DRONE, DRONE DOCKING PORT AND METHOD OF USE

Abstract

A drone docking ports (DDP) mounted on a pole top in close proximity to an accident scene with an openable and closable enclosure, a docking plate having integrated battery wired or wireless recharging pads, and a control module (CM) is disclosed. The CM is adapted to autonomously control all functions of the DDP including actuation of the enclosure and relay of video, audio, and flight control information between the CM and a central monitoring center and/or emergency personnel. A drone with a top and bottom profile design allowing numerous drones to be stacked upon one another and store in the DDP. When the DDP enclosure is in an open position, a drone or stack of drones may initiate a flight from the DDP and to re-dock the drone or stack of drones when the flight is completed, the enclosure may be closed to protect the drone or stack of drones.

Claims

1. A drone docking port (DDP) comprising a housing having an inner cavity, an openable and closable enclosure with two cylindrical halves attached to two support and actuator rods, actuated by an opening/closing motor, a docking plate, and a drone or multiple drones in a stack, wherein the docking plate is affixed within the housing and the drone or multiple drones in a stack are deployable mounted on the docking plate, and wherein the DDP is adapted such that when the enclosure is in a closed position, the housing substantially seals out environment external to the DDP, and wherein when the enclosure is in an open position, the drone or multiple drones in a stack are exposed so as to be able to launch, and wherein the enclosure height is adapted to the height of the drone or multiple drones in a stack.

2. The DDP of claim 1, wherein each drone includes at least one battery, and wherein when the first drone is mounted on the docking base, the at least one battery is automatically charged by at least one of a wired battery charger and/or a wireless battery charger, then the second drone in the stack is mounted on the first drone, the at least one battery in the second drone is automatically charged by at least one of a wired battery charger or a wireless battery charger, then the third drone in the stack is mounted on the second drone in the stack, the at least one battery in the second drone is automatically charged by at least one of a wired battery charger or a wireless battery charger, and so on until the last or top drone in the stack is automatically charged by at least one of a wired battery charger or a wireless battery charger.

3. The DDP of claim 1, wherein the DDP is mounted on a DDP inverted support cone at the top of a pole in near proximity to a target monitoring site, and wherein the DDP inverted support cone contains the DDP battery charger, DDP battery and optional traffic flow sensor system.

4. The DDP of claim 1, wherein in response to a predetermined signal, the enclosure automatically opens and the drone or multiple drones in a stack automatically or autonomously flies to a target monitoring site.

5. The DDP of claim 4, wherein when the drone or multiple drones in a stack are at the target monitoring site, each drone in the multiple drones in a stack performs at least one of the functions of recording video data of the target monitoring site, recording audio data of the target monitoring site, transmitting video data of the target monitoring site, transmitting audio data of the target monitoring site, transmitting audio data to the target monitoring site, directing traffic at the target monitoring site, providing a warning at the target monitoring site, illuminating the target monitoring site, and creating a light beacon over the target monitoring site.

6. The DDP of claim 1, wherein the docking plate is adapted to receive drones of a plurality of shapes and sizes, and wherein the docking base is adapted to house a plurality of drones simultaneously, and wherein the docking plate includes at least one target thereon and is adapted so as to automatically guide landing of a drone to the at least one target.

7. The DDP of claim 1, wherein the DDP functionally includes at least one of an electric motor, a back-up battery, a solar panel, an air conditioner, a heater, an anemometer, a temperature sensor, a relative humidity sensor, and a barometer.

8. A drone docking port (DDP) comprising a housing having an inner cavity, an openable and closable enclosure, a drone or multiple drones in a stack, a docking plate with a flat top and curved edges and adapted to receive and secure a drone or multiple drones in a stack, and each drone with at least one battery, wherein the docking plate is affixed within the housing and the first drone is deployably mounted on the docking plate, the second drone is deployably mounted on the first drone, the third drone is deployably mounted on the second drone, and so on up the stack of drones to the last or top drone, and wherein the DDP is adapted such that when the openable and closable enclosure is in a closed position, the housing substantially seals out environment external to the DDP, and wherein when the openable and closable enclosure is in an open position, the drone or multiple drones in a stack are exposed so as to be able to launch, and wherein the enclosure height is adapted to the height of the drone or multiple drones in a stack, and wherein when the first drone is mounted on the docking plate, the at least one battery is automatically charged by at least one of a wired battery charger through recharging pads on the docking plate making contact with metal or foil recharging pads on the bottom curved surface of the first drone, and recharging pads wrapping around to the top curved surface of the first drone, or the first drone's battery is recharged with a wireless battery charger, then the second drone in the stack is mounted on the first drone, the at least one battery in the second drone is automatically charged by at least one of a wired battery charger through metal or foil recharging pads on the top curved surface of the first drone making contact with recharging pads on the bottom curved surface of the second drone, and recharging pads wrapping around to the top curved surface of the second drone, or the second drone's battery is charged by a wireless battery charger, then the third drone in the stack is mounted on the second drone in the stack, the at least one battery in the third drone is automatically charged by at least one of a wired battery charger through recharging pads on the top curved surface of the second drone making contact with recharging pads on the bottom curved surface of the third drone, and recharging pads wrapping around to the top curved surface of the third drone, or the third drone's battery is recharged with a wireless battery charger, and so on until the last or top drone in the stack is automatically charged by at least one of a wired battery charger or a wireless battery charger.

9. The DDP of claim 8, wherein the DDP is mounted on a DDP inverted support cone at the top of a pole in near proximity to a target monitoring site, and wherein in response to a predetermined signal, the enclosure automatically opens and the drone automatically or autonomously flies to a target monitoring site, and wherein the DDP inverted support cone contains the DDP battery charger, DDP battery and optional traffic flow sensor system.

10. The DDP of claim 9, wherein when the drone or multiple drones in a stack are at the target monitoring site, each drone in the multiple drones in a stack performs at least one of the functions of recording video data of the target monitoring site, recording audio data of the target monitoring site, transmitting video data of the target monitoring site, transmitting audio data of the target monitoring site, transmitting audio data to the target monitoring site, directing traffic at the target monitoring site, providing a warning at the target monitoring site, illuminating the target monitoring site, and creating a light beacon over the target monitoring site.

11. The DDP of claim 8, wherein the docking base includes at least one target thereon and wherein the DDP is adapted so as to automatically guide first drone to the at least one target, and is secured to the docking plate such that the first drone will not dislodge in response to a predetermined wind load, the second drone will be guided to the top surface and secured to the first drone, the third drone will be guided to the top surface and secured to the second drone, and so on until the last or top drone has secured to the second to last drone in the stack of drones, and will not dislodge in response to a predetermined wind load.

12. The DDP of claim 8, wherein the drone or multiple drones in a stack comprises of a plurality of side panels or LED light panel modules (LPM) having a plurality of multicolor LED lights affixed thereto, wherein the multicolor LED lights include at least one of a green color, a yellow color, a red color, a blue color and a white color, and wherein a green color, a yellow color and a red color LED lights are adapted to provide guidance to traffic at a target monitoring site, and wherein intensity of the multicolor LED lights is adapted to vary so as to be visible during daytime and nighttime, and wherein a blue color is adapted as a beacon to show the location of a target monitoring site, and wherein the white color LED light is adapted to illuminate a target monitoring site with overhead lighting during nighttime.

13. The DDP of claim 8, wherein the drone or multiple drones in a stack comprises a plurality of cameras, optionally comprises a plurality of Lidar, a plurality of Radar, a plurality of Ultrasonic sensors or any combination thereof affixed to the side panels or LPMs wherein the cameras and optional Lidar, Radar and/or Ultrasonic sensors include a digital signal processing unit, video processing unit and an artificial intelligence module adapted to process sensor and video data at a target monitoring site so as to aid in drone navigation, target monitoring, target inspection, and to detect at least one of a predetermined pattern and a predetermined object.

14. A drone docking port (DDP) for use in providing a docking port for an unmanned aerial vehicle (drone) enabled to automatically perform takeoff, mission accomplishment, landing, and post-landing battery recharging, the DDP comprising an enclosure having two cylindrical halves attached with hinges to two DDP support and actuator rods, with a weather strip affixed to the opening/closing edge and around the DDP base plate, wherein the support and actuator rods are activated by an opening/closing motor, wherein the two DDP support and actuator rods, opening/closing motor and docking plate support rods are attached to DDP base plate, a control module, and a battery pack are affixed to the top portion of the DDP base plate and underneath the docking plate, wherein the base plate is affixed to the top of the DDP inverted support cone and includes a battery charger and optional traffic flow sensor system functionally mounted therein, wherein when the enclosure is closed with the weather strips being in a compressed weather sealing state and a DDP inner cavity being formed thereby and being substantially sealed from an external weather environment, and wherein the DDP is adapted such that when the motor actuates to move the enclosure from a closed position to an open position, the motor causes the two cylindrical halve members to rotate until the enclosure is opened with the weather strips being in an uncompressed non-weather sealing state and the DDP being in a drone receivable and drone launchable state, and wherein opening the enclosure from a closed state occurs within 10 seconds, and wherein closing the enclosure from an open state occurs within 10 seconds, and wherein the DDP is adapted such that the enclosure is automatically positioned between a closed state and a fully opened state to a mid-state such that substantial weather protection is provided while also allowing the DDP inner cavity temperature to equalize with the DDP proximate external temperature, and wherein a degree of opening of such mid-state is automatically proportionate to the DDP proximate external temperature,

15. The DDP of claim 14, wherein the DDP includes a drone or multiple drones in a stack launchably and dockably retained therein.

16. The DDP of claim 14, wherein the DDP includes a drone docking plate mounted therein and having at least one charging pad thereon, the drone docking plate being adapted such that when drone contacts the at least one charging pad, at least one of wired charging and wireless charging of the drone is initiated.

17. The DDP of claim 16, wherein the drone docking plate comprises at least one of metal, plastic, fiberglass, and a combination thereof, and wherein the drone docking plate is formed in at least one of a circular shape, an oval shape, and a rectangular shape, and with curved edges, and wherein the drone docking plate includes a plurality of charging pads.

18. The DDP of claim 14, wherein the DDP is mounted on an elevated elongate structure in near proximity to a target monitoring site.

19. The DDP of claim 14, wherein in response to a predetermined signal, the enclosure automatically opens and the drone automatically or autonomously flies to a target monitoring site.

20. The DDP of claim 19, wherein when the drone or multiple drones in a stack are at the target monitoring site, the drone or multiple drones in a stack performs at least one of the functions of recording video data of the target monitoring site, recording audio data of the target monitoring site, transmitting video data of the target monitoring site to a central monitoring station, transmitting audio data of the target monitoring site to a central monitoring station, receiving audio data from a central monitoring center, receiving non-audio data from a central monitoring center, directing traffic at the target monitoring site, providing a warning at the target monitoring site, illuminating the target monitoring site, and creating a light beacon over the target monitoring site.

21. The DDP of claim 20, wherein the data receiving from the central monitoring center comprises a drone override command.

22. The DDP of claim 14, wherein the battery pack is adapted to operate the DDP without external power or recharging for at least 36 hours, and wherein the battery pack is adapted to continuously recharge a drone battery for at least 2 hours.

23. The DDP of claim 14, wherein the DDP includes at least one of a solar panel adapted to recharge the battery pack, an air conditioning unit adapted to automatically control temperature and humidity inside of the DDP, a heating unit adapted to automatically control temperature and humidity inside of the DDP, a weather monitoring device adapted to monitor at least one of temperature, wind speed, humidity, rain, snow, ice, fog, and dust.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order that the advantages of the invention will be readily understood, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings. Understanding that these drawings depict only typical embodiments of the invention and are not therefore to be considered to be limiting of its scope, the invention will be described and explained with additional specificity and detail through the use of the accompanying drawings, in which:

[0037] FIG. 1 is a side sectional view of the DDP to include the cylindrical container (CC) in the closed position;

[0038] FIG. 2 is a top sectional view of the DDP and CC in the closed position;

[0039] FIG. 3 is a side sectional view of the DDP to include the CC in the open position;

[0040] FIG. 4 is a top sectional view of the DDP and CC in the open position;

[0041] FIG. 5 is a top view of the drone with LED Panel Modules (LPM);

[0042] FIG. 6 is a bottom view of the drone with LED Panel Modules (LPM);

[0043] FIG. 7 is a side sectional view of a drone;

[0044] FIG. 8 is a front view of an LED Panel Module (LPM);

[0045] FIG. 9 is a side sectional view of an LED Panel Module (LPM);

[0046] FIG. 10 is a front view of an LPM mounted on the side of a drone;

[0047] FIG. 11 is a front view of multiple LPMs mounted on multiple or stacked drones;

[0048] FIG. 12 is a side sectional view of a DDP to include stackable drones within the CC in the closed position:

[0049] FIG. 13 is a side sectional view of a DDP to include stackable drones within the CC in the open position;

[0050] FIG. 14 is a block logic diagram of an LED Panel Module (LPM);

[0051] FIG. 15 is a block logic diagram of a DDP with CM; and

[0052] FIG. 16 is a remote control unit.

DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] Reference throughout this specification to “one embodiment,” “an embodiment,” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrases “in one embodiment,” “in an embodiment,” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0054] Furthermore, the described features, structures, or characteristics of the invention may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are included to provide a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention can be practiced without one or more of the specific details, or with other methods, components, materials, and so forth. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of the invention.

[0055] Referring to FIGS. 1-16, DDP 500 includes DDP cylindrical container 501 or enclosure, cylindrical container half 502&503, DDP base plate 505, container half hinge 506, container support and actuator rod 507, container open/close actuator motor 508, docking plate 510, docking plate curved portion 511, DDP recharging pad 512, docking plate orientation marks 513, control module 515, battery pack 516, optional A/C 517, highway support pole top 400, inverted cone support base 520 and optional support base traffic flow sensor system 530. The docking plate 510 further includes distinguishing and orientation marks 513 to aid docking cameras 620&621 to aid in precise drone docking maneuvers. The drone 600 and drone bottom 614 and drone bottom curved surface 615 is similar in shape to the docking plate 510 and docking plate curved surface 511, and upon docking, the curved surfaces will allow the drone 600 to fall or slip in place making contact with a plurality of recharging pads 613 comprised of a conductive material (e.g. metal or metal foil) located on multiple locations on the bottom curved surface 615 wrapping around the bottom edge to the top curved surface 611 so that the drone's 600 batteries 618 will be recharged while the drone is stored within the DDP 500. The drone 600 and drone top 610 and top curved surface 611 is similar in shape to the docking plate 510, docking plate curved surface 511, drone bottom 614 and drone bottom curved surface 615 and upon docking of a second drone 600 upon the first docked drone, the curved surfaces will allow the second drone 600 to fall or slip in place on the first docked drone, making contact with a plurality of recharging pads 613 of the second drone docking with plurality of recharging pads 612 of the first drone docked. This process is repeated for each drone in the DDP 500. Docking plate support rods 514 are attached to the base plate 505 and docking plate 510, the space between the base plate 505 and docking plate 510 contain the DDP control module (CM) 515, DDP battery pack 516 and optional A/C module 517. CM 515 controls all aspects of the DDP 500 to include opening and closing of the cylindrical container (CC) 501 container halves 502&503, recharging DDP batteries 516 and drone batteries 618. In an inactive mode, DDP 500 contains drones 600 with CC sections 502&503 in the closed position and enclosing drones 600 from the outside environment. While the DDP 500 is in the inactive mode, the docking plate recharging pads 512 makes electrical contact with the first drone 600 recharging pads 613 and corresponding second drone, third drone, etc. allowing all drone batteries to recharge. Upon activation, CC sections 502&503 opens to fully exposing drones 600, the top drone 600 motors 607 and propellers 606 start to allow drone 600 takeoff. Once the top drone 600 takeoff is complete, the next top drone 600 motors 607 and propellers 606 start to allow drone 600 takeoff, then the next top drone 600 and so on until all drones 600 are deployed. Upon drone 600 return, drone 600 autonomously positions itself above DDP 500 for landing, verifies proper orientation with using distinguishing marks 513 on docking plate 510, then descends to the docking plate 510 where the plurality of recharging pads 613 make contact with plurality of docking plate recharging pads 512, this process is repeated until all the drones 600 have securely docked. Once secure, CC sections 502 and 503 close to cover the drones 600 and enclose them from the outside environment and DDP 500 reverts to an inactive mode where drones 600 remains until the next drone 600 activation after all drone batteries 618 are fully recharged.

[0056] FIG. 1 shows a side sectional view of the DDP 500 in the closed position, comprising cylindrical container CC 501 comprising DDP enclosure base 505 and CC sections 502 and 503 that can be mounted on the top of a support pole 400. The CC 501 comprises two cylindrical half sections 502 and 503 to form a cylinder to include a top curved portion each having an edge 504. CC sections 502 and 503 are attached to hinges 506 which are attached to an actuator rod 507 which is attached to an actuator motor 508 and may be rotated with an actuator motor 508 into a fully closed position as seen in FIG. 1, or rotated into an open position exposing the docking base 505 and docking plate 510 and all stacked drones 600 to the outside environment. As CC sections 502 and 503 transition from an open position to a closed position, the CC section edge 504 contains a weatherproof barrier and when the two halves are fully rotated and completely closed, the CC section edge 504 will be completely sealed from the exterior environment. DDP 500 interior consists of a docking plate 510 that assists drone 600 landing or docking, base plate 505 with CM 515, DDP batteries 516, and DDP air conditioning unit 517, support rods 514 affixing docking plate 510 and base plate 505 as an assembly. The base plate 505 is attached to an inverted cone support structure 520 and the support structure 520 sits on and is attached to a support pole top 400. The support structure 520 contains an optional traffic flow sensor system 530 comprising EO/IR cameras, Lidar/Radar/acoustic sensors and advanced processor technology.

[0057] FIG. 2 shows a top view of the DDP 500 in the closed position with CC sections 502 and 503 with edges 504 forming a weatherproof seam and attached to hinges 506 which are attached to actuator rods 507 to allow DDP 500 opening and closing.

[0058] FIG. 3 shows a side sectional view of the DDP 500 in the open position, comprising cylindrical container CC 501 comprising DDP enclosure base 505 and CC sections 502 and 503 that can be mounted to an inverted cone support structure 520 which in turn can be mounted on the top of a support pole 400. The CC 501 comprises two cylindrical half sections 502 and 503 to form one half of a cylinder to include a top curved portion each having an edge 504. CC sections 502 and 503 are attached to hinges 506 which are attached to an actuator rod 507 which is attached to an actuator motor 508 and may be rotated with an actuator motor 508 into a fully open position as seen in FIG. 3, exposing the docking base 505 and docking plate 510 and all stacked drones 600 to the outside environment, or rotated into a closed position. As CC sections 502 and 503 transition from a closed position to an open position, the CC section edges 504 decouple breaking the weatherproof barrier and exposing the stackable drones 600 to the outside environment, when the two halves are fully rotated and completely open, the drones 600 may be deployed.

[0059] FIG. 4 shows a top view of the DDP 500 in the open position with CC sections 502 and 503 in the fully open position and attached to hinges 506 which are attached to actuator rods 507 to allow DDP 500 opening and closing and when open exposing the base plate 505, docking plate 510, docking plate curved surface 511, recharging pads 512, and docking plate markings 513 to the outside environment.

[0060] FIG. 5 shows a top view of the drone 600 comprising ducted fans 604, ducted fan tube and heat sink 605 propellers 606, drone control module (DCM) 617, recharging pads 612, Cameras EO/JR 620 and 621, LED lights 631, and mounted to the periphery—LED (RGYB-W) Panel Module (LPM) 700 and 701, all of which are weatherproof to the outside environment. The drone 600 top surface 610 is flat with curved surfaces 611 at the drone's 600 sides where recharging pads 612 are affixed and wrap around the drone's 600 bottom edge to the drone's 600 to the recharging pads 613 on the bottom curved surface 615. So that when docking, the drone's 600 bottom surface 614 and bottom curved surface 615 will easily drop or slide into position on the docking plate 510 and docking plate curved surface 511 making contact with docking plate 510 recharging pads 512 and drone's 600 recharging pads 613, or docked drone's 600 top surface 610 and top curved surface 611, making contact with the landed drone's 600 top recharging pads 612 with the landing drone's 600 bottom recharging pads 613. Once all the docked the drones 600 or stack of drones 600 are securely docked and mated, the DDP 500 CC 501 (enclosure) closes and the drones 600 batteries 618 are recharged. The DCM 617 controls all aspects of the drone flight to include autonomous flight and communicates with the attached LPMs 700 and 701, DDP 500. The DDP 500 in turn communicates with a central monitoring center and/or emergency personnel located at the scene via remote control units 390 or personal cell phone apps. High resolution EO/IR cameras 620 and 621 pointed upwards for obstacle avoidance or overhead inspection, and assisted by LED light 631 illumination. Mounted on the drone periphery is an LPM 700 and zero to three LPM 701s (depending on highway type) providing traffic signal light control to vehicles and signal codes transmitted directly to vehicles equipped to accept signal codes in an emergency situation or to guide traffic around an incident or accident. For example, when implemented on freeways or other divided highways, drones 600 with a single LPM 700 may be employed, as traffic incidents generally occur in one direction, thus traffic control would be from one direction. When implemented near intersections, a drone 600 with one LPM 700 and three LPM 701s should be employed as traffic incidents occurring at intersections, particularly at intersections without traffic signals, would require control in four directions, and for intersections with traffic signal control, during power outages or traffic signal outages a drone 600 with one LPM 700 and three LPM 701s would assist traffic control until the outage is resolved. The LPM 700 is considered the Master LPM and issue commands to the LPM 701s in order to switch traffic signal lights in a coordinated, synchronous fashion.

[0061] FIG. 6 shows a bottom view of the drone 600 comprising ducted fan 604, ducted fan tube heat sink 605, propellers 606, recharging pads 613, EO/IR cameras 622 and 623, Lidar/Radar/Ultrasonic sensors 625, large LED light 630, and mounted to the periphery—LED (RGYB-W) Panel Module (LPM) 700 and 701, all of which are weatherproof to the outside environment. Ducted fan tube heat sink 605 consist of a metal tube and acts a high efficiency heat sink, particularly with high velocity air being driven over the ducted fan tube's 605 surface by propellers 606. Heat generating components internal to the Drone 600 or DCM 617 can be dissipated through the ducted fan tube heat sink 605. High resolution EO/IR cameras 622 and 623 pointed downwards for drone 600 geo location, accident scene observation and investigation, obstacle avoidance and docking maneuvers, and assisted by LED light 630 illumination. Optional Lidar/Radar/Ultrasonic sensors are provided to aid in drone 600 situational awareness, flight maneuvering around obstacles, accident observation and investigation data, returning to the DDP 500, hovering and docking. Mounted on the drone periphery are LPM 700, and zero to three LPM 701s (depending on highway type) providing traffic signal light control to vehicles and signal codes transmitted directly to vehicles equipped to accept such signal codes in an emergency situation or to guide traffic around an incident or accident.

[0062] FIG. 7 shows a side sectional view of a drone 600 comprising ducted fan 604, ducted fan heat sink 605, propellers 606, propeller motor 607, propeller motor mount 608, protective screens 609 over and below each propeller 606 to keep foreign objects away from propellers 606, drone top surface 610, drone top curved surface 611, drone bottom surface 614, drone bottom curved surface 615, drone control module (DCM) 617, drone batter pack (DBP) 618, and LPM 701s, all of which are weatherproof to the outside environment. The top surface 610 and top curved surface 611 of one drone 600 is the same in shape and allows mating with an above second drone's 600 bottom surface 614 and bottom curved surface 615 and easily drop or slide into position on the docked drone's 600.

[0063] FIG. 8 shows a front view of an LPM 700 as a master controller for LPMs 701 as slave LPMs (LPM 701 has the same front view without the Lidar/Radar/Ultrasonic Sensors 725 and 726), is weatherproof to the outside environment, and consists of a large multicolored LED light panel 710 displaying Red, Green, Yellow LED traffic signal lights, Blue LED beacon light and white LED lights to assist emergency personnel, provides commands to switch traffic signal lights from Red to Green to Yellow and sends a traffic signal control code to other LPM 701s via wired or wireless control to synchronously switch all LPM 701s LED light panel 710 displays. Where Green or Yellow signal lights are displayed in two opposing directions and a Red signal light in a 90 degree opposing directions. The LPM 700 also provides traffic signal codes transmitted directly to vehicles equipped to accept such signal codes to guide equipped vehicles around an incident or accident (i.e. autonomous vehicles). The LPM 700 contains an EO/IR stereo camera pair 722 and 723, fixed and pointing outward to observe a traffic flow, accident or incident and used to collect investigative data. The LPM 700 also contains an optional Lidar/Radar/Ultrasonic Sensors 725 and 726 or any combination of Cameras, Lidar, Radar and/or Ultrasonic Sensors to precisely detect location, distance, speed and size of on-coming vehicles, and to assist in safely and efficiently directing them around an accident or incident.

[0064] FIG. 9 shows an edge view of an LED Panel Module (LPM) 700. LPM 700 comprises a weatherproof housing with an LED light panel 710 affixed to the front, cameras 722 and 723, Lidar/Radar/Ultrasonic sensors 725 and 726, a video processing unit 727, a digital signal processing unit 728 and a neural network 729 affixed to a circuit board 720 within the LPM 700 and with appropriate power, input and output capability through a multi-pin connector 721 attached to the circuit board, protruding through the housing with a weatherproof seal around the connector and connected to a mating connector as located on each side of the drone 600.

[0065] FIG. 10 shows a side view of a drone 600 with recharging pads 612 and 613 on the drone's 600 top and bottom curved surfaces 611 and 615, LED light module holder 640 and a front view of an LPM 700 attached to the drone 600 with an LED light module holder 640 so that an LPM 700 and 701 may be inserted, firmly affixed to the drone 600. The LPM 700 contains a large multicolored LED light panel 710, an EO/IR stereo camera pair 722 and 723, and Lidar/Radar/Ultrasonic Sensors 725 and 726. An LPM 700 is employed on all drone 600s particularly deployed for freeways and highways with one directional traffic flow patterns and one to three LPMs 701 are employed on drones deployed for more than one directional traffic flow pattern as in traffic intersections.

[0066] FIG. 1I shows a stack of three drones 600 with recharging pads 612 and 613, LED light module holder 640 and front view of LPMs 700 attached to drones 600 with an LED light module holder 640. The LPM 700 contains a large multicolored LED light panel 710, an EO/IR stereo camera pair 722 and 723, and Lidar/Radar/Ultrasonic Sensors 725 and 726. Bottom drone 600 has a middle drone 600 in docked position, making contact 616 with recharging pads 612 and 613. The middle drone 600 has a top drone 600 in docked position, making contact 616 with recharging pads 612 and 613 to complete a stack of drones 600.

[0067] FIG. 12 shows a side sectional view of a DDP 500 with CC 502 and 503 in the closed position with a stack 650 of docked drones 600, atop an inverted support cone 520, atop a pole 400.

[0068] FIG. 13 shows a side sectional view of a DDP 500 with CC 502 and 503 in the open position with a stack 650 of docked drones 600, ready to take off.

[0069] FIG. 14 shows a block diagram of an LED Panel Module (LPM) 700, with Sensor Inputs, VPU (Video Processing Unit), DSP (Digital Signal Processor) and Neural Network, LPM-Drone communications, LED Light Panel, DCM, Video Output and SD Card Blocks. LPM 700 Sensor Input data from Cameras 722&723 video and Lidar, Radar and/or Ultrasonic sensor 725&726 data are processed with a video processor 727, digital signal processor (DSP) 728 and neural network 729. Video data, light commands and flight control instructions are transmitted to and from the LPM 700 to other on-board LPM 701s for LED signal light switching, and to the drone 600 and drone control module (DCM) 617 for video recording and flight control for precise drone 600 maneuvering.

[0070] In operation, the video processing unit 727 and DSP unit 728 provides feature extraction and other video or signal processing techniques and outputs this data to a neural network 729. The neural network uses the video and DSP processing unit data and/or has the ability to input and process raw video and DSP data, and provides detection, recognition, classification and tracking of objects, like people, bicycles, cars, trucks, etc., so that when an accident occurs, the LPM 700 on a drone 600 can detect obstacles on the way to an accident scene and provide instructions to the drone control module (DCM) 617 to avoid those obstacles, and once at the scene, the drone 600 and LPM 700 can immediately communicate, determine severity, and provide some level of comfort to the accident victims, communicate this status to a central monitoring center, then perform a thorough investigation of the accident scene with video and/or Lidar, while other drones are performing different modes of the accident scene operation. One of the modes, directing traffic around the accident or incident in a safe, efficient manner by implementing the LED light panel 710 on the LPM 700 and/or 701 as a traffic signal light changing from green to yellow to red for a direction of traffic. For example, a freeway with four lanes would have four drones 600 immediately fly a sufficient distance away from the accident with a drone above each lane and initially displaying a Red LED light to stop all vehicles, then when safe, a drone in a first lane will display a Green LED light allowing multiple vehicles in a lane to pass with Red lights on all other lanes, then display a Yellow LED light, then a Red LED light for cars in the lane to stop. A drone in a second lane displays a Green LED light and the process continues with the third and fourth lanes, then the process is repeated until the accident is cleared. In addition to the LPM 700 switching the LED light signals, personnel at the central monitoring center and first responder personnel or police can take over drone 600 control and LPM 700 LED light signal control to maneuver and switch LED light signals as appropriate. Another mode would have one or more drones positioned from the drone 600 stopping point to the accident displaying Green LED lights to direct traffic around the accident in a safe, efficient manner. Another drone would display a Blue LED light and fly high above the accident and act a beacon to let drivers and first responder personnel know of the accident location to give them an idea of time they have to wait for normal traffic flows. When accidents occur at night, another drone in the stack of drones would be deployed displaying a bright White LED light and fly at a safe distance over the accident to assist emergency personnel clearing the accident scene.

[0071] Upon a low battery alert or when the accident operations are complete and the drones 600 return to the DDP 500, the drones with LPM 700s on board land in order of battery depletion and drones with LPM 700 and 701s on board are last to land as these are the drones that perform accident scene investigation with master LPM 700 and slave 701s and should be the first drones to deploy from the DDP upon the next incident after batteries are recharged. When drones return to the DDP to dock and recharge batteries, other drones from the DDP stack of drones, other DDPs or drones in Emergency vehicles will take their place and resume their modes of operation. In the event of a malfunction, a malfunction signal or code will be sent to the central traffic control monitoring center for resolution.

[0072] The LPM 700 vision processing unit (VPU) and neural network are key components within the LPM 700 as manufactured by INTEL, NVIDIA, QUALCOM, GENERAL VISION and others as used for processing. INTEL has a several vision processing unit chips, including one that features a neural compute engine with 16 core processors each providing the ability to perform separate pipeline algorithms, sensor fusion and/or convolution neural networks all in a low power chip suitable for battery operation. The neural compute engine portion adds hardware accelerators designed to dramatically increase performance of deep neural networks without including the low power characteristics of the chip. Known software and algorithms will be applied to this chip or others to detect, recognize and analyze vehicles, vehicular incidence and/or accidents, vehicles in a traffic lane, as well as drone 600 position and orientation to provide flight controls to precisely dock a drone 600. INTEL and GENERAL VISION both have low power chips that perform RBF (Radial Basis Function) neural networks in real time and can be considered fast learning (as opposed to deep learning) processors. GENERAL VISIONS's chips have 576 neurons with low power characteristics in a very small package, where each neuron consists of a processor and memory. Neurons can be configured in parallel or hierarchical and suitable for fast or real time learning and provides real time image or signal detection, classification and recognition. These processors (chips) are taught and not necessarily programmed, so programming is simplified and known by technologists in that field. Furthermore, GENERAL VISTON's NEUROMEM Technology can be implemented in Field Programmable Gate Array (FPGA) chips and has been previously implemented on INTEL chips and vision sensor die from OMNIVISTON as a single chip camera solution.

[0073] Sensor data that is processed on neural network architectures, designed specifically around the Radial Basis Function (RBF) or K Nearest Neighbor modes of operation, can be considered an expert system, which recognizes and classifies objects or situations and makes instantaneous decisions, based on accumulated knowledge. It accumulates its knowledge ‘by example’ from data samples and corresponding categories. Its generalization capability allows it to react correctly to objects or situations that were not part of the learning examples. The learning capability of an RBF neural network model is not limited in time, as opposed to some other models. It is capable of additional learning while performing classification tasks. The RBF mode of operation allows for instant “learning on the fly”. As an example, tracking a vehicle, an operator can select an object to be tracked by placing a region of interest (ROI) around the object and selecting this region with a mouse click while neural network is in its learning mode, feature extraction algorithms may be applied (neural network can work with raw data or feature extracted data), data from the ROI will be loaded into the memory block automatically and sequentially (requiring from one to a multitude of neurons), thus training neural network from a single frame of imagery and in real time. Once learned, neural network will input the second frame of imagery, compare data from the entire frame with the neuron memory contents, find a match, classify the match, and provide an X-Y (coordinates) position or location output. This X-Y output will allow an associated pan and tilt mechanism to track the object of interest in real time. This process continues for each successive frame. In the event the vehicle turns or changes shape in relation to the camera location, the degraded quality of the neuron memory comparison will trigger the neural network learning mode to capture this changed data and commit more neurons for the new object shape. This neural network will simultaneously and continuously track the object, allowing itself the ability to track even as new patterns are learned.

[0074] Artificial Intelligence (AI) solutions today typically require high performance computers and/or parallel processors running AI or neural network software performing “Deep Learning” on back propagation and other neural networks. These systems can be large, consume significant power and be very costly for both the hardware and software. The learning phase for Deep Learning neural networks is generally performed in data centers or the “Cloud” and takes huge computing resources that can take days to process depending on the data set and number of levels in the network. After the network has been generated it can be downloaded to relatively low power processing systems (Target Systems) in the field. However, these target systems are typically not capable of embedded learning, and generally consist of powerful PCs and GPU (Graphic Processing Unit) acceleration resulting in significant cost and power consumption. Additionally, as the training dataset grows during the learning phase, there is no guarantee that the target hardware will remain sufficient and users may have to upgrade their target systems to execute properly after a new network has been generated during the learning phase. The major limitation to this approach is that new training data cannot be incorporated directly and immediately in the executable knowledge. It often also requires a fair amount of hand coding and tuning to deliver useful performance on the target hardware and is therefore not easily portable. Unlike Deep Learning networks, the neural network based on RBF networks can be easily mapped on hardware because the structure of the network does not change with the learned data. This ability to map the complete network on specialized hardware allows RBF networks to reach unbeatable performances in terms of speed and power dissipation both for learning and recognition. Preferably, the neural network has a NeuroMem™ architecture.

[0075] For traffic flow determination, low and constant latency is a very desirable feature as it guarantees high and predictable results. With Deep Learning, latency varies. Typically, the more the system learns, the slower it becomes. This is due to the Von Neumann architecture bottlenecks found in all computers which run sequential programs. Even the most modern multi-core architectures, even the best GPU or VPU architectures have limitations to their parallelism because some resources (cache, external memory access, bus access, etc.) are shared between the cores and therefore limit their true parallelism. The NeuroMem™ architecture goes beyond the Von Neumann paradigm and, thanks to its in-memory processing and fully parallel nature does not slow down when the training dataset grows. In fact, any environment which needs on-the-job learning, fast and predictable latency, easy auditing of decisions is likely to be better served by RBF neural networks, rather than by Deep Learning neural networks.

[0076] FIG. 15 shows a block logic diagram of DDP 500, CM 515 The DDP 500 contains CM 515 that controls all aspects of the DDP 500 to include: DDP enclosure or CC 501 opening and closing, DDP battery pack 516 recharging, drone battery 618 recharging and communications capabilities from other traffic sensor systems, central monitoring stations, first responder personnel and to act as a relay communications device to the drone in flight and/or other drones in flight in the near vicinity. CM 515 would relay video signals to the central monitoring center and provide for video recording at or in close proximity to the CM 515. CM 515 would also relay flight or camera control signals and audio commands from the central monitoring center to the drone 600 in flight, giving central monitoring center personnel the ability to override autonomous drone flight control should they desire. For example, CM 515 receives a traffic alert from a Traffic Flow Sensor System (TFSS) of a nearby traffic accident. The TFSS is a separate device and consists of an EO/IR camera, stereo camera pair, lidar and/or radar sensors and any combination thereof to detect and monitor traffic flow and abnormal traffic flow to include traffic incidence. Upon the TFSS issuing a traffic alert of an incident or accident, CM 515 initiates a signal to a central monitoring center, and the FAA for flight approval. Once approved, CM 515 signals DDP 500 to open the DDP enclosure CC 501 and when open to start the drone propellers 606 and commence autonomous drone flight—to takeoff, fly to and hover over the accident, take photographs and videos of the scene and assist in accident scene forensics and to assist police in clearing the scene more rapidly, so as to resume normal traffic flow. Central monitoring center personnel have the ability to override the autonomous drone control at any time to aid in the resolution and clearing of traffic incidence. Designated emergency personnel with first-hand knowledge of the incident would also have the ability to override the autonomous drone control at any time to aid in the resolution and clearing of traffic incidence through their remote control devices or cell phone apps at the incident scene. LPM 700 LED signal light controller also communicates directly with autonomous or semiautonomous vehicles for a signal light status or change. The communication is selected from the group consisting of a Bluetooth communication, LoRa Communication, an internet communication, a cell phone network communication (4G/5G), an independent intranet network communication, an RF communication, a wired communication, or an optic fiber communication. Preferably, the data, video, audio and remote control commands are communicated or streamed in real time with very low latency in both directions—to and from the deployed drone 600, DDP 500 and central monitoring center. In the event of a malfunction, a malfunction signal or code will be sent to the central traffic control monitoring center for resolution.

[0077] FIG. 16 shows a Remote Control Unit (RCU) 390 in another embodiment.

[0078] As explained above, various embodiments of the present invention use similar technology as implemented in consumer drones or cell phones with very small, lightweight, low power and low price (SWAP) components and powered by solar panels and rechargeable batteries. Coupled with LED's as traffic signals and overhead lighting, drone deployment from drone docking ports could substantially reduce the time and costs involved in resolving traffic incidents or accidents at the scene, direct traffic around the accident more efficiently, saving drivers time, fuel and cost and potentially save lives.

[0079] An advantage of the disclosed drone docking port is the ability to place (especially autonomous) drones in strategic locations along highways or traffic intersections conducive to rapid deployment to incidents, events and/or traffic accidents as first responders. These autonomous drones would reside in their drone docking ports until an incident arises, then be deployed, providing emergency and central monitoring center personnel live video of the scene with the ability to provide two way audio to injured or other persons, then to aid emergency personnel in directing vehicle traffic efficiently and safely around an incident and resolving the incident in a timely fashion.

[0080] The present invention may be embodied in other specific forms without departing from its spirit or essential characteristics. The described embodiments are to be considered in all respects only as illustrative and not restrictive. The scope of the invention is, therefore, indicated by the appended claims rather than by the foregoing description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.