Online Path Planning of Autonomous UAVs for Bearing-Only Standoff Multi-Target Following in Threat Environment

Obaidat

et al. 2020

Int J Communication

SummaryRouting remains the most important problem to be explored, for discovering an ideal pathway in unmanned aerial vehicles (UAVs). Several problems for identifying and targeting location continue giving the immense motility of UAVs. In routing planning, optimum decisions have to be taken for several vital operations for solving the issues in UAVs. Many types of routing techniques of UAVs, which have been used for many years, have been analyzed in this paper. Routing techniques aim to provide striking‐free surroundings to the UAVs and not only to find an optimum path but also the smallest path. Routing techniques play a vital part in the development of a secured way in a minimal time to reach the ultimate destination and hence much needed. There are various routing techniques, real‐time applications of UAVs which are elaborated in this paper, namely, representative, cooperative, and noncooperative techniques. In UAV, collision avoidance is very important for the obstacle‐free environment. Collision avoidance techniques are discussed for a safe environment of UAV. Further, the UAVs network communication coverage and connectivity by these techniques are discussed. Finally, we discuss the security and connectivity and future directions in UAV.

Section: Routing Techniquesmentioning

confidence: 99%

Routing and collision avoidance techniques for unmanned aerial vehicles: Analysis, optimal solutions, and future directions

Obaidat

et al. 2020

Int J Communication

“…In order to identify the acceptable release area, it is necessary to first calculate the edges of landing footprint of airborne guided bombs, which consists of the impact points of limiting range (optimal range). Then, the landing footprint area is transformed into LAR in line with the transformation process 15 .…”

Section: Launch Acceptable Regionmentioning

confidence: 99%

“…The UCAV adopted in simulations is the Storm Shadow UCAV. The relevant platform parameters are listed 15 .…”

Section: Simulation Settingsmentioning

confidence: 99%

A Case based Online Trajectory Planning Method of Autonomous Unmanned Combat Aerial Vehicles with Weapon Release Constraints

Tang

Dai

et al. 2020

Def. Sc. Jl.

As a challenging and highly complex problem, the trajectory planning for unmanned combat aerial vehicle (UCAV) focuses on optimising flight trajectory under such constraints as kinematics and complicated battlefield environment. An online case-based trajectory planning strategy is proposed in this study to achieve rapid control variables solution of UCAV flight trajectory for the of delivery airborne guided bombs. Firstly, with an analysis of the ballistic model of airborne guided bombs, the trajectory planning model of UCAVs is established with launch acceptable region (LAR) as a terminal constraint. Secondly, a case-based planning strategy is presented, which involves four cases depending on the situation of UCAVs at the current moment. Finally, the feasibility and efficiency of the proposed planning strategy is validated by numerical simulations, and the results show that the presented strategy is suitable for UCAV performing airborne guided delivery missions in dynamic environments.

“…The intelligence of search and rescue drones is mainly reflected in the path planning ability of autonomous flight and the ability to perform self-determination of tasks. Future drones should be able to autonomously plan flight paths based on their respective missions and corresponding constraints [2], [3]. When the constraints change, the drone will autonomously adjust the flight path.…”

Section: Introductionmentioning

confidence: 99%

UAV Autonomous Target Search Based on Deep Reinforcement Learning in Complex Disaster Scene

et al. 2019

IEEE Access

108

In recent years, artificial intelligence has played an increasingly important role in the field of automated control of drones. After AlphaGo used Intensive Learning to defeat the World Go Championship, intensive learning gained widespread attention. However, most of the existing reinforcement learning is applied in games with only two or three moving directions. This paper proves that deep reinforcement learning can be successfully applied to an ancient puzzle game Nokia Snake after further processing. A game with four directions of movement. Through deep intensive learning and training, the Snake (or self-learning Snake) learns to find the target path autonomously, and the average score on the Snake Game exceeds the average score on human level. This kind of Snake algorithm that can find the target path autonomously has broad prospects in the industrial field, such as: UAV oil and gas field inspection, Use drones to search for and rescue injured people after a complex disaster. As we all know, post-disaster relief requires careful staffing and material dispatch. There are many factors that need to be considered in the artificial planning of disaster relief. Therefore, we want to design a drone that can search and rescue personnel and dispatch materials. Current drones are quite mature in terms of automation control, but current drones require manual control. Therefore, the Snake algorithm proposed here to be able to find the target path autonomously is an attempt and key technology in the design of autonomous search and rescue personnel and material dispatching drones. INDEX TERMS Deep reinforcement learning, Markov decision, Monte Carlo, Q-learning. The associate editor coordinating the review of this manuscript and approving it for publication was Zhanyu Ma.