Advanced Manufacturing Technologies and Machine Learning in Unmanned Aviation Systems

Abstract

A vehicle can be configured to include a body having a body bottom conjoined with a body sidewall and a body top forming a body cavity. The body top includes a body top opening and the body sidewall includes a body sidewall opening. The vehicle can include a payload housing having a payload bottom conjoined with a payload housing sidewall and a payload housing top forming a payload housing cavity, wherein the payload housing cavity is configured to hold at least one operating module for the vehicle. The vehicle can include at least one arm. The vehicle can include at least one interlocking arrangement of the body top opening or body side wall configured to removably secure the payload housing and the at least one arm to the body. Each of the body, the payload housing, and the at least one arm can be structured with additive manufactured material.

What is claimed is: 
     
         1 . A vehicle, comprising:
 a body having a body bottom conjoined with a body sidewall and a body top forming a body cavity, wherein the body top includes a body top opening and the body sidewall includes a body sidewall opening;   a payload housing having a payload bottom conjoined with a payload housing sidewall and a payload housing top forming a payload housing cavity, wherein the payload housing cavity is configured to hold at least one operating module for the vehicle;   at least one arm; and   at least one interlocking arrangement of the body top opening or body side wall configured to removably secure the payload housing and the at least one arm to the body;   wherein each of the body, the payload housing, and the at least one arm are structured with additive manufactured material.   
     
     
         2 . The vehicle recited in  claim 1 , wherein each of the body, the payload housing, and the at least one arm are structured entirely with additive manufactured material. 
     
     
         3 . The vehicle recited in  claim 1 , comprising:
 a cover structured with additive manufactured material, wherein the at least one interlocking arrangement is configured to removably secure the cover to the body.   
     
     
         4 . The vehicle recited in  claim 1 , wherein the at least one interlocking arrangement is configured to be manually transitioned between an engaged configuration and a disengaged configuration. 
     
     
         5 . The vehicle recited in  claim 1 , wherein the at least one arm includes plural arms. 
     
     
         6 . The vehicle recited in  claim 1 , wherein the at least one arm includes a failure point configured to facilitate mechanical failure of the at least one arm upon experiencing a threshold force vector before transferring the threshold force vector to another component of the vehicle. 
     
     
         7 . The vehicle recited in  claim 1 , wherein the at least one arm includes at least one motor configured to propel the vehicle. 
     
     
         8 . The vehicle recited in  claim 7 , wherein the at least one arm includes an electrical connector conduit configured to route an electrical connector from the at least one motor to facilitate electrical communication between the motor and the at least one operating module. 
     
     
         9 . The vehicle recited in  claim 1 , wherein the vehicle is configured to be an aerial vehicle, a land vehicle, and/or a water vehicle. 
     
     
         10 . The vehicle recited in  claim 1 , wherein the vehicle is configured to be an autonomous vehicle. 
     
     
         11 . The vehicle recited in  claim 1 , wherein the at least one operating module includes a navigation module, a surveillance module, and/or a telemetry module. 
     
     
         12 . The vehicle recited in  claim 11 , wherein the navigation module is an avionics module. 
     
     
         13 . A method of using a vehicle, the method comprising:
 manually assembling a payload housing and at least one arm to a body via at least interlocking arrangement used to secure the payload housing to the body, and the at least one arm to the body; and   manually attaching at least one motor to the at least one arm.   
     
     
         14 . The method recited in  claim 13 , comprising:
 fabricating the body, the payload housing, and the at least one arm via additive manufacturing.   
     
     
         15 . The method recited in  claim 13 , comprising:
 receiving raw data including real time video stream information about an environment; and   generating distilled data including still image information from the real time video stream information, the still image information including at least one object identified via an object classification and localization technique.   
     
     
         16 . The method recited in  claim 15 , comprising:
 receiving the raw data at a first data bandwidth; and   transmitting the distilled data at a second data bandwidth, the second data bandwidth being less than the first data bandwidth.   
     
     
         17 . The method recited in  claim 16 , wherein:
 the first data bandwidth is >1 Mbps; and   the second data bandwidth is <1 Mbps.   
     
     
         18 . The method recited in  claim 13 , wherein the payload housing includes plural payload housings having a first payload housing and a second payload housing, the method comprising:
 manually securing the first payload housing via the at least one interlocking arrangement;   manually removing the first payload housing to disengage the at least one interlocking arrangement;   manually securing the second payload housing via the at least one interlocking arrangement.   
     
     
         19 . The method recited in  claim 15 , comprising:
 receiving first raw data;   manually removing a first payload housing to disengage the at least one interlocking arrangement;   manually securing a second payload housing via the at least one interlocking arrangement; and   receiving second raw data;   wherein the first raw data is different from the second raw data.   
     
     
         20 . The method recited in  claim 15 , comprising:
 receiving raw data and transmitting first distilled data;   manually removing a first payload housing to disengage the at least one interlocking arrangement;   manually securing a second payload housing via the at least one interlocking arrangement; and   receiving raw data and transmitting second distilled data;   wherein the first distilled data is different from the second distilled data.   
     
     
         21 . An operating module for a vehicle, comprising:
 a navigation module including a navigation processor and at least one navigation sensor, the navigation module configured to communicate with at least one motor of the vehicle to facilitate navigation and propulsion of the vehicle;   a surveillance module including a surveillance processor and at least one surveillance sensor, the surveillance module configured to: receive raw data, the raw data including real time video stream information about an environment; and generate distilled data, the distilled data including still image information from the real time video stream information, the still image information including at least one object identified via an object classification and localization technique;   a telemetry module including a telemetry processor and a telemetry transceiver, the telemetry module configured to transmit the distilled data to a computer device.   
     
     
         22 . The operating module recited in  claim 21 , wherein the navigation module includes a Global Positioning System (GPS) sensor. 
     
     
         23 . The operating module recited in  claim 21 , wherein the surveillance module includes a Graphics Processing Unit (GPU) processor. 
     
     
         24 . The operating module recited in  claim 21 , wherein the telemetry module includes a gateway transceiver. 
     
     
         25 . The operating module recited in  claim 21 , comprising:
 a communications network configured to facilitate communication between the telemetry module and the computer device, wherein:
 the surveillance module is configured to receive the raw data at a first data bandwidth; and 
 the telemetry module is configured to transmit the distilled data at a second data bandwidth. 
   
     
     
         26 . The operating module recited in  claim 25 , wherein:
 the first data bandwidth is >1 Mbps; and   the second data bandwidth is <1 Mbps.   
     
     
         27 . The operating module recited in  claim 21 , wherein the surveillance module is configured to use machine learning as part of the object classification and localization technique, the machine learning generating a confidence score for each identified object, the confidence score being a probabilistic measure of the identified object falling within a match parameter of a learned object. 
     
     
         28 . The operating module recited in  claim 27 , wherein the surveillance module is configured to include the identified object with the still image information only when the confidence score is greater than a selectable or configurable threshold. 
     
     
         29 . The operating module recited in  claim 27 , wherein the surveillance module is configured to display the confidence score associated with each identified object within the distilled data. 
     
     
         30 . The operating module recited in  claim 21 , wherein the surveillance module is configured to convert the still image information into a Portable Document Format (PDF) file or another file format. 
     
     
         31 . The operating module recited in  claim 21 , wherein the navigation module is configured to generate vehicle coordinates and the surveillance module is configured to use the vehicle coordinates and a ranging technique to generate object coordinates for the at least one identified object. 
     
     
         32 . The operating module recited in  claim 21 , wherein the navigation module is configured for navigation and propulsion of an autonomous vehicle. 
     
     
         33 . A method of surveillance, the method comprising:
 receiving raw data at a first data bandwidth, the raw data including real time video stream information about an environment;   generating distilled data, the distilled data including still image information from the real time video stream information, the still image information including at least one object identified via an object classification and localization technique; and   transmitting the distilled data at a second data bandwidth, the first data bandwidth being greater than the second data bandwidth.   
     
     
         34 . The method recited in  claim 33 , wherein:
 the first data bandwidth is >1 Mbps; and   the second data bandwidth is <1 Mbps.   
     
     
         35 . The method recited in  claim 33 , wherein the object classification and localization technique involves machine learning to generate a confidence score for each identified object, the confidence score being a probabilistic measure of the identified object falling within a match parameter of a learned object. 
     
     
         36 . The method recited in  claim 34 , comprising:
 including the identified object with the distilled data only when the confidence score is greater than a selectable or configurable threshold.   
     
     
         37 . The method recited in  claim 36 , comprising:
 displaying the confidence score associated with each identified object within the distilled data.   
     
     
         38 . The method recited in  claim 33 , wherein generating the distilled data involves converting the still image information into a Portable Document Format (PDF) file or another file format. 
     
     
         39 . An operating module for a vehicle, comprising:
 a navigation module including a navigation processor and at least one navigation sensor, the navigation module configured to communicate with at least one motor of the vehicle for navigation and propulsion of the vehicle;   a surveillance module including a surveillance processor and at least one surveillance sensor, the surveillance module configured to:
 receive raw data, the raw data including real time video stream information about an environment; and 
 process the raw data to generate distilled data, the distilled data including a still image information from the real time video stream information, the still image information including at least one object identified via an object classification and localization technique; and 
   a telemetry module including a telemetry processor and a telemetry transceiver, the telemetry module configured to transmit the distilled data to a computer device;   wherein the navigation module generates vehicle coordinates and the surveillance module uses the vehicle coordinates and a ranging technique to generate object coordinates for the at least one object; and   wherein the surveillance module co-registers the object coordinates with the at least one object and includes the co-register object coordinates as part of the distilled data.   
     
     
         40 . The operating module recited in  claim 39 , wherein the navigation module includes a Global Positioning System (GPS) sensor. 
     
     
         41 . The operating module recited in  claim 39 , wherein the surveillance module includes a Graphics Processing Unit (GPU) processor. 
     
     
         42 . The operating module recited in  claim 39 , wherein the telemetry module includes a gateway transceiver. 
     
     
         43 . The operating module recited in  claim 39 , comprising:
 a communications network configured to facilitate communication between the telemetry module and the computer device, wherein:
 the surveillance module is configured to receive the raw data at a data bandwidth of >1 Mbps; and 
 the telemetry module is configured to transmit the distilled data at a data bandwidth of <1 Mbps. 
   
     
     
         44 . The operating module recited in  claim 39 , wherein the surveillance module is configured to use machine learning as part of the object classification and localization technique, the machine learning generating a confidence score for each identified object that is a probabilistic measure of the identified object falling within a match parameter of a learned object. 
     
     
         45 . The operating module recited in  claim 44 , wherein the surveillance module is configured to include the identified object with the distilled data only when the confidence score is greater than a selectable or configurable threshold. 
     
     
         46 . The operating module recited in  claim 45 , wherein the surveillance module is configured to display the confidence score associated with each identified object within the distilled data. 
     
     
         47 . The operating module recited in  claim 39 , wherein the surveillance module is configured to convert the distilled data into a Portable Document Format (PDF) file or another file format. 
     
     
         48 . The operating module recited in  claim 39 , wherein the navigation module is configured for navigation and propulsion of an autonomous vehicle. 
     
     
         49 . A method of surveillance, the method comprising:
 receiving raw data at a first a data bandwidth, the raw data including real time video stream information about an environment; and   generating distilled data from the raw data, the distilled data including a still image information from the real time video stream information, the still image information including at least one object identified via an object classification and localization technique;   co-registering object coordinates for the at least one identified object as part of the distilled data; and   transmitting distilled data at a second data bandwidth.   
     
     
         50 . The method recited in  claim 49 , wherein:
 the first a data bandwidth is >1 Mbps; and   the second data bandwidth is <1 Mbps.   
     
     
         51 . The method recited in  claim 49 , wherein the object classification and localization technique involves machine learning to generate a confidence score for each identified object, the confidence score being a probabilistic measure of the identified object falling within a match parameter of a learned object. 
     
     
         52 . The method recited in  claim 51 , comprising:
 including the identified the object with the distilled data only when the confidence score is greater than a selectable or configurable threshold.   
     
     
         53 . The method recited in  claim 52 , comprising:
 displaying the confidence score associated with each identified object within the distilled data.   
     
     
         54 . The method recited in  claim 49 , wherein generating the distilled data involves converting the still image information into a Portable Document Format (PDF) file or another file format. 
     
     
         55 . A vehicle, comprising:
 a body including at least one mount, each mount configured to secure a motor;   a payload including at least one operating module for the vehicle;   at least one interlocking arrangement configured to removably secure the payload to the body;   wherein the body is structured with additive manufactured material.   
     
     
         56 . The vehicle recited in  claim 55 , wherein each mount is disposed in or on a structure, the structure extending from the body and/or removably attached to the body. 
     
     
         57 . The vehicle recited in  claim 56 , wherein the structure includes any one or combination of: a pillared structure, a tripod structure, a crossbar structure, a pyramid structure, and an arm. 
     
     
         58 . The vehicle recited in  claim 57 , wherein the structure is configured to extend orthogonally or non-orthogonally from a top of the body, orthogonally or non-orthogonally from a bottom of the body, and/or orthogonally or non-orthogonally from a side of the body. 
     
     
         59 . The vehicle recited in  claim 57 , wherein the structure is configured as an arm and the at least one interlocking arrangement is configured to removably secure the arm to the body. 
     
     
         60 . The vehicle recited in  claim 56 , wherein the structure includes a hinged joint. 
     
     
         61 . The vehicle recited in  claim 60 , wherein the structured is pivoted about the hinged joint to transition the structure to and from a stowed position and a deployed position. 
     
     
         62 . The vehicle recited in  claim 55 , wherein the body is configured as any one or combination of:
 a walled member having a body cavity formed within the body, the cavity being configured to receive the payload;   a single planar member configured to support the payload on a surface thereof; and   plural planar members configured to retain the payload by sandwiching the payload.   
     
     
         63 . The vehicle recited in  claim 55 , wherein the motor is configured to drive a propulsion means for the vehicle. 
     
     
         64 . The vehicle recited in  claim 63 , wherein the propulsion means includes any one or combination of an impeller, a propeller, a thruster, and a drivetrain. 
     
     
         65 . A method of producing a vehicle, the method comprising:
 generating a body via additive manufacturing; and   generating a payload including at least one operating module for the vehicle;   wherein at least one interlocking arrangement is included in or on the body and is configured to removably secure the payload to the body by manual assembly.   
     
     
         66 . The method recited in  claim 65 , comprising:
 generating at least one structure via additive manufacturing with a mount disposed therein or thereon, the mount configured to secure a motor.   
     
     
         67 . The method recited in  claim 66 , wherein the at least one structure is removably secured to the body via the at least one interlocking arrangement. 
     
     
         68 . The method recited in  claim 65 , comprising:
 generating a payload housing via additive manufacturing, the payload housing configured to retain the payload and configured to be removably secured to the body via the at least one interlocking arrangement.   
     
     
         69 . The method recited in  claim 65 , comprising:
 generating at least one structure via additive manufacturing with a mount disposed therein or thereon, the mount configured to secure a motor, wherein the at least one structure is removably secured to the body via at least one interlocking arrangement; and   generating a payload housing via additive manufacturing, the payload housing configured to retain the payload and configured to be removably secured to the body via the at least one interlocking arrangement.   
     
     
         70 . The method recited in  claim 69 , wherein:
 any one or combination of the body, the structure, and the payload housing is partly or entirely generated via additive manufacturing performed at a first location and/or a second location;   the first location is a manufacturing facility;   the second location is an area at or within operational reach of an environment within which the vehicle will be operated.   
     
     
         71 . The method recited in  claim 70 , wherein design criteria including shape and configuration of any one or combination of the body, the structure, and the payload housing is determined by Finite Element Analysis (“FEA”). 
     
     
         72 . The method recited in  claim 71 , wherein the FEA uses operational parameters related to a type of propulsion for which the motor is configured, a type of operational module for which the operating module is configured, and/or environmental constraints within which the vehicle will be operated. 
     
     
         73 . The method recited in  claim 72 , wherein the environmental constraints include any one or combination of:
 transport of components of the vehicle to the second location, the components including the body, the structure, the payload, the payload housing, the motor, a battery unit, circuitry, sensors, and/or propulsion means, wherein the propulsion means includes any one or combination of an impeller, a propeller, a thruster, and a drivetrain; and   ability and speed with which the additive manufacturing at the second location generates components of the vehicle.