We believe the wide adoption of drones to replace current CO2 emitting delivery vans will contribute to a significant reduction of carbon additions to the atmosphere. However, if drones are not able to be adopted safely this valuable reduction of carbon emissions will not be realized. Our project combines the technologies of an FPGA, embedded processor, analog sensors and cloud communications to enable the wide and safe adoption of drones to replace traditional CO2 emitting delivery systems.
We will demonstrate an FPGA + Processor + Sensor technology that enables a scout drone to detect atmospheric upsets such as turbulence generated around buildings in windy conditions or city thermals. Atmospheric upsets of drones can cause drone loss of control (LOC), collisions between drones, and collisions with buildings or people. The scout demonstration will send near real-time turbulence location data via the cloud to cargo drones to ensure safe delivery of packages with a reduced hazard to third parties.
The key technology of turbulence and upset detection for prevention of LOC has already been developed by Foale Aerospace Inc and has been developed as a solar powered sensor system that can be attached to flying vehicles without aircraft wiring or integration. We were awarded 3rd prize by the Experimental Aircraft Association Founders Prize competition to produce solutions to prevent Loss of Control, by expert judges at Air Venture 2021 at Oshkosh, Wisconsin.
We propose to add FPGA signal processing to improve performance, reduce detection times and reduce false positive signals from our system. We propose a cloud interface will allow near real-time (a few seconds latency) hazardous conditions detected by a light scout drone to change the flight path of a cargo drone and prevent a hazardous or unsafe outcome.
We have experience in Verilog, Quartus and Modelsim targeting a Terasic DE0-Nano with a Raspberry Pi Zero processor interface written in Python, as well as Yosys/Arachne-pnr/IceStorm toolchain for Trenz Icezero iCE40 FPGA. We have 30 years of programming experience with C and C++. We will use this experience to breadboard a flight system onto the Terasic-Intel-Analog Devices-Microsoft InnovateFPGA platform. Real flight data recorded during light drone aircraft flights in turbulent and calm conditions will be used to demonstrate the identification of atmospheric conditions that cause aircraft upsets using the InnovateFPGA platform as if it were mounted on a scout drone. Communication hazard bulletins via the cloud to the cargo drone will be demonstrated via a wi-fi link to a raspberry pi based processor mounted on a wheeled rover, to demonstrate hazard bulletin reception and responsive action by a cargo drone in flight.
1. High-level project introduction and performance expectation
Drone package delivery safety in turbulent atmosphere conditions in confined areas like Cities
See our presentation video at https://www.youtube.com/watch?v=mjRCBjk6Ixs
See our demonstration video at https://www.youtube.com/watch?v=VbMiAuBnGYQ
See our project files at https://github.com/cfoale/innovateFPGA2022-AS038
Foale Aerospace Inc Team AS038 members:
Colin Michael Foale
High-level project introduction and performance expectationlevel project introduction and performance expectation
Figure 1: Illustration showing Scout operational deployment preceding a cargo drone with valuable cargo. When the Scout detects hazardous conditions, it causes the cargo to be saved.
We believe the wide adoption of renewable powered drones to replace current CO2 emitting delivery methods will contribute to a significant reduction of carbon additions to the atmosphere. ' Last minute' point to point courier delivery in cities predominantly relies on CO2 emitting services. These services can be replaced by solar powered drones standing by ready to serve. If all the current package delivery methods are replaced with renewable powered drones, more than 2 million tons of CO2 reduction per annum by 2050 could be realized. However, if drones are not able to be adopted safely this valuable reduction of carbon emissions will not be possible.
Our project combines the technologies of the DE10-Nano FPGA, HPS embedded processor, Analog Devices sensors and Microsoft Cloud communications to enable the wide and safe adoption of drones to replace traditional CO2 emitting delivery systems.
We demonstrate a technology that enables a scout drone to detect atmospheric upsets such as turbulence generated around buildings in windy conditions or city thermals. Atmospheric upsets of drones can cause drone loss of control (LOC), collisions between drones, and collisions with buildings or people. Our scout drone hazard detection demonstration shows how near real-time turbulence location data can be detected, processed, and interpreted for distribution via the Microsoft Cloud to cargo drones, to ensure safe delivery of packages with a reduced hazard to third parties.
The key technology of turbulence and upset detection for prevention of LOC has already been demonstrated by us and has been developed as a solar powered sensor system that can be attached to large flying vehicles without aircraft wiring or integration. We were awarded 3rd prize by the Experimental Aircraft Association Founders Prize competition to produce solutions to prevent Loss of Control, by expert judges at Air Venture 2021 at Oshkosh, Wisconsin.
The technology relied on data collection and Digital Signal Processing (DSP) running between each new data measurement, on an ARM processor. We realized an FPGA could provide concurrent DSP assessments without slowing the data flow. This feature is highly desirable to reliably detect abrupt environment hazards. We have developed FPGA based DSP to improve performance, reduce detection times and reduce false positive signals from our system. The use of Digital Signal Processing without data gaps requires the use of concurrent Hardware Description Language running on an FPGA, such as the DE10-Nano.
We use the DE10-Nano Cyclone V FPGA to fetch and correlate data with a sufficient time history for LOC identification and prediction using Verilog HDL on the FPGA. In a pre-operations mode a longer data time history is collected and saved for analysis by passing the data to the DE10-Nano HPS processor over the Intel Avalon bus interface. The longer data history is used to develop the cross-correlation targets that are used by the FPGA HDL DSP module. We use an IoT Python script running on the Linux LXDE on the HPS to pass DSP detected hazard assessments at a 1 second rate to the Microsoft Azure Cloud, which is then available to users.
This innovative FPGA based Scout drone system allows near real-time (a few seconds latency) hazardous environment conditions detected by a light scout drone to be passed to the Cloud and enable a real-time change of a cargo drone delivery, and prevent a hazardous, unsafe outcome or property destruction. When a hazardous condition is reported to the Cloud, 'Cloud Control' can issue a route change as the most likely outcome, or command a delay, similar to the way airline transport is rerouted or delayed due to bad weather. Our solution will provide drone package delivery services a reliable environment hazard detection capability, ensuring that their services can be safely, reliably, and profitably deployed.
2. Block Diagram
Figure 2 shows the key elements of our Scout Hazard Detection System. The system consists of:
DE10-Nano, utilizing the Cyclone V FPGA for data collection and DSP.
DE10-Nano Analog to Digital Conversion (ADC) of Scout sensor data to the FPGA.
A Scout drone sensor measuring accelerations, rotations and dynamic pressure differences caused by atmospheric flight disturbances or turbulence.
A DE10-Nano HPD Python script implementing Microsoft Azure IoT interface to the Microsoft Azure Cloud.
Analog Devices DC2025A-A Digital to Analog Converter (DAC) for data play back to the DE10-Nano FPGA ADC for DSP hazard detection assessment.
A table-top turbulent atmosphere simulator to provide flight-like physical input to the Scout drone sensor assembly, as if it were mounted on a scout drone. The simulator reproduces real flight data recorded during glider flights in turbulent hazardous atmospheric conditions.
These components can be repackaged to meet the physical and flight requirements of a Scout drone. Two different use cases are shown, with emphasis to be applied to Autonomous vehicles and Transportation to achieve the best sustainability improvements for global CO2 reduction.
Figure 2: Scout Hazard Detection System composition, realization, and exploitation
3. Expected sustainability results, projected resource savings
Expected sustainability results, projected resource savings
A 1Kg package transported 1 km by a modern truck emits up to 0.15 g of CO2. Reference https://timeforchange.org/co2-emissions-for-shipping-of-goods/ . The upstream production and delivery CO2 cost per gallon of fuel at the pump is an extra 34%. Reference https://www.sierraclub.org/sierra/ask-mr-green/hey-mr-green-how-much-co2-generated-producing-and-transporting-gallon-gas. In the US in 2020 Amazon delivered 4 billion packages, with a negligible fraction delivered using renewable energy vehicles. Total package delivery by all services was 20 billion packages in the US. Reference https://www.modernretail.co/platforms/amazon-now-ships-more-parcels-than-fedex/. Looking at the reference, demand for next day deliveries within cities is increasing steadily, and in two years it has doubled. CO2 added to the atmosphere today, by all existing package delivery services in the US assuming an average 'last mile' trip of 10 km is roughly 80 million kilograms of CO2 per annum. The US GDP is roughly 14% of worldwide GDP, or 7 times the CO2 produced in the US. In 2022 the global CO2 production caused by 'last mile' delivery services could be 7 times that of the US, 560 million kilograms. By 2050 at least a growth factor of 4 could be expected, meaning a worldwide CO2 production of more than 2 million tons per annum. If drone package delivery can become safe and available, the current use of trucks can be replaced by drone delivery using electricity generated by wind and solar systems. This will not be possible if drone delivery is unsafe or unreliable.
We show significant hazard identification of wind gusts, downdrafts and updrafts acting on a Scout drone. This will encourage drone manufacturers for package delivery to incorporate this capability into their products. We expect the Federal Aviation Administration, and the International Civil Aviation Organization will require this capability in addition to transponder and other traffic control capabilities for drones. The FAA and NASA have airspace improvement studies underway to address drone safety. Our use of FPGA and embedded system technology will introduce a new weather and hazard avoidance capability to future planning for safe and efficient drone operations contributing to the wide adoption of package delivery by drones.
4. Design Introduction
Our design was inspired by the comments of a competition judge who suggested we apply our entry to drones. The event was the Experimental Aircraft Association Founder’s Innovation Loss of Control competition, held at Oshkosh, Wisconsin in 2021. Our entry received 3rd place.
The prior design for aircraft provided only moderate hazard detection reliability
Our design, called Solar Pilot Guard (SPG), employed sensors to measure instantaneous accelerations, rotations, and air stream dynamic pressures about the aircraft to detect atmospheric motions that lead to aircraft upset and loss of control. Figure 3 shows SPG wing and tail sensor assemblies.