Presentation Title

Collision Avoidance for Fixed Wing UAVs Utilizing ADS-B Sensors

Faculty Mentor

Subodh Bhandari

Start Date

17-11-2018 8:30 AM

End Date

17-11-2018 10:30 AM

Location

HARBESON 61

Session

POSTER 1

Type of Presentation

Poster

Subject Area

engineering_computer_science

Abstract

The integration of unmanned aerial vehicles (UAVs) into the national airspace system presents itself with a myriad of technical problems. One of the key requirements for this integration is the human equivalent level of safety, which requires the ability to detect and avoid other aircraft/obstacles in their flight path so that the UAVs complete their mission without any loss or damage to other aircraft or property. This presentation talks about the use of ADS-B (Automatic Dependent Surveillance- Broadcast) transponders for detection of collision of other similarly equipped aircraft/UAVs. ADS-B transponders can receive and broadcast global position and velocities among other pertinent information in a 100-nautical mile radius. This research used Ping-2020 ADS-B transponders for the collision detection. The collision detection and avoidance is first tested in software-in-the-loop simulation, which also uses the flight controller, Ardupilot, and ADS-B transponders in the loop. MAVproxoy, a UAV ground station software package, is used to communicate between in the autopilot and simulation environment via MAVLink. FlightGear flight simulator is used to visualize the motion of the UAVs.

The research uses two fixed-wing aircraft equipped with Pixhawk autopilots, which allow autonomous waypoint navigation. The collision avoidance algorithms use a three-step system of detect, predict, and avoid. The algorithm calculates and sends the waypoints for collision avoidance to the autopilot. Using the kinematic equations, the UAV velocities can be calculated from the information received from GPS sensors, and future positions can be predicted. The collision avoidance algorithm is tested using the incoming information from real-time aircraft.

Summary of research results to be presented

The algorithm has accurately predicted the positions for multiple aircraft up to 10 seconds in the future. As well as calculated an avoid waypoints, which needs to be inputted immediately to avoid other aircraft.

We successfully generated ADS-B traffic in a simulator and are able to control the plane and produce our own waypoints through the ground control station. We can manipulate ADS-B parameters and can use or edit our flight dynamics model within JSBSim to visualize an accurate UAV in flight.

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Nov 17th, 8:30 AM Nov 17th, 10:30 AM

Collision Avoidance for Fixed Wing UAVs Utilizing ADS-B Sensors

HARBESON 61

The integration of unmanned aerial vehicles (UAVs) into the national airspace system presents itself with a myriad of technical problems. One of the key requirements for this integration is the human equivalent level of safety, which requires the ability to detect and avoid other aircraft/obstacles in their flight path so that the UAVs complete their mission without any loss or damage to other aircraft or property. This presentation talks about the use of ADS-B (Automatic Dependent Surveillance- Broadcast) transponders for detection of collision of other similarly equipped aircraft/UAVs. ADS-B transponders can receive and broadcast global position and velocities among other pertinent information in a 100-nautical mile radius. This research used Ping-2020 ADS-B transponders for the collision detection. The collision detection and avoidance is first tested in software-in-the-loop simulation, which also uses the flight controller, Ardupilot, and ADS-B transponders in the loop. MAVproxoy, a UAV ground station software package, is used to communicate between in the autopilot and simulation environment via MAVLink. FlightGear flight simulator is used to visualize the motion of the UAVs.

The research uses two fixed-wing aircraft equipped with Pixhawk autopilots, which allow autonomous waypoint navigation. The collision avoidance algorithms use a three-step system of detect, predict, and avoid. The algorithm calculates and sends the waypoints for collision avoidance to the autopilot. Using the kinematic equations, the UAV velocities can be calculated from the information received from GPS sensors, and future positions can be predicted. The collision avoidance algorithm is tested using the incoming information from real-time aircraft.