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Unmanned aerial vehicles (UAVs) have a wide variety of applications such as surveillance and search. Many of these tasks are better executed by multiple UAVs acting as a group. One of the main problems to be tackled in a high‐density UAV traffic scenario is that of collision avoidance among UAVs. The purpose of this paper is to give a collision avoidance algorithm to detect and resolve the conflicts of projected path among UAVs.

The collision avoidance algorithm developed in the paper handles multiple UAV conflicts by considering only the most imminent predicted collision and doing a maneuver to increase the line‐of‐sight rate to avoid that conflict. After the collision avoidance maneuver, the UAVs fly to their destinations via Dubins shortest path to minimize time to reach destination. The algorithm is tested on realistic six degree of freedom UAV models augmented with proportional‐integral controllers to hold altitude, velocity, and commanded bank angles.

The paper shows, through extensive simulations, that the proposed collision avoidance algorithm gives a good performance in high‐density UAV traffic scenarios. The proposed collision avoidance algorithm is simple to implement and is computationally efficient.

The algorithm developed in this paper can be easily implemented on actual UAVs.

There are only a few works in the literature that address multiple UAV collision avoidance in very high‐density traffic situations. This paper addresses very high‐density multiple UAV conflict resolution with realistic UAV models.

As a standard procedure of human factors engineering, the design of complex systems (e.g., operator interfaces) starts with analyses of system objectives, missions, functions, and tasks. Perceptual Control Theory (PCT) provides a theoretical framework for guiding this process. PCT is founded on notions from control theory, in which closed-loop, negative-gain, feedback systems can be used to build powerful models of goal-directed behavior and for implementing complex systems (Powers, 1973). One of the strengths of PCT over competing human behavior theories is that it explains how humans can control systems that are subject to a wide variety of external influences. UAV control is through the operators’ interaction with the interfaces in remote control stations. A closed-loop feedback system is crucial for both operators and control systems to understand the states and goals of each other. It is likely that advanced UAV control systems will require operators to interact with automated systems such as IAIs. IAIs are sophisticated and will require knowledge about mission goals, the operators’ goals, and states, as well as the UAV and environmental states. Thus, the methods of analysis used in this research were based on PCT given its engineering origins in control theory and advantages accommodating various external disturbances.

The purpose of this paper is to develop a distributed and integrated method to get a fast and feasible solution for cooperative mission planning of multiple heterogeneous unmanned aerial vehicles (UAVs).

In this study, the planning process is conducted in a distributed framework; the cooperative mission planning problem is reformulated with some specific constraints in the real mission; a distributed genetic algorithm is the algorithm proposed for searching for the optimal solution; genes of the chromosome are modified to adapt to the heterogeneous characteristic of UAVs; a fixed-wing UAV’s six degrees-of-freedom (DOF) model with a path following method is used to test the proposed mission planning method.

This method not only has the ability to obtain good feasible solutions but also improves the operating rate vastly.

This study is only applied to the case where the communication among UAVs is linked during the mission.

This study is expected to be practical for a real mission because of its fast operating rate and good feasible solution.

This solution is tested on a fixed-wing UAV’s 6-DOF model by a path following method, so it is believable from the perspective of an autonomous UAV guidance and control system.

This paper aims to present a comprehensive review in major research areas of unmanned air vehicles (UAVs) navigation, i.e. three degree-of-freedom (3D) path planning, routing algorithm and routing protocols. The paper is further aimed to provide a meaningful comparison among these algorithms and methods and also intend to find the best ones for a particular application.

The major UAV navigation research areas are further classified into different categories based on methods and models. Each category is discussed in detail with updated research work done in that very domain. Performance evaluation criteria are defined separately for each category. Based on these criteria and research challenges, research questions are also proposed in this work and answered in discussion according to the presented literature review.

The research has found that conventional and node-based algorithms are a popular choice for path planning. Similarly, the graph-based methods are preferred for route planning and hybrid routing protocols are proved better in providing performance. The research has also found promising areas for future research directions, i.e. critical link method for UAV path planning and queuing theory as a routing algorithm for large UAV networks.

The proposed work is a first attempt to provide a comprehensive study on all research aspects of UAV navigation. In addition, a comparison of these methods, algorithms and techniques based on standard performance criteria is also presented the very first time.

The purpose of this paper is to generate optimised trajectories for an unmanned aerial vehicle (UAV) during a forest fire detection mission. It is assumed that the UAV flies 3D curvature-constrained Dubins manoeuvres and has a limited amount of battery energy that prevents it from covering the entire search area in a single trip.

In this paper, the search area is discretised into a grid of multiple targets, and each target assigned with a score that is proportional to the time elapsed since the last UAV visit. This problem, known as Dubins Airplane Orienteering Problem, consists of finding the number and order of targets to visit and the UAV heading at each target that maximises the total trip score without exceeding the UAV battery energy. The solution is found using the Randomised Variable Neighbourhood Search metaheuristic. All target scores are updated after each trajectory generation according to the elapsed time since the last UAV visit.

The proposed approach produced feasible results when generating optimised trajectories for a fire detection mission context where energy battery constraints are important.

The authors carry out the planning of UAV missions with limited amounts of onboard energy such as a real fire detection mission using a single electric propulsion and fixed-wing UAV.

This paper introduces an energy-based approach to the Dubins Airplane Orienteering Problem, which takes into account the UAV performance and energy budget when generating optimised trajectories.

In the tourism industry, unmanned aerial vehicles (UAVs) can perform monitoring and patrol missions to protect assets and tourists at attractions such as coastal areas, canyons, national parks, etc. Such use of UAVs can improve safety and security of tourism attraction and facilitate tourism industry. However, there is a key issue regarding economic investment and efficient operation for actual system implementation. The purpose of this paper is to provide a guideline for supporting economic investment and the efficient operation of UAV system in the tourism industry.

Ideas and methodologies have been proposed to overcome the fundamental limitations of commercial UAVs. A mathematical optimization model is developed to determine the optimal number of UAVs to be purchased, and its operation schedules simultaneously.

Using proposed concept and methodology, UAVs can support long duration of missions. Economic system design as well as the operation schedule is derived with the hypothesis data in Kkot-Ji beach in Korea. The proposed methodology and approach is expected to have huge potential at many tourism attractions to achieve the safety and security of tourists.

The result of this study can facilitate practical use of UAVs in the tourism industry. Furthermore, it is applicable in many industries that need UAV systems such as national defence, agriculture, disaster management, etc.

The proposed study suggests a solution to handle fundamental weakness of UAVs and support long duration of missions. In addition, this study incorporates economic system design issue and operation issue simultaneously.

This paper aims to realize a fully distributed multi-UAV collision detection and avoidance based on deep reinforcement learning (DRL). To deal with the problem of low sample efficiency in DRL and speed up the training. To improve the applicability and reliability of the DRL-based approach in multi-UAV control problems.

In this paper, a fully distributed collision detection and avoidance approach for multi-UAV based on DRL is proposed. A method that integrates human experience into policy training via a human experience-based adviser is proposed. The authors propose a hybrid control method which combines the learning-based policy with traditional model-based control. Extensive experiments including simulations, real flights and comparative experiments are conducted to evaluate the performance of the approach.

A fully distributed multi-UAV collision detection and avoidance method based on DRL is realized. The reward curve shows that the training process when integrating human experience is significantly accelerated and the mean episode reward is higher than the pure DRL method. The experimental results show that the DRL method with human experience integration has a significant improvement than the pure DRL method for multi-UAV collision detection and avoidance. Moreover, the safer flight brought by the hybrid control method has also been validated.

The fully distributed architecture is suitable for large-scale unmanned aerial vehicle (UAV) swarms and real applications. The DRL method with human experience integration has significantly accelerated the training compared to the pure DRL method. The proposed hybrid control strategy makes up for the shortcomings of two-dimensional light detection and ranging and other puzzles in applications.

The most important advance in system design is the development of modeling and simulation methods to predict complex performance before prototypes are developed. New systems are developed in a spiraling approach; as more is learned about the system, design changes are proposed and evaluated. This approach allows the engineering team to “spin out” early versions of the system for preliminary evaluation, permitting changes to be made to the system design without incurring unacceptable cost. Because of the complexity of human performance, current modeling techniques provide only a first approximation. However, it has been demonstrated that even simple, inexpensive modeling approaches are useful in uncovering workload and performance problems related to developing systems (Barnes & Beevis, 2003). More important, these models can serve as the basis for operator simulation experiments that verify and also calibrate the original models. Furthermore, early field tests and system of systems demonstrations that can validate these results under actual conditions are becoming an increasingly significant part of the early design process. Fig. 1 illustrates this interdependence indicating a spiraling process throughout the design starting with simple predictive methods and progressing to more expensive validation methods. These iterations should continue until most of the soldier's variance is accounted for, and before any formal soldier testing is conducted. Fig. 1 presents the ideal combination of techniques; not all systems can be evaluated this thoroughly but more cost-effective modeling and simulation tools combined with realistic field exercises should make this approach more the norm as future unmanned systems are developed (Barnes & Beevis, 2003). In the remainder of this chapter, several case studies are presented to illustrate how the techniques in Fig. 1 have been applied in UAV programs.

The purpose of this paper is to develop a monocular visual measurement system for autonomous aerial refueling (AAR) for unmanned aerial vehicle, which can process images from an infrared camera to estimate the pose of the drogue in the tanker with high accuracy and real-time performance.

Methods and techniques for marker detection, feature matching and pose estimation have been designed and implemented in the visual measurement system.

The simple blob detection (SBD) method is adopted, which outperforms the Laplacian of Gaussian method. And a novel noise-elimination algorithm is proposed for excluding the noise points. Besides, a novel feature matching algorithm based on perspective transformation is proposed. Comparative experimental results indicated the rapidity and effectiveness of the proposed methods.

The visual measurement system developed in this paper can be applied to estimate the pose of the drogue with a fast speed and high accuracy and it is a feasible measurement strategy which will considerably increase the autonomy and reliability for AAR.

The SBD method is used to detect the features and a novel noise-elimination algorithm is proposed. Besides, a novel feature matching algorithm based on perspective transformation is proposed which is robust and accurate.

The purpose of this paper is to propose a new algorithm for in‐mission trajectories and speed adjustment of multiple unmanned aerial vehicles (UAVs) participating in a mission that requires them to arrive at target location simultaneously with switching and imperfect communication among the vehicles.

This algorithm, programmed at each UAV level, is based on the repeated consensus seeking among the participating vehicles about the time‐on‐target (ToT) through an imperfect inter‐vehicle communication link. The vehicles exchange their individual ToT values repeatedly for a particular duration to pick the highest value among all the vehicles in communication. A consensus confidence flag is set high when consensus is successful. After every consensus cycle with high confidence value, the mission adjustment is carried out by computing difference value between ToT consensus and a threshold value. For the difference values higher than a certain limit, vehicle's trajectory is adjusted by in‐mission insertion of new waypoint (WP) and for lower values the vehicle's speed is varied under allowable limits. The consensus seeking followed by the mission adjustment is repeated periodically to quash the imperfect communication effects.

A mathematical analysis has been carried out to establish the conditions for convergence of the algorithm. The simultaneous arrival of the vehicles subjected to switching communication is achieved only when the union of the switching links during the consensus period enables a vehicle to receive information from all the other vehicles and the switching rate is sufficiently high. This algorithm has been tested in a 6‐degree‐of‐freedom (DoF) multiple UAV simulation environment and achieves simultaneous arrival of multiple fixed wing UAVs under imperfect communication links that meets the aforementioned conditions.

The presented algorithm and design strategy can be extended for other types of cooperative control missions where certain variable of interest is shared among all the vehicles over imperfect communication environment. The design is modular in functionality and can be incorporated into existing vehicles or simulations.

This research presents a new consensus algorithm that repeatedly performs polling of ToT among the vehicles through intermittent communication. The continual nature of consensus seeking covers the weakness of the imperfect communication. A two‐level mission adjustment provides better accuracy in simultaneous arrival at the target location.