Aerospace and Electronic Systems Magazine September 2016 - 30
Feature Article:
DOI. No. 10.1109/MAES.2016.150131
An Autonomous Quadrotor Avoiding a Helicopter in
Low-Altitude Flights
Zhilong Liu, Aislan Gomide Foina, University of California, Berkeley, Berkeley, CA, USA
INTRODUCTION
Recent advances in sensing and computing technology has made
unmanned aerial vehicles/systems (UAV/UAS) low cost but still
increasingly capable of executing complex missions in challenging environments. They have gained popularity in a vast range of
civilian applications, including search and rescue, disaster relief,
and filming. Recently, the Federal Aviation Administration (FAA)
has issued the Notice of Proposed Rulemaking (NPRM) on UAS
certifications [1], indicating that a large number of UAS will be
present in the National Airspace System (NAS) in the near future.
The NASA UTM project is an effort on enabling low-altitude UAS
flights [2]. Big value envisaged by Amazon Prime Air [3] happens
only when the drone is able to fly itself tens of miles from the distribution center to people's homes autonomously. One prerequisite for
such flights is collision avoidance. Our research aims at drones that
travel in class G airspace. This article is an exploration of the drone
collision avoidance problem in urban areas. The main contribution
is a safety control framework that enables a UAS to perform collision avoidance with manned aircraft during autonomous navigation.
To formulate the collision avoidance problem, we first identify
the manned and unmanned aircraft of interest. Airspace management proposals from the FAA [1] and corporations such as Google
[4] and Amazon [3], [5] envisage drones flying below 500 ft, or
150 m in class G airspace, i.e., below all the aircraft carrying people. At this altitude, emergency flights involving news, police, and
emergency medical service (EMS) helicopters occupy a majority
of the possible flights [4]. On the other hand, UAS flying in urban
areas need vertical take-off, landing, hovering ability, and omnidirectional maneuverability. This makes most urban UAS multirotors, and quadrotors are the simplest type of multirotors. Hence we
focus on collision avoidance between quadrotors and helicopters.
For safety critical type of application, it is important to capture
the worst case scenario. Although airspace design can efficiently
Authors' address: University of California, Berkeley, Civil and
Environmental Engineering, 604 Davis Hall, Berkeley, CA
94720. E-mail: (lzl200102109@gmail.com).
Research supported in part by NASA UARC Contract # UCSCMCA-14-020 and NSF Contract # CNS-1136141.
Manuscript received July 15, 2015; revised January 7, 2016,
April 5, 2016; ready for publication May 2, 2016.
Review handled by G. Fasano.
0885/8985/16/a26.00 © 2016 IEEE
30
separate helicopters from drones, the separation is not 100% guaranteed in urgent scenarios such as helicopter emergency flights,
which accounts for a majority of low-altitude helicopter missions.
In addition, it is not safe to assume exclusive avoidance responsibility to human pilots because pilot errors account for 85% of
crashes in general aviation [6]. To ensure safety, the quadrotor
must take avoidance actions in the last minute, before a collision
becomes unavoidable.
In general, sense-and-avoid (SAA) technology could be divided into two categories, namely, collaborative and noncollaborative
[3]. Noncollaborative SAA relies on remote sensing technologies
to detect obstacles. Aircraft with these sensors could detect a wide
range of obstacles. However, for small UAS (sUAS) less than 50
lb [1], we have limited payload capacity and detection range. Some
state-of-the-art sensors with direct distance measurements are
small radars [7], lidars [8], and 3D cameras [9], with ranges of 400
m, 60 m, and 30 m, respectively. Significant UAS SAA research
has been conducted on them [10]-[14]. Monocular cameras may
or may not give distance information, depending on the detection
algorithms. For static obstacles, distance information is possible
via monocular simultaneous localization and mapping (SLAM)
[15], [16]. However, for moving objects, only the optical-flow algorithm is available [17]-[19], with no distance information. Other
research efforts focus on fusion of both types of sensors [20]-[22].
On the other hand, collaborative SAA relies on vehicle-tovehicle (V2V) long-range communication, broadcasting, and subscribing GPS-based traffic data of aircraft nearby. Google provides
a discussion on collaborative SAA systems in [4]. The transceiver
candidates are dedicated short-range communications (DSRC)
and automatic dependent surveillance-broadcast (ADS-B). The
communication range goes from 900 m (DSRC) [23] to 24 km
(ADS-B) [24]. However, collaborative SAA also has several shortcomings. The first one is communication delay. ADS-B Out has a
bounded transmission latency of up to 2 s [25]. Other WiFi-based
technologies such as WiMax and 4G LTE suffer from insufficient
geo-spatial coverage and indefinite/unpredictable communication
delays and are not suitable for safety critical communications [26].
Second, the position information is obtained from GPS, typically
accurate to 5 m near ground [27]. In addition, obstacles outside the
network are not detected.
The collision avoidance problem of interest involves highspeed vehicles. Typical quadrotors and helicopters can travel at
30 m/s and 70 m/s, respectively. The 400 m detection range from
noncollaborative SAA implies a 4 s reaction time under perfect
IEEE A&E SYSTEMS MAGAZINE
SEPTEMBER 2016
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