Abstract: An autonomous mobile device includes a processor to establish a Transmission Control Protocol Robot Operating System (TCPROS) connection over a Protocol Data Unit (PDU) session between the AMD and a Multi-Access Edge Computing (MEC) device that hosts an AMD controller in a cellular network, from the AMD the AMD controller. The processor may perform a mission in accordance with the mission plan while the AMD is connected to the AMD controller via the TCPROS connection.

Abstract: A device may include a memory storing instructions and processor configured to execute the instructions to receive information relating to a plurality of vehicles in an area. The device may be further configured to use a trained machine learning model to determine a likelihood of collision by one or more of the plurality of vehicles; identify one or more relevant vehicles of the plurality of vehicles that are in danger of collision based on the determined likelihood of collision; and send an alert indicating the danger of collision to at least one of the identified one or more relevant vehicles.

Abstract: A device may receive images identifying interiors of buildings and movable objects and unmovable objects located in the interiors, and may train a machine learning model with the images to generate a trained machine learning model. The device may receive an image identifying an interior portion of a building and objects located in the interior portion, and may process the image, with the trained machine learning model, to identify a movable object and an unmovable object. The device may disregard the movable object to generate an image in which data identifying the movable object has been disregarded, and may process the image in which the data identifying the movable object has been disregarded, with a visual positioning system, to determine a location of the user device in the interior portion of the building. The device may perform one or more actions based on the location of the user device.

Abstract: In some implementations, a device may receive spatial data corresponding to an interior of a building and objects located in the interior of the building. The device may generate a digital map of the interior of the building based on the spatial data. The device may generate a geographic coordinate map of the interior of the building. The device may receive, from a sensor mounted on an uncrewed aerial vehicle (UAV), sensor data indicating three-dimensional geographic points. The device may compare the sensor data to the geographic coordinate map to localize the UAV on the geographic coordinate map. The device may generate coordinate data indicating geographic coordinates associated with the geographic coordinate map and formatted in a global positioning system coordinate format and a National Marine Electronics Association format. The device may transmit, to a controller of the UAV, at least a subset of the coordinate data.

Abstract: A device may receive, from a vehicle device, a geographical (e.g., GNSS) location of a vehicle, and may utilize the GNSS location as a determined location of the vehicle when the GNSS location satisfies a first threshold. The device may receive, from the vehicle device, an image identifying reference points associated with the vehicle, and may process the image, with a VPS, to calculate a VPS location of the vehicle. The device may utilize the GNSS location of the vehicle as the determined location when the VPS location fails to satisfy a second threshold, and may calculate, when the VPS location of the vehicle satisfies the second threshold, coordinate sets based on groups of coordinate combinations from the GNSS location and the VPS location. The device may process the coordinate sets, with a model, to determine the determined location, and may perform actions based on the determined location.

Abstract: A device may include a memory storing instructions and processor configured to execute the instructions to receive information relating to a plurality of vehicles in an area. The device may be further configured to use a trained machine learning model to determine a likelihood of collision by one or more of the plurality of vehicles; identify one or more relevant vehicles of the plurality of vehicles that are in danger of collision based on the determined likelihood of collision; and send an alert indicating the danger of collision to at least one of the identified one or more relevant vehicles.

Abstract: A device may include a memory storing instructions and processor configured to execute the instructions to receive information relating to a plurality of vehicles in an area. The device may be further configured to use a trained machine learning model to determine a likelihood of collision by one or more of the plurality of vehicles; identify one or more relevant vehicles of the plurality of vehicles that are in danger of collision based on the determined likelihood of collision; and send an alert indicating the danger of collision to at least one of the identified one or more relevant vehicles.

Abstract: A device can be configured to receive flight data associated with an unmanned aerial vehicle (UAV), at least some of the flight data being received from the UAV; store the flight data; receive, from a requesting device, a flight data request, the flight data request including information identifying the UAV or a flight of the UAV; determine response data based on the flight data request and based on an entity associated with the requesting device, the response data including a subset of the flight data, which is available to be accessed by the entity; and perform an action associated with the response data.

Abstract: In some implementations, a device may receive status information for robotic devices. The status information may include locations and navigation plans of the robotic devices. The device may monitor, based on the status information, individual statuses of the robotic devices. The device may receive a mission request associated with performance of an operation. The device may select, based on the individual statuses, a first robotic device to perform the operation. The device may determine, based on a mapping of an environment, potential navigation plans associated with the first robotic device traversing the environment according to the operation. The device may select a navigation plan based on the locations and the navigation plans of the robotic devices. The device may stream navigation instructions associated with the selected navigation plan to the first robotic device to cause the first robotic device to traverse the environment according to the navigation plan.

Abstract: A device may receive images identifying interiors of buildings and movable objects and unmovable objects located in the interiors, and may train a machine learning model with the images to generate a trained machine learning model. The device may receive an image identifying an interior portion of a building and objects located in the interior portion, and may process the image, with the trained machine learning model, to identify a movable object and an unmovable object. The device may disregard the movable object to generate an image in which data identifying the movable object has been disregarded, and may process the image in which the data identifying the movable object has been disregarded, with a visual positioning system, to determine a location of the user device in the interior portion of the building. The device may perform one or more actions based on the location of the user device.

Abstract: A device may receive images identifying interiors of buildings and movable objects and unmovable objects located in the interiors, and may train a machine learning model with the images to generate a trained machine learning model. The device may receive an image identifying an interior portion of a building and objects located in the interior portion, and may process the image, with the trained machine learning model, to identify a movable object and an unmovable object. The device may disregard the movable object to generate an image in which data identifying the movable object has been disregarded, and may process the image in which the data identifying the movable object has been disregarded, with a visual positioning system, to determine a location of the user device in the interior portion of the building. The device may perform one or more actions based on the location of the user device.

Abstract: A device may receive images identifying interiors of buildings and movable objects and unmovable objects located in the interiors, and may train a machine learning model with the images to generate a trained machine learning model. The device may receive an image identifying an interior portion of a building and objects located in the interior portion, and may process the image, with the trained machine learning model, to identify a movable object and an unmovable object. The device may disregard the movable object to generate an image in which data identifying the movable object has been disregarded, and may process the image in which the data identifying the movable object has been disregarded, with a visual positioning system, to determine a location of the user device in the interior portion of the building. The device may perform one or more actions based on the location of the user device.

Abstract: A device may include a memory storing instructions and processor configured to execute the instructions to receive information relating to a plurality of vehicles in an area. The device may be further configured to use a trained machine learning model to determine a likelihood of collision by one or more of the plurality of vehicles; identify one or more relevant vehicles of the plurality of vehicles that are in danger of collision based on the determined likelihood of collision; and send an alert indicating the danger of collision to at least one of the identified one or more relevant vehicles.

Abstract: A device may receive, from a vehicle device, a geographical (e.g., GNSS) location of a vehicle, and may utilize the GNSS location as a determined location of the vehicle when the GNSS location satisfies a first threshold. The device may receive, from the vehicle device, an image identifying reference points associated with the vehicle, and may process the image, with a VPS, to calculate a VPS location of the vehicle. The device may utilize the GNSS location of the vehicle as the determined location when the VPS location fails to satisfy a second threshold, and may calculate, when the VPS location of the vehicle satisfies the second threshold, coordinate sets based on groups of coordinate combinations from the GNSS location and the VPS location. The device may process the coordinate sets, with a model, to determine the determined location, and may perform actions based on the determined location.

Abstract: A device can be configured to receive flight data associated with an unmanned aerial vehicle (UAV), at least some of the flight data being received from the UAV; store the flight data; receive, from a requesting device, a flight data request, the flight data request including information identifying the UAV or a flight of the UAV; determine response data based on the flight data request and based on an entity associated with the requesting device, the response data including a subset of the flight data, which is available to be accessed by the entity; and perform an action associated with the response data.

Abstract: A device can be configured to detect a physical connection with an unmanned aerial vehicle (UAV) flight controller through an interface. The device can identify a UAV identifier associated with the UAV flight controller and determine, based on the UAV identifier, an application programming interface (API) for communicating with the UAV flight controller. Using the API, the device can establish a communications link with the UAV flight controller and perform an action based on the communications link.

Abstract: A device can be configured to receive flight data associated with an unmanned aerial vehicle (UAV), at least some of the flight data being received from the UAV; store the flight data; receive, from a requesting device, a flight data request, the flight data request including information identifying the UAV or a flight of the UAV; determine response data based on the flight data request and based on an entity associated with the requesting device, the response data including a subset of the flight data, which is available to be accessed by the entity; and perform an action associated with the response data.

Abstract: A system includes a UAV with at least three rotors, a flight controller on the UAV, and a pistol-grip-style transmitter to receive operator input for controlling the UAV. The transmitter includes (1) a speed trigger for controlling a linear speed of the UAV, (2) a steering wheel for performing left or right turns by the UAV, (3) an altitude slider for controlling a flying altitude of the UAV, and (4) a communication interface to send control signals indicating relative positions of the speed trigger, the steering wheel, and the altitude slider. The flight controller includes a processor to receive, from the transmitter, the control signals indicating the relative positions of the speed trigger, the steering wheel, and the altitude slider. The processor adjusts, in response to the control signals, a rotational speed of each rotor independently to implement the operator input for forward-facing flight.

Abstract: A system may determine, using first location data, that a client device is located in an interior of a premises in which multiple wireless network access points (APs) are deployed in a wireless network; identify, using second location data, locations of the client device within the interior; obtain, for the locations, a dataset including wireless network signal strength values corresponding to signal coverage areas associated with the wireless network APs; generate, using the dataset, a wireless network mapping corresponding to a layout of the interior; evaluate, against connectivity rules, the wireless network mapping with respect to overlapping signal coverage areas, the connectivity rules defining operations for connecting the client device to the wireless network as a function of the locations; and apply at least one of the operations to the client device at a current location relative to a preceding location and/or a succeeding location of the client device.

Abstract: A system may determine, using first location data, that a client device is located in an interior of a premises in which multiple wireless network access points (APs) are deployed in a wireless network; identify, using second location data, locations of the client device within the interior; obtain, for the locations, a dataset including wireless network signal strength values corresponding to signal coverage areas associated with the wireless network APs; generate, using the dataset, a wireless network mapping corresponding to a layout of the interior; evaluate, against connectivity rules, the wireless network mapping with respect to overlapping signal coverage areas, the connectivity rules defining operations for connecting the client device to the wireless network as a function of the locations; and apply at least one of the operations to the client device at a current location relative to a preceding location and/or a succeeding location of the client device.