the distributed manufacturing network. The information technology system includes a set of digital twins generated by the additive manufacturing platform. The information technology system includes an artificial intelligence system configured to be executed by a data processing system in communication with the additive manufacturing platform. The artificial intelligence system is trained to generate process parameters for the workflows managed by the additive manufacturing platform using data collected from the set of distributed manufacturing network entities. The information technology system includes a control system configured to adjust the process parameters during an additive manufacturing process performed by at least one of the set of distributed manufacturing network entities.

A vehicle control device includes a communication unit configured to communicate with a plurality of autonomous driving vehicles configured to perform autonomous traveling, and a processor. The processor is configured to, when an abnormality occurs in or around at least one first autonomous driving vehicle among the plurality of autonomous driving vehicles, determine a travel instruction for controlling traveling of the first autonomous driving vehicle, transmit the travel instruction to the first autonomous driving vehicle via the communication unit, set a priority representing the degree of the priority in which an instruction operator is notified of the travel instruction in the order determined according to the content of the abnormality, notify any one of at least one instruction terminal of the determined travel instruction in the order of highest priority, and receive a result of checking the determined travel instruction from the instruction terminal.

An operation device including: an operation section configured to receive operation by a remote operator conferred with operation authority to operate a vehicle capable of autonomous driving; a communication section configured to transmit, to the vehicle, operation information for remote driving based on the operation received by the operation section; a memory; and a processor coupled to the memory, the processor being configured to: acquire biometric information regarding the remote operator, determine whether or not a compromised state, in which operation of the vehicle by the remote operator is compromised, has arisen based on the acquired biometric information, and transfer operation authority of the vehicle to another remote operator in a case in which the compromised state has been determined to have arisen.

A method of targeting, which involves capturing a first video of a scene about a potential targeting coordinate by a first video sensor on a first aircraft; transmitting the first video and associated potential targeting coordinate by the first aircraft; receiving the first video on a first display in communication with a processor, the processor also receiving the potential targeting coordinate; selecting the potential targeting coordinate to be an actual targeting coordinate for a second aircraft in response to viewing the first video on the first display; and guiding a second aircraft toward the actual targeting coordinate; where positive identification of a target corresponding to the actual targeting coordinate is maintained from selection of the actual targeting coordinate.

An automated package retrieval system is provided. The automated package retrieval system includes a hub apparatus that includes multiple docking stations for multiple delivery devices, a power supply unit coupled to the hub apparatus, and a controller. The controller is configured to instruct at least one of the delivery devices to travel to a location to deliver an item ordered by a user. Once it is determined that the user has not retrieved the item from the delivery device, the delivery device is instructed to return to the hub apparatus. In response to detecting the user in proximity to the hub apparatus after the delivery device has returned to the hub apparatus, the user is provided with access to a storage compartment of the delivery device.

Disclosed herein are methods and systems for operating automated vehicles to accomplish an agricultural mission according to a hierarchically computed mission plan, comprising receiving a plurality of mission parameters defining an agricultural related mission, computing a generic plan for executing the mission by one or more of a plurality of vehicle classes grouping automated vehicles sharing common vehicle parameters coupled with one or more of a plurality of payload classes grouping payloads sharing common payload parameters, computing a plurality of specific plans for executing the mission by one or more automated vehicles of the selected vehicle class(s) coupled with one or more payloads of the selected payload class(s), selecting one of the plurality of specific plans which reduces one or more mission attributes of the mission and outputting instructions for operating the selected automated vehicle(s) and the selected payload(s) to execute the mission according to the selected specific plan.