Article
UAV Path Planning for Reconnaissance and Look-Ahead
Coverage Support for Mobile Ground Vehicles
Herath M.P.C. Jayaweera *
,†
and Samer Hanoun
†
Citation: Jayaweera, H.M.P.C.;
Hanoun, S. UAV Path Planning for
Reconnaissance and Look-Ahead
Coverage Support for Mobile Ground
Vehicles. Sensors 2021, 21, 4595.
https://doi.org/10.3390/s21134595
Academic Editor: George
Nikolakopoulos
Received: 28 May 2021
Accepted: 28 June 2021
Published: 5 July 2021
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Institute for Intelligent System Research and Innovation, Deakin University, Melbourne, VIC 3125, Australia;
samer.hanoun@deakin.edu.au
* Correspondence: pherathmudiyans@deakin.edu.au
† Current address: 75 Pigdons Road, Waurn Ponds, VIC 3216, Australia.
Abstract:
Path planning of unmanned aerial vehicles (UAVs) for reconnaissance and look-ahead
coverage support for mobile ground vehicles (MGVs) is a challenging task due to many unknowns
being imposed by the MGVs’ variable velocity profiles, change in heading, and structural differences
between the ground and air environments. Few path planning techniques have been reported in
the literature for multirotor UAVs that autonomously follow and support MGVs in reconnaissance
missions. These techniques formulate the path planning problem as a tracking problem utilizing
gimbal sensors to overcome the coverage and reconnaissance complexities. Despite their lack of
considering additional objectives such as reconnaissance coverage and dynamic environments, they
retain several drawbacks, including high computational requirements, hardware dependency, and
low performance when the MGV has varying velocities. In this study, a novel 3D path planning
technique for multirotor UAVs is presented, the enhanced dynamic artificial potential field (ED-APF),
where path planning is formulated as both a follow and cover problem with nongimbal sensors. The
proposed technique adopts a vertical sinusoidal path for the UAV that adapts relative to the MGV’s
position and velocity, guided by the MGV’s heading for reconnaissance and exploration of areas and
routes ahead beyond the MGV sensors’ range, thus extending the MGV’s reconnaissance capabilities.
The amplitude and frequency of the sinusoidal path are determined to maximize the required look-
ahead visual coverage quality in terms of pixel density and quantity pertaining to the area covered.
The ED-APF was tested and validated against the general artificial potential field techniques for
various simulation scenarios using Robot Operating System (ROS) and Gazebo-supported PX4-SITL.
It demonstrated superior performance and showed its suitability for reconnaissance and look-ahead
support to MGVs in dynamic and obstacle-populated environments.
Keywords: UAV path planning; artificial potential field; reconnaissance and look-ahead coverage
1. Introduction
Unmanned aerial vehicles (UAVs) are aircrafts that fly without a pilot onboard. They
have been extensively deployed for assisting in military missions such as
reconnaissance [1],
surveillance [
2
], and combat operations [
3
]. Recently, UAVs have been utilized in other
sectors supporting different commercial [
4
–
8
], environmental [
9
], and leisure [
4
] ap-
plications. Examples include monitoring of construction sites [
10
], inspection of civil
infrastructures [10]
, surveying powerlines [
7
], mapping gas pipelines [
11
], counting agri-
culture livestock [
5
], assisting with forest fires [
9
], and mostly in cinema and photography
for professional and leisure purposes [
4
]. In these applications, the UAV is usually equipped
with appropriate thermal and visual sensors to effectively capture live views and photos
for the objects and areas of interest, which are either stored onboard or relayed to a base
station for further online and offline analysis.
Classification of UAVs can be based generally on their wing configuration as fixed-
wing, rotary-wing, and hybrid. Unlike fixed-wing UAVs, rotary-wing UAVs are less
Sensors 2021, 21, 4595. https://doi.org/10.3390/s21134595 https://www.mdpi.com/journal/sensors
