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

2020 ◽  
Vol 33 (18) ◽  
pp. e4628
Author(s):  
Bhisham Sharma ◽  
Mohammad S. Obaidat ◽  
Vinay Sharma ◽  
Kuei-Fang Hsiao
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Collision Avoidance ◽  
Optimal Solutions ◽  
Future Directions ◽  
Aerial Vehicles

scholarly journals A Detailed Survey and Future Directions of Unmanned Aerial Vehicles (UAVs) with Potential Applications

Aerospace ◽  
2021 ◽  
Vol 8 (12) ◽  
pp. 363
Author(s):  
Nourhan Elmeseiry ◽  
Nancy Alshaer ◽  
Tawfik Ismail
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Collision Avoidance ◽  
Design Process ◽  
Precision Agriculture ◽  
Optimization Techniques ◽  
Battery Life ◽  
Future Directions ◽  
Multiple Sensors ◽  
Aerial Vehicles ◽  
Potential Applications

Recently, unmanned aerial vehicles (UAVs), also known as drones, have gained widespread interest in civilian and military applications, which has led to the development of novel UAVs that can perform various operations. UAVs are aircraft that can fly without the need of a human pilot onboard, meaning they can fly either autonomously or be remotely piloted. They can be equipped with multiple sensors, including cameras, inertial measurement units (IMUs), LiDAR, and GPS, to collect and transmit data in real time. Due to the demand for UAVs in various applications such as precision agriculture, search and rescue, wireless communications, and surveillance, several types of UAVs have been invented with different specifications for their size, weight, range and endurance, engine type, and configuration. Because of this variety, the design process and analysis are based on the type of UAV, with the availability of several control techniques that could be used to improve the flight of the UAV in order to avoid obstacles and potential collisions, as well as find the shortest path to save the battery life with the support of optimization techniques. However, UAVs face several challenges in order to fly smoothly, including collision avoidance, battery life, and intruders. This review paper presents UAVs’ classification, control applications, and future directions in industry and research interest. For the design process, fabrication, and analysis, various control approaches are discussed in detail. Furthermore, the challenges for UAVs, including battery charging, collision avoidance, and security, are also presented and discussed.

Intelligent cooperative collision avoidance via fuzzy potential fields

Robotica ◽  
2021 ◽  
pp. 1-20
Author(s):  
Daegyun Choi ◽  
Anirudh Chhabra ◽  
Donghoon Kim
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Collision Avoidance ◽  
Fuzzy Inference ◽  
Potential Fields ◽  
Rule Base ◽  
Local Minima ◽  
Inference System ◽  
Aerial Vehicles ◽  
Different Shapes ◽  
Simulation Results

Summary This paper proposes an intelligent cooperative collision avoidance approach combining the enhanced potential field (EPF) with a fuzzy inference system (FIS) to resolve local minima and goal non-reachable with obstacles nearby issues and provide a near-optimal collision-free trajectory. A genetic algorithm is utilized to optimize parameters of membership function and rule base of the FISs. This work uses a single scenario containing all issues and interactions among unmanned aerial vehicles (UAVs) for training. For validating the performance, two scenarios containing obstacles with different shapes and several UAVs in small airspace are considered. Multiple simulation results show that the proposed approach outperforms the conventional EPF approach statistically.

scholarly journals Distributed Cooperative Avoidance Control for Multi-Unmanned Aerial Vehicles

Actuators ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 1 ◽  
Author(s):  
Sunan Huang ◽  
Rodney Swee Huat Teo ◽  
Wenqi Liu
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Case Studies ◽  
Collision Avoidance ◽  
Hardware In The Loop ◽  
Crucial Issue ◽  
Communication Failure ◽  
Aerial Vehicles ◽  
Avoidance Control ◽  
Velocity Obstacle ◽  
Multi Uav

It is well-known that collision-free control is a crucial issue in the path planning of unmanned aerial vehicles (UAVs). In this paper, we explore the collision avoidance scheme in a multi-UAV system. The research is based on the concept of multi-UAV cooperation combined with information fusion. Utilizing the fused information, the velocity obstacle method is adopted to design a decentralized collision avoidance algorithm. Four case studies are presented for the demonstration of the effectiveness of the proposed method. The first two case studies are to verify if UAVs can avoid a static circular or polygonal shape obstacle. The third case is to verify if a UAV can handle a temporary communication failure. The fourth case is to verify if UAVs can avoid other moving UAVs and static obstacles. Finally, hardware-in-the-loop test is given to further illustrate the effectiveness of the proposed method.

Design and flight-stability analysis of a closed fixed-wing unmanned aerial vehicle formation controller

2018 ◽  
Vol 233 (8) ◽  
pp. 1045-1054 ◽  
Author(s):  
Jialong Zhang ◽  
Bing Xiao ◽  
Maolong Lv ◽  
Qiang Zhang
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Collision Avoidance ◽  
High Speed ◽  
Physical Simulation ◽  
Controller Design ◽  
Formation Flight ◽  
External Factors ◽  
Flight Stability ◽  
Aerial Vehicles ◽  
Closed Formation

This article addresses a flight-stability problem for the multiple unmanned aerial vehicles cooperative formation flight in the process of the closed and high-speed flight. The main objective is to design a cooperative formation controller with known external factors, and this controller can keep the consensus of attitude and position and reduce the communication delay between any two unmanned aerial vehicles and increase unmanned aerial vehicles formation cruise time under the known external factors. Known external factors are taken into consideration, and longitude maneuvers using nonlinear thrust vectors were employed with unsteady aerodynamic models, according to the attitude and position of unmanned aerial vehicles, which were employed as corresponding input signals for studying the dynamic characteristics of unmanned aerial vehicles formation flight. In addition, the relative distance between any two unmanned aerial vehicles was not allowed to exceed their safe distance so that the controller could perform collision avoidance. An analysis of formation flight distance error shows that it converged to a fixed value that well ensured unmanned aerial vehicles formation flight stability. The experimental results show that the controller can improve the speed of a closed formation effectively and maintain the stability of formation flight, which provides a method for closed formation flight controller design and collision avoidance for any two unmanned aerial vehicles. Meanwhile, the effectiveness of proposed controller is fully proved by semi-physical simulation platform.

Optimized artificial potential field algorithm to multi-unmanned aerial vehicle coordinated trajectory planning and collision avoidance in three-dimensional environment

2019 ◽  
Vol 233 (16) ◽  
pp. 6032-6043 ◽  
Author(s):  
Jun Tang ◽  
Jiayi Sun ◽  
Cong Lu ◽  
Songyang Lao
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Collision Avoidance ◽  
Unmanned Aerial Vehicle ◽  
Trajectory Planning ◽  
Potential Field ◽  
Three Dimensional ◽  
Artificial Potential Field ◽  
Aerial Vehicles ◽  
Aerial Vehicle ◽  
Vehicle Trajectory

Multi-unmanned aerial vehicle trajectory planning is one of the most complex global optimum problems in multi-unmanned aerial vehicle coordinated control. Results of recent research works on trajectory planning reveal persisting theoretical and practical problems. To mitigate them, this paper proposes a novel optimized artificial potential field algorithm for multi-unmanned aerial vehicle operations in a three-dimensional dynamic space. For all purposes, this study considers the unmanned aerial vehicles and obstacles as spheres and cylinders with negative electricity, respectively, while the targets are considered spheres with positive electricity. However, the conventional artificial potential field algorithm is restricted to a single unmanned aerial vehicle trajectory planning in two-dimensional space and usually fails to ensure collision avoidance. To deal with this challenge, we propose a method with a distance factor and jump strategy to resolve common problems such as unreachable targets and ensure that the unmanned aerial vehicle does not collide into the obstacles. The method takes companion unmanned aerial vehicles as the dynamic obstacles to realize collaborative trajectory planning. Besides, the method solves jitter problems using the dynamic step adjustment method and climb strategy. It is validated in quantitative test simulation models and reasonable results are generated for a three-dimensional simulated urban environment.

scholarly journals Collective Perception Using UAVs: Autonomous Aerial Reconnaissance in a Complex Urban Environment

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2926
Author(s):  
Petr Stodola ◽  
Jan Drozd ◽  
Karel Šilinger ◽  
Jan Hodický ◽  
Dalibor Procházka
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Urban Environment ◽  
Computer Simulations ◽  
Optimal Solutions ◽  
Uneven Terrain ◽  
Area Of Interest ◽  
Aerial Vehicles ◽  
Benchmark Instances ◽  
Aerial Reconnaissance

This article examines autonomous reconnaissance in a complex urban environment using unmanned aerial vehicles (UAVs). Environments with many buildings and other types of obstacles and/or an uneven terrain are harder to be explored as occlusion of objects of interest may often occur. First, in this article, the problem of autonomous reconnaissance in a complex urban environment via a swarm of UAVs is formulated. Then, the algorithm based on the metaheuristic approach is proposed for a solution. This solution lies in deploying a number of waypoints in the area of interest to be explored, from which the monitoring is performed, and planning the routes for available UAVs among these waypoints so that the monitored area is as large as possible and the operation as short as possible. In the last part of this article, two types of main experiments based on computer simulations are designed to verify the proposed algorithms. The first type focuses on comparing the results achieved on the benchmark instances with the optimal solutions. The second one presents and discusses the results obtained from a number of scenarios, which are based on typical reconnaissance operations in real environments.

Sign in / Sign up

Export Citation Format

Share Document