Special Issue on Unmanned Aerial Vehicles (UAVs)

: This editorial paper was a special issue of Applied Sciences belonging to the section of mechanical engineering in MDPI journal and summarized the collected manuscripts regarding the unmanned aerial vehicles (UAVs) related technologies, including communication, control, collision avoidance, modeling, path planning, human-machine interface (HMI), artiﬁcial intelligence (AI), etc. Chronologically, this special issue was started to be coordinated at the end of Oct 2018, prepared for a month and opened to collect manuscripts from the middle of Nov 2018 until the end of Dec 2019. During almost a year, 26 papers were published online out of 50 submitted papers which results in 52% acceptance rate.

There are two papers focused on UAV communication technology [1,2]. D. Shin, K. Park, and M. Park present the risk assessment on autonomous vehicular V2V communication [1]. J. Lee, W. Lee, H. Kim, and H. Kim present an adaptive TCP transmission adjustment technique for the robust UAV communication network build-up [2].
There are eight papers focused on UAV control technology [3][4][5][6][7][8][9][10]. S. Sato, H. Yokoyama, and A. Lida present plasma actuators were introduced to simplify the complex feathering motion and to control the flow around insect's flapping wings having simplified sinusoidal motion [3]. T. Huang, H. Jiang, Z. Zou, L. Ye, and K. Song propose an integrated robust adaptive Kalman filter, called the strong adaptive Kalman filter (SAKF), to solve the divergence and low accuracy issues of the existing Kalman filter (KF) for the high-speed UAV application [4]. S. Yeom and I. Cho present an interacting multiple model (IMM) filtering based empirical method to detect and track moving pedestrians using a small UAV (SUAV) in which the detection algorithm is consisted of frame subtraction, thresholding, morphological filter, and false alarm reduction processes and the tracking algorithm uses the output from the detection algorithm processes [5]. A. T. Nguyen, N. Xuan-Mung, and S. Hong present a step by step empirical processes for the adaptive trajectory tracking control of SUAV, in particular, by using the Euler-Lagrange based backstepping control technique [6]. Z. Ma, X. Zhu, Z. Zhou, X. Zou, and X. Zhao present a lateral-directional control method for the high aspect ratio full-wing UAV without any aileron and rudder using the differential thrust of propellers as the control output and the yaw angle as the controlled attitude angle [7]. H. Chuang, D. He, and A. Namiki present a direct visual servoing system for the UAV using a single onboard high-speed monocular camera to accomplish the autonomous flight in GPS denied area [8]. N. Xuan-Mung and S. K. Hong present a robust backstepping trajectory control of the SUAV based on both the parameterized uncertainties and external disturbances by solving the control input saturation issue with a new auxiliary system [9]. Q. Zhang, X. Chen and D. Xu present an adaptive neural fault-tolerant tracking control scheme for the yaw control of the multicopter type UAV, which is a non-affine nonlinear system, to manage actuator faults, input saturation, full-state constraints, and external disturbances [10].
There is one paper focused on UAV collision avoidance technology [11]. Y. Wan, J. Tang, and S. Lao present a collision avoidance (CA) algorithm for a cooperative fixed-wing UAV based on the maneuver coordination and planned trajectory prediction by generating and predicting three trajectory candidates [11].
There is one paper focused on UAV modeling technology [12]. M. Wu and M. Chen present nonlinear dynamic modeling of a small-scale compound helicopter with the first physical principles and linear modification method by considering the ducted fan, free-rotate wing, and horizontal stabilizer [12].
There are six papers focused on the UAV path planning technology [13][14][15][16][17][18]. F. Yan, X. Zhu, Z. Zhou, and J. Chu present an empirical hierarchical mission planning to decouple and solve the simultaneous arrival of a multi-UAV coalition problem [13]. S. Xie, A. Zhang, W. Bi, and Y. Tang present an improved mathematical model for UAV mission allocation by considering the difference in each target's importance, constraints of the time window, and an indicator of reconnaissance reward [14]. Z. Shao, F. Yan, Z. Zhou, and X. Zhu present the distributed cooperative particle swarm optimization (DCPSO) algorithm with an elite keeping strategy to generate a safe flyable path of a single UAV in the multi-UAV rendezvous mission [15]. S. Jung presents the energy-efficient path planning by considering the distance between waypoint nodes, the minimum and maximum speed of the UAV, the weight of the UAV, and the angle between two intersection edges [16]. M. Zhu, X. Zhang, H. Luo, G. Wang, and B. Zhang present step by step optimization processes to achieve the safe shortest Dubins path using a multi-UAV rapid-assessment routing problem (MURARP) by considering the multi-depot revisit-allowed Dubins team orienteering problem (TOP) with variable profit [17]. M. Sun, X. Ji, K. Sun, and M. Zhu present a flight strategy optimization model for the high-altitude long-endurance (HALE) UAV based on the gravity energy reserving and mission altitude by incorporating integrated models including the five flight path phase models, three-dimensional kinematic model, aerodynamic model, solar irradiation model, and energy store and loss model [18].
There is one paper focused on UAV HMI technology [19]. B. Chen, C. Hua, D. Li, Y. He, and J. Han present an intelligent human-UAV interaction system, the so-called HMI system, by using a deep learning-based action-gesture joint detection system [19].
There are three papers focused on UAV AI technology [20][21][22]. J. Moon and B. Lee present a cooperation framework between the UAV and unmanned ground vehicle (UGV) that connects deep learning techniques and a symbolic planner for heterogeneous robots by using the planning domain definition language (PDDL) planning with natural language-based scene understanding method [20]. W. Yue, X. Guan, and L. Wang present the UAV cooperative search mission for multi-dynamic targets in sea areas using a reinforcement learning (RL) algorithm [21]. H. Ahn, H. Choi, M. Kang, and S. Moon present a machine learning-based framework to detect and monitor abnormal behaviors during UAV swarm operation [22].
There are four papers focused on non-specific UAV technology [23][24][25][26]. W. Choi and S. Jung present the degradation of launch performance caused by the remnants of a missile canister cover with a sabot interface, on interference with adjacent structures [23]. C. Luo, W. Zhao, Z. Du, and L. Yu present the UAV design, implementation, and testing of a soft-landing gear together with a neural network-based control method for replicating avian landing behavior on non-flat surfaces [24]. P. Cwiakala presents an experimental test campaign using a UAV and measuring the obtained UAV positions during different flight tasks and in other operative conditions [25]. F. I. Llerena, A. F. Barranco, J. A. Bogeat, F. Segura, and J. M. Andujar present a method converting a fixed-wing UAV's internal combustion engine into an electric lithium-ion battery-driven remotely piloted aircraft systems (RPASs) [26].

Future UAV Technologies
Although the special issue has been closed, more in-depth research in UAV technologies is expected. It can be anticipated that a more advanced level of autonomy, more increased flight time, and faster and more accurate data processing based on AI technology will be demanded the advanced UAV. Although the UAV technologies are in the autonomy level 2 (semi-autonomous) at the moment, the destination of UAV technology development, level 3 (autonomous), will come sooner than we think.

Conclusions
Based on the collected papers, we could conclude that UAV control and path planning are the most interested in researchers in modern UAV technology development trends. Following this special issue ended at the end of Dec 2019, we are currently collecting the second special issue regarding the UAV related technology, which will end at the end of Dec 2020.