Geo-Fence Detection System for UAV Airspace to Provide Counter-Autonomy Mitch Campion, Nicholas Christian, Joe Englund, Prakash Ranganathan University of North Dakota, Department of Electrical Engineering
Overview
Components • Tarot 650 Ironman quadcopter builds • 6S 6000-12000mAh • 80KV motors • Pixhawk 2.1 w/ Intel Edison processor • Open source APM/PX4 flight stack • Associated sensors/telemetry • HERE GNSS • DJI Matrice 100 + SDK • ESP8266-12F Wi-Fi Modules • Mission Planner GCS • Various sensors (BME280, GPS, Compass, Gyro, Accelerometer, etc..) • Scanse Sweep Lidar systems • Raspberry Pi companion computers • Long range communication components
New Funded Research Project on UAS Cyber and UAS Big Data 1. Counter Autonomy Drone Detection Project, Rockwell Collins ( 2017-2018) P.I: Dr. Prakash Ranganathan Goal 1. This work focuses on the development hardware and software necessary to deploy autonomous fleets of Unmanned Aerial Vehicles (UAV’s) to provide counterautonomous security and detection. This is achieved through dynamic geo-fencing and advanced Swarm technology. Goal 2. Artificial intelligence based heterogeneous algorithms composed of data from image processing, acoustic sensing, RF scanning, and heat recognition (including FLIR imagery and other sensors) will be developed for the purposes of rogue drone detection and other critical infrastructure security. This is a work that is in preliminary stages. Goal 3. Establish the role of UND Electrical Engineering research capabilities to the UAV industry. Figure 1. UAV swarm block diagram with optional GCS and RC
Hardware Developed
Implementation
Future Directions
Figure 2. Swarm of UAVs with drone-to-drone communication capabilities in addition to GCS communication. Representative diagram of our testing setup Figure 5. Simple payload Wi-Fi transceiver board
Figure 6. Transceiver display. (ex. GCS use)
board
with
o Data security and encryption algorithms for swarm data transmission o Counter-drone detection schemes o Hardware and Software development o UAS payload hardware and software o On-board Image recognition/processing o Commercial applications o Precision Ag, FLIR, inspection systems o Defense Applications o Cyber Security, rogue drone detection, geo-fencing, autonomy o FLIR Imagery Image Recognition Algorithms o Computer vision based technology for FLIR imagery in future (currently nonexisting) industry
Contact For further information regarding this work, please contact: Prakash Ranganathan Ph.D., Director: Secure Cyber Physical Energy Systems & Data Sciences Laboratory University of North Dakota
[email protected]
Figures 3, 4. Matrice 100 (above) and Tarot 650 (right) quads with raspberry pi configurations. Initial configuration and development stages shown.
Acknowledgement Figure 7. Quadcopter breakout board connected to Feather M0 microcontroller and XBee Pro 900 for mesh networking
The authors acknowledge the support of Rockwell Collins for support of this research work.
