A method comprising receiving a plurality of images of a scene captured by at least one drone; identifying features within the plurality of images; identifying similar images of the plurality of images based on the features identified within the plurality of images; comparing the similar images based on the features identified within the similar images to determine a proportion of features shared by the similar images; selecting a subset of the plurality of images that have a proportion of shared features that meets a predetermined range; generating a first 3D model of the scene from the subset of images using a first 3D model building algorithm; generating a second 3D model of the scene from the subset of images using a second 3D model building algorithm; computing errors for the first and second 3D models; and selecting as the model of the scene the first or second 3D model.
B64U 101/20 - UAVs specially adapted for particular uses or applications for use as communications relays, e.g. high altitude platforms
B64U 101/30 - UAVs specially adapted for particular uses or applications for imaging, photography or videography
G01C 15/00 - Surveying instruments or accessories not provided for in groups
G05D 1/46 - Control of position or course in three dimensions
G05D 1/692 - Coordinated control of the position or course of two or more vehicles involving a plurality of disparate vehicles
G05D 1/695 - Coordinated control of the position or course of two or more vehicles for maintaining a fixed relative position of the vehicles, e.g. for convoy travelling or formation flight
A method for directing a domain-specific request to a machine learning (ML) agent of a plurality of ML agents configured to generate domain specific language (DSL) scripts includes, at a computing system: processing a request received from a user to determine a particular domain associated with the request; identifying an ML agent of the plurality of ML agents that is associated with the particular domain; generating, using the ML agent, a DSL script for the particular domain based on the request; executing, using the ML agent, at least one of an API call and a database query based on the DSL script; processing, using the ML agent, a response to the at least one of the API call and the database query; generating, using the ML agent, an output based on the response; and processing the output using at least one other ML agent of the plurality of ML agents.
A method for directing a domain- specific request to a machine learning (ML) agent of a plurality of ML agents configured to generate domain specific language (DSL) scripts includes, at a computing system: processing a request received from a user to determine a particular domain associated with the request; identifying an ML agent of the plurality of ML agents that is associated with the particular domain; generating, using the ML agent, a DSL script for the particular domain based on the request; executing, using the ML agent, at least one of an API call and a database query based on the DSL script; processing, using the ML agent, a response to the at least one of the API call and the database query; generating, using the ML agent, an output based on the response; and processing the output using at least one other ML agent of the plurality of ML agents.
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
A central command system may determine a mission plan for resilient execution by a swarm of drones comprising one or more sensors to capture data in accordance with the mission plan. The mission plan may specify requirements for fault tolerance or parallelism and a redundancy structure for the swarm. The mission plan may be transmitted to a remote drone swarm controller device that determines a swarm configuration based on the mission plan and available drones. The controller may transmit instructions regarding the swarm configuration to dispatch a resilient swarm of drones. During execution of the mission plan, drones in the resilient swarm may be monitored by other drones in the swarm, by the remote drone swarm controller, and/or by the central command system. The redundancy structure provides for failover options for one or more drones in the resilient swarm.
A method for controlling a plurality of drones to survey a location, the method comprising, at a computing system: automatically generating preliminary flight plans for a plurality of drones to survey the location based on a 3D model; receiving survey data from the plurality of drones as the plurality of drones are surveying the location based on the preliminary flight plans; updating the 3D model based on the survey data received from the plurality of drones; and automatically updating at least a portion of the flight plans based on the updated 3D model
H04W 64/00 - Locating users or terminals for network management purposes, e.g. mobility management
B64C 39/02 - Aircraft not otherwise provided for characterised by special use
G01C 15/00 - Surveying instruments or accessories not provided for in groups
G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
G05D 1/692 - Coordinated control of the position or course of two or more vehicles involving a plurality of disparate vehicles
G05D 1/695 - Coordinated control of the position or course of two or more vehicles for maintaining a fixed relative position of the vehicles, e.g. for convoy travelling or formation flight
A method for improving wireless communication for a drone swarm, the method comprising, at a computing system, receiving, from a plurality of drones of a drone swarm, data comprising radio frequency signal characteristics detected by the plurality of drones; generating a model of a radio frequency environment for the drone swarm based on the data received from the plurality of drones; and controlling at least one wireless communication system to improve wireless communication for the drone swarm based on the model of the radio frequency environment.
A method comprising receiving a plurality of images of a scene captured by at least one drone; identifying features within the plurality of images; identifying similar images of the plurality of images based on the features identified within the plurality of images; comparing the similar images based on the features identified within the similar images to determine a proportion of features shared by the similar images; selecting a subset of the plurality of images that have a proportion of shared features that meets a predetermined range; generating a first 3D model of the scene from the subset of images using a first 3D model building algorithm; generating a second 3D model of the scene from the subset of images using a second 3D model building algorithm; computing errors for the first and second 3D models; and selecting as the model of the scene the first or second 3D model.
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