7.4 -
Research Assignment: Sense and Avoid Sensor Selection
An effective sense-and-avoid system will need
multiple sensors able to detect manned and unmanned aircraft equipped with
transponders broadcasting their position and those who aren't equipped or who
refuse to broadcast their position.
A number of sense-and-avoid systems for UAS
contain four primary types of sensors: Traffic Alert & Collision Avoidance
system, better known as T-CAS, Automatic
Dependent Surveillance-Broadcast, known as ADS-B, electro-optical
sensors and radar.
T-CAS is a suite of airborne sensors designed
to provide collision avoidance protection to aircraft. The system provides
traffic advisories and recommends escape maneuvers (FAA, 2011). ADS-B is a
precise satellite based system using GPS to define an aircraft's location and
airspeed. The system then transmits this information to a network of ground
stations. The ground stations in turn transmit the data to air traffic control
as well as nearby aircraft equipped to receive the data via ADS-B (FAA, 2016). Electro optical cameras are
sensors which detect light across a large spectrum and effectively enable the
platform to 'see' (Keller, 2013).
The U.S. military recognized as early as
2001, the necessity for sense and avoid capabilities for its UAV program. The
military employ a series of algorithms to collect and interpolate the data into
a 'picture' for situational awareness for the pilot and the computer. While the
Air Force has been researching airborne sense and avoid systems the Army has
been researching ground based systems. The idea is to eventually tie both
systems together to enable a UAS to fly anywhere (Insinna, 2014).
One such sense and avoid system under
development is a project within DARPA's Fast, Lightweight, Autonomy (FAST)
program. "DARPA’s FLA program aims to develop and test algorithms that
could reduce the amount of processing power, communications, and human
intervention needed for unmanned aerial vehicles (UAVs) to accomplish low-level
tasks, such as navigation around obstacles in a cluttered environment"
(DARPA, 2016).
There are micro UAVs capable of flying fast
with significant agility but they are too small to contain the current sense
and avoid technology. There are also extremely large military UAVs loaded with
sensor suites able to provide a significant sense and avoid system. DARPA is
attempting to find a middle ground which would provide key sense and avoid
capabilities for smaller UAVs.
DARPA's FAST quad copter was designed to
autonomously navigate through a crowded indoor environment. The system is
designed to be small enough to enter through a window and operate in a GPS
deprived environment. One advancement DARPA succeeded in demonstrating was the
use of sensors to navigate corridors, stairs and obstacles while using
significantly less processing power. The quad copter demonstrated its ability
to recognize or sense obstacles and fly around them.
The quad copter was also equipped with LIDAR,
Sonar, inertial measurement sensors and several cameras. The platform is
designed to operate autonomously which includes planning, sensing, perception
and flight control. The next phase calls for more sophisticated algorithms
allowing the quad copter to recognize or see obstacles from a greater distance
and successfully navigate them at greater speeds. The plan also calls for more
agility demonstrated via tight turns and sudden moves at high speeds.
DARPA's plan, once the sense and avoid system
is operational for UAS, is to export the technology for use on UGS and UMS.
For this project, DARPA used a DJI Flamewheel
450 airframe, E600 motors with 12" propellers and 3DR Pixhawk autopilot.
The DJI Framewheel 450 has the following
specifications:
Frame weight:
282 g
Diagonal Length: 450 mm
Price:
from as low as $32 (GetFPV, 2016)
The E600 motor with 12" propellers has
the following specifications:
Recommended load: 600 g/axis
Maximum thrust: 1600 g/axis
Voltage:
14.8 V - 25.2 V
Weight:
90 g
Price:
Not available in my region (DJI, 2016)
The 3DR Pixhawk autopilot has the following
specifications:
3 microprocessors
4 sensors
10 interfaces
Power system
Software (Newegg, 2016)
References:
DJI Store. (2016). Retrieved from https://store.DJI.com/product/E600-6
DARPA, FLA Program Takes Flight. (2016,
February 12). Retrieved from http://www.darpa.mil/news-events/2016-02-12
FAA, Automatic Dependent
Surveillance Broadcast (ADS-B) (2016, June 30). Retrieved from http://www.faa.gov/nextgen/programs/adsb/
FAA, Introduction to TCAS II, Version 7.1.
(2011, February 28). Retrieved from http://www.faa.gov/documentLibrary/media/Advisory_Circular/TCAS%20II%20V7.1%20Intro%20booklet.pdf
GetPFV.com. (2016). Retrieved from
http://www.getfpv.com/dji-flamewheel-f450-basic-kit.html?utm_source=google_shopping&m=simple&gdffi=747627bc7e464db68e5f529d971aacf3&gdfms=62AA982B9F454C1190FB68274ECB5D47&gclid=CLexhbvF_s0CFVRahgodThYB4g
Insinna, V. (2014, May). National Defense.
Military, Industry Racing to Create Sense-and-Avoid Systems. Retrieved from http://www.nationaldefensemagazine.org/archive/2014/may/pages/military,industryracingtocreatesense-and-avoidsystems.aspx
Keller, J. (2013, October 8). Electro-optical
sensor payloads for small UAVs. Retrieved from http://www.militaryaerospace.com/articles/print/volume-24/issue-10/technology-focus/electro-optical-sensor-payloads-for-small-uavs.html
Newegg. Pixhawk flight Controller board 3d Robotics 32 Bit Autopilot
System good for FL. (2016). Retrieved from http://www.newegg.com/Product/Product.aspx?Item=9SIA7BF34S0056&nm_mc=KNC-GoogleMKP-PC&cm_mmc=KNC-GoogleMKP-PC-_-pla-_-3D+Printers+Accessories-_-9SIA7BF34S0056&gclid=CKOjjp_D_s0CFcxahgodf9YB8g&gclsrc=aw.ds
