Citation: Kim, M.; Yoo, S.; Lee, D.;
Lee, G.-H. Local Path-Planning
Simulation and Driving Test of
Electric Unmanned Ground Vehicles
for Cooperative Mission with
Unmanned Aerial Vehicles. Appl. Sci.
2022, 12, 2326. https://doi.org/
10.3390/app12052326
Academic Editor: Dario Richiedei
Received: 20 January 2022
Accepted: 22 February 2022
Published: 23 February 2022
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Article
Local Path-Planning Simulation and Driving Test of Electric
Unmanned Ground Vehicles for Cooperative Mission with
Unmanned Aerial Vehicles
Mingeuk Kim *, Seungjin Yoo, Dongwook Lee and Geun-Ho Lee
Korea Institute of Machinery and Materials, Daejeon 34103, Korea; seungjinyoo@kimm.re.kr (S.Y.);
lego0410@kimm.re.kr (D.L.); ghlee762@kimm.re.kr (G.-H.L.)
* Correspondence: kmg0702@kimm.re.kr
Featured Application: Local path-planning simulation and experiment using potential field
considering calculation time.
Abstract:
Recently, various studies related to the development of unmanned vehicles have been
conducted around the world. In particular, unmanned ground vehicles (UGVs) and unmanned aerial
vehicles (UAVs) have been developed and utilized for various purposes. In this study, we developed
a method for the path generation of UGVs in a system in which one operator controls many different
types of unmanned vehicles. In the driving control system (DCS), it is necessary to process sensor
data such as GPS/INS and LiDAR when generating a path by receiving the target waypoint from the
ground control station. In addition, the DCS must upload the current location, posture, state, etc., as
well as save driving log. Therefore, in order to recognize obstacles in real time and generate a path, a
safe path generation algorithm with a short computation time is required. Among the various path
generation methods, the potential field algorithm was selected, and the algorithm was modified to
reduce the computation time. The computation time before and after modification of the algorithm
was obtained and compared through simulation, and the algorithm was verified through application
to an actual system by performing an obstacle avoidance experiment and a simultaneous control
experiment for two UGVs.
Keywords: unmanned ground vehicle; path planning; potential field; driving test
1. Introduction
The interest in and demand for unmanned vehicles that perform unmanned mis-
sions have continuously increased; in particular, UAV (unmanned aerial vehicle)-related
technologies are being developed rapidly. However, UAVs have limitations such as short
operating time and small payload, making it difficult to mount various equipment and
to acquire data near the ground. If UGVs (unmanned ground vehicles) perform missions
together, more diverse missions can be performed more efficiently. Such research is actively
underway worldwide. Cantelli [
1
] tried to create a UGV route more precisely using UAV
images, while Grocholsky [
2
] developed a system for local search simultaneously using
both UAVs and UGVs. In addition, in order to overcome the short operation time of UAVs,
Mathew [
3
] studied an operation method considering charging, while Ropero [
4
] studied
how to optimize the UAV’s path for charging.
Since this study is one in which one operator simultaneously operates multiple UAVs
and UGVs, the operator cannot be in the field where the UGVs and UAVs operate. There-
fore, the UGVs are controlled remotely, and an autonomous driving function must be
implemented because one person cannot directly control multiple unmanned vehicles at
the same time.
Appl. Sci. 2022, 12, 2326. https://doi.org/10.3390/app12052326 https://www.mdpi.com/journal/applsci
