UCAV Research Articles

Commanding or Being a Simple Intermediary: How Does It Affect Moral Behavior and Related Brain Mechanisms?

Psychology and neuroscience research have shown that fractioning operations among several individuals along a hierarchical chain allows diffusing responsibility between components of the chain, which has the potential to disinhibit antisocial actions. Here, we present two studies, one using fMRI (Study 1) and one using EEG (Study 2), designed to help understand how commanding or being in an intermediary position impacts the sense of agency and empathy for pain. In the age of military drones, we also explored whether commanding a human or robot agent influences these measures. This was done within a single behavioral paradigm in which participants could freely decide whether or not to send painful shocks to another participant in exchange for money. In Study 1, fMRI reveals that activation in social cognition-related and empathy-related brain regions was equally low when witnessing a victim receive a painful shock while participants were either commander or simple intermediary transmitting an order, compared with being the agent directly delivering the shock. In Study 2, results indicated that the sense of agency did not differ between commanders and intermediary, no matter whether the executing agent was a robot or a human. However, we observed that the neural response over P3 event-related potential was higher when the executing agent was a robot compared with a human. Source reconstruction of the EEG signal revealed that this effect was mediated by areas including the insula and ACC. Results are discussed regarding the interplay between the sense of agency and empathy for pain for decision-making.

The Proliferation of Combat Drones in Civil and Interstate Conflicts: The Case of Türkiye and Azerbaijan

Recent developments in conflict zones show the proliferation of Unmanned Aerial Vehicles (UAV) or combat drones and how they transform and shape new warfare. A rapidly growing literature examines UAV’s performance and utilization by the first generation of drone producers and users like the U.S. and Israel. However, the acquisition, proliferation, and production of combat drones by middle and small states and how these new technologies are applied in warfare by these countries have attracted less attention. The paper explores Türkiye as a producer and active user of UAVs in conflicts in Africa and the Middle East and Azerbaijan as a small state that is a consumer of Turkish and Israeli drones. The paper argues that drones provide an advantage on the battlefield, increase precision in wars, and expand the methods used in wars against insurgency; however, the proliferation of drones simultaneously makes states prone to war and so increases the vulnerability of regional peace and security.

NUMERICAL INVESTIGATION OF X-45A TYPE UNMANNED COMBAT AIR VEHICLE

In this paper, the low-speed aerodynamic performance of unmanned combat air vehicle (UCAV) X-45 delta wing was investigated by a numerical method using computational fluid dynamic approaches (CFD). The investigation was conducted with X-45 and the formation of leading-edge vortices (LEV) and vortex breakdown was studied by a varying angle of attack with the range of 5° to 30° at the Reynolds number of 10,000 using the SST turbulence model and are compared with experimental data to validate simulation accuracy of CFD. Stall conditions happened at around 30°, averaged vorticity layer demonstrates a prolonged form that goes along by narrow recirculation zones neighboring to the wing surface. Detail about flow field, including LEV formation, vortex breakdown, interaction, and nonlinear aerodynamic characteristics of X-45 was presented and discussed.

UCAV Air Combat Maneuver Decisions Based on a Proximal Policy Optimization Algorithm with Situation Reward Shaping

Autonomous maneuver decision by an unmanned combat air vehicle (UCAV) is a critical part of air combat that requires both flight safety and tactical maneuvering. In this paper, an unmanned combat air vehicle air combat maneuver decision method based on a proximal policy optimization algorithm (PPO) is proposed. Firstly, a motion model of the unmanned combat air vehicle and a situation assessment model of air combat was established to describe the motion situation of the unmanned combat air vehicle. An enemy maneuver policy based on a situation assessment with a greedy algorithm was also proposed for air combat confrontation, which aimed to verify the performance of the proximal policy optimization algorithm. Then, an action space based on a basic maneuver library and a state observation space of the proximal policy optimization algorithm were constructed, and a reward function with situation reward shaping was designed for accelerating the convergence rate. Finally, a simulation of air combat confrontation was carried out, which showed that the agent using the proximal policy optimization algorithm learned to combine a series of basic maneuvers, such as diving, climb and circling, into tactical maneuvers and eventually defeated the enemy. The winning rate of the proximal policy optimization algorithm reached 62%, and the corresponding losing rate was only 11%.

Study on Delivery of Military Drones and Transport UGVs with Time Constraints Using Hybrid Genetic Algorithms

This paper studies the method of delivering munitions using both drones and UGVs that are developing along with the 4th Industrial Revolution. While drones are more mobile than UGVs, their loading capacity is small, and UGVs have relatively less mobility than drones, but their loading capacity is better. Therefore, by simultaneously operating these two delivery means, each other's shortcomings may be compensated. In addition, on actual battlefields, time constraints are an important factor in delivering munitions. Therefore, assuming an actual battlefield environment with a time limit, we establish delivery routes that minimize delivery time by operating both drones and UGVs with different capacities and speeds. If the delivery is not completed within the time limit, penalties are imposed. We devised the hybrid genetic algorithm to find solutions to the proposed model, and as results of the experiment, we showed the algorithm we presented solved the actual size problems in a short time.

Threat sequencing of multiple UCAVs with incomplete information based on game theory

The threat sequencing of multiple unmanned combat air vehicles (UCAVs) is a multi-attribute decision-making (MADM) problem. In the threat sequencing process of multiple UCAVs, due to the strong confrontation and high dynamics of the air combat environment, the weight coefficients of the threat indicators are usually time-varying. Moreover, the air combat data is difficult to be obtained accurately. In this study, a threat sequencing method of multiple UCAVs is proposed based on game theory by considering the incomplete information. Firstly, a zero-sum game model of decision maker ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{D}$</tex> ) and nature ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{N}$</tex> ) with fuzzy payoffs is established to obtain the uncertain parameters which are the weight coefficient parameters of the threat indicators and the interval parameters of the threat matrix. Then, the established zero-sum game with fuzzy payoffs is transformed into a zero-sum game with crisp payoffs (matrix game) to solve. Moreover, a decision rule is addressed for the threat sequencing problem of multiple UCAVs based on the obtained uncertain parameters. Finally, numerical simulation results are presented to show the effectiveness of the proposed approach.

An adaptive neighborhood-based search enhanced artificial ecosystem optimizer for UCAV path planning

Path planning is an essential component of the unmanned combat aerial vehicle (UCAV) system and is the precondition for achieving tasks such as aerial reconnaissance, monitoring, and fire attack. Its objective is to find a satisfactory route with full consideration of the threat area and constraints, which is a multi-constraint global optimization problem. The work proposes an adaptive neighborhood-based search enhanced artificial ecosystem optimizer (NSEAEO) to address the UCAV path planning problem. The new algorithm uses distance-fitness-based information in the consumption phase to construct an adaptive neighborhood for consumers, who choose the better individuals within the neighborhood for predation. The strategy not only facilitates a thorough search of the problem space but also enhances the global exploration capability of the algorithm. In addition, due to the lack of diversity of the population in the decomposition stage of the AEO algorithm, it is easy to fall into the local extremum. A novel updating decomposition mechanism is designed to dynamically choose the decomposition method according to the threshold in each iteration, which effectively averts insufficient diversity and increases the ability to escape from local extrema. Furthermore, to further improve the search capability of the algorithm, a quadratic interpolation (QI) operator is embedded in the AEO algorithm. The core idea is to choose three agents to match a quadratic function close to the goal function and adopt the extremum of the quadratic function to produce new agents. The above approach achieves a good balance between exploitation and exploration. The experimental results on a series of benchmark functions and UCAV path planning in complex environments demonstrate that NSEAEO outperforms other algorithms in terms of solution quality.

An immune plasma algorithm based approach for UCAV path planning

Immune Plasma algorithm for short IPA is one of the most recent meta-heuristics and differs when the speciality of the process tried to be modeled is considered. The competitive performance of the IPA on different numerical optimization or engineering problems gave inspiration for the studies related with the specializing or porting IPA to more complex real world problems. In this study, IPA was ported in order to solve unmanned combat aerial vehicle (UCAV) path planning problem and its capabilities were investigated in detail by assigning different values to the population size, number of donors-receivers and using challenging battlefield scenarios. The results found by the IPA were also compared with the results obtained by a set of well-known meta-heuristic algorithms. The comparative studies informed that IPA is capable of finding more robust path or paths for the UCAV operating on a battlefield and outperforms other executed algorithms for almost all the test scenarios.

Improving defensive air battle management by solving a stochastic dynamic assignment problem via approximate dynamic programming

Military air battle managers face several challenges when directing operations during quickly evolving combat scenarios. These scenarios require rapid assignment decisions to engage moving targets having dynamic flight paths. In defensive operations, the success of a sequence of air battle management decisions is reflected by the friendly force’s ability to maintain air superiority and defend friendly assets. We develop a Markov decision process (MDP) model of a stochastic dynamic assignment problem, named the Air Battle Management Problem (ABMP), wherein a set of unmanned combat aerial vehicles (UCAV) must defend an asset from cruise missiles arriving stochastically over time. Attaining an exact solution using traditional dynamic programming techniques is computationally intractable. Hence, we utilize an approximate dynamic programming (ADP) technique known as approximate policy iteration with least squares temporal differences (API-LSTD) learning to find high-quality solutions to the ABMP. We create a simulation environment in conjunction with a generic yet representative combat scenario to illustrate how the ADP solution compares in quality to a reasonable, closest-intercept benchmark policy. Our API-LSTD policy improves mean success rate by 2.8% compared to the benchmark policy and offers an 81.7% increase in the frequency with which the policy performs perfectly. Moreover, we find the increased success rate of the ADP policy is, on average, equivalent to the success rate attained by the benchmark policy when using a 20% faster UCAV. These results inform military force management and defense acquisition decisions and aid in the development of more effective tactics, techniques, and procedures.

COMPARING FUEL-OPTIMAL AND SHORTEST PATHS WITH OBSTACLE AVOIDANCE

This paper presents a comparison of fuel-optimal and shortest paths of an unmanned combat aerial vehicle (UCAV) with obstacle avoidance. A nonlinear constrained optimization algorithm is applied to obtain the optimal paths. An initial value problem (IVP) and an inverse-dynamics approach are used separately to determine optimal paths for various scenarios and in order to reduce computation time. While inputs of the optimization algorithm are discrete control variables in the IVP method, discrete state variables are used as inputs in the inverse-dynamics method. The minimized path segments of the geometrical model provide an initial estimation of the heading angle for the aircraft flight mechanics model. The number of variables used by the optimization algorithm has a direct effect upon the optimal accuracy; however, the computation time is inversely proportional to the number of the variables. Simulation results demonstrate that the proposed IVP method effectively converges to optimal solutions.

An approximate dynamic programming approach for solving an air combat maneuvering problem

Within visual range air combat involves execution of highly complex and dynamic activities, requiring rapid, sequential decision-making to achieve success. Fighter pilots spend years perfecting tactics and maneuvers for these types of combat engagements, yet the ongoing emergence of unmanned, autonomous vehicle technologies elicits a natural question — can an autonomous unmanned combat aerial vehicle (AUCAV) be imbued with the necessary artificial intelligence to perform challenging air combat maneuvering tasks independently? We formulate and solve the air combat maneuvering problem (ACMP) to examine this important question, developing a Markov decision process (MDP) model to control a defending AUCAV seeking to destroy an attacking adversarial vehicle. The MDP model includes a 5-degree-of-freedom, point-mass aircraft state transition model to accurately represent both kinematics and energy while maneuvering. An approximate dynamic programming (ADP) approach is proposed wherein we develop and test an approximate policy iteration algorithm that implements value function approximation via neural network regression to attain high-quality maneuver policies for the AUCAV. A representative intercept scenario is specified for testing purposes wherein the AUCAV must engage and destroy an adversary aircraft attempting to penetrate the defended airspace. Several designed experiments are conducted to determine how aircraft velocity and adversary maneuvering tactics impact the efficacy of the proposed ADP solution approach and to enable efficient algorithm parameter tuning. ADP-generated policies are compared to two benchmark maneuver policies constructed from two reward shaping functions found in the ACMP literature, attaining improved mean probabilities of kill for 24 of 36 air combat situations considered.

Particle Swarm Network Design for UCAV Intelligence System Path Planning

In military battle, the unmanned combat aerial vehicle (UCAV) plays a critical role. The UCAV avoids the fatal military zone as well as radars. If there is just a narrow path between the defensive areas, it is dan-gerous. It chooses the quickest and safest path. The balance evolution technique is used to improve the path planning of UCAV in this study, which results in a novel artificial bee colony. To regulate the position of a swarm of UCAVs, a particle swarm network is used to communicate between the UCAVs in the swarm. According to simulation data, the particle swarm network technique is more efficient than the ABC ap-proach. The intelligence system is taught via an artificial neural network.

Particle Swarm Network Design for UCAV Intelligence System Path Planning

In military battle, the unmanned combat aerial vehicle (UCAV) plays a critical role. The UCAV avoids the fatal military zone as well as radars. If there is just a narrow path between the defensive areas, it is dan-gerous. It chooses the quickest and safest path. The balance evolution technique is used to improve the path planning of UCAV in this study, which results in a novel artificial bee colony. To regulate the position of a swarm of UCAVs, a particle swarm network is used to communicate between the UCAVs in the swarm. According to simulation data, the particle swarm network technique is more efficient than the ABC ap-proach. The intelligence system is taught via an artificial neural network.

Modern Warfare, Spiritual Health, and the Role of Artificial Intelligence

Modern warfare utilizes artificial intelligence (AI) combined with remotely piloted aircraft (RPA) to enhance battlefield strategy and create advantages against adversaries. Military drones extend the range of combat, which limits risks, thereby minimizing casualties and loss of human life. With onboard AI systems, drones provide more data and facilitate rapid decision-making for greater situational awareness during conflicts. Military leaders also theorize that remote missions would be physically, emotionally, and psychologically easier for drone pilots, thus reducing mental health issues that plague fighter pilots. However, the intersection of AI with military drones creates unique situations of stress and trauma. RPA personnel manifest symptoms of post-traumatic stress disorder (PTSD) that adversely affect their spiritual health and well-being.

UCAV Path Planning for Avoiding Obstacles using Cooperative Co-evolution Spider Monkey Optimization

The unmanned combat aerial vehicle (UCAV) path planning problem is a complex global optimization problem that aims to seek an optimal flight route that avoids the threats and constraints on the battlefield. However, as the number and degree of the path threats increases, the recent UCAV path planning algorithms all suffer from being trapped into local optima with a low convergence rate, which leads to the poor performance. Therefore, to resolve these problems, we propose a Cooperative co-Evolution (CE)-based Spider Monkey Optimization (SMO) algorithm, named CESMO to address UCAV path planning problem for avoiding obstacles. First, a cooperative co-evolution strategy is proposed to prevent the algorithms from falling into local optima easily, enhancing the coherence of the path. After that, a further division is applied to the algorithm for diversifying the search direction of each individual in the sub-groups. Finally, the spider monkey optimization-based search mechanism is designed to further strengthen the search efficiency and boost the convergence rate. Experimental results demonstrate that our proposed method is more competitive than other state-of-the-art evolutionary algorithms for UCAV path planning problem considering the quality and stability of the final paths.

Effects of Coanda jet direction on the aerodynamics and flow physics of the swept circulation control wing

Over the last decade, many flight tests of the conventional/canard/flying-wing layout UAVs (unmanned aerial vehicle) have proven that the trailing-edge circulation control (CC) is an effective method to generate a significant rolling moment for the rolling control. However, the Coanda jet’s three-dimensional effects, especially the Coanda jet direction, on the design of UCAVs (unmanned combat air vehicle) have not been revealed and discussed clearly yet. In order to solve this problem, the effects of the Coanda jet direction on the swept wing’s aerodynamics and flow physics are investigated in detail in this paper. An in-house CFD(computational fluid dynamics) solver HyperFLOW with the Spalart–Allmaras for Rotation and Curvature turbulence model and the velocity inlet boundary has been validated by 2-D and 3-D CC cases and is used in this work. The research reveals that compared with the Coanda jet in the freestream direction (FJ jet), the Coanda jet perpendicular to the trailing edge (PJ jet) has a more significant impact on the aerodynamic loads and flow structures for swept wings, including increasing lift, drag, and nose-down moment, enhancing the wingtip vortex and weakening the thickness vortex. In contrast, the FJ jet performs better in improving the lift-to-drag ratio of swept wings. On a typical UCAV, the generalizability of the conclusion about the Coanda jet direction effects on the aerodynamic characteristics is verified. The current research gets an insight into the three-dimensional effects of the trailing-edge CC and provides solid support for implementing the CC technology into practical aircraft concepts.

Security in Internet of Drones: A Comprehensive Review

Unmanned Aerial Vehicles (UAVs), also called drones, are used for various applications with two basic classifications: civilian and military drones. Civilian drones are used for various applications like construction site monitoring, natural disaster area monitoring, agriculture, etc. Military drones are used for applications like monitoring a country’s border, the transmission of information about the intruders to Ground Control Station (GCS), or to any other server which has been designed for the purpose. Before the real-time deployment of the Internet of Drones (IoD), the feasibility and efficiency of a proposed UAV framework should be tested in an open-source UAV simulator or any network simulator. AirSim simulator is an open-source UAV simulator that has different features like ease of development, efficient motion capture, efficient obstacle detections and collision detections, use of different sensor models, and physics models. Hence, a review has been done on recent UAV frameworks which have been designed and tested using the AirSim simulator. Since a vast amount of data is being transmitted between IoD devices in this era, there is a need for designing secure IoD communication frameworks, with the least compromise in the performance of the designed frameworks. Hence, an extensive review has been done on different secure IoD communication frameworks which have used different cryptography concepts like key agreement, authentication, encryption and decryption, integrity, blockchain, digital signatures, and have implemented their proposed frameworks in real-time, or by using network simulators. The common network simulators that have been used for simulating secure IoD frameworks/mechanisms are NS3 and OMNeT++.

An immune plasma algorithm with a modified treatment schema for UCAV path planning

The success of a task carried out by an unmanned combat aerial vehicle for short UCAV is directly related with the path in the battlefield or operation area. The path of the UCAV should ensure that the probability of being shot down by the enemy weapon systems and the consumption of fuel are optimized. Immune Plasma algorithm (IP algorithm or IPA) is one of the most recent optimization techniques. In this study, the path planning of UCAV was made based on a modified IP algorithm called as centrifuge IPA (centIPA). The performance of the centIPA was first investigated in detail by assigning different values to the control parameters and using various battlefield scenarios. The results obtained by the centIPA were also compared with the results of other metaheuristics and their improved variants under the same conditions. The comparative studies between centIPA and other metaheuristics showed that newly introduced plasma extraction technique significantly contributes to the solving capabilities of IPA and centIPA outperforms its competitors for the vast majority of the test scenarios.

Research on UCAV Maneuvering Decision Method Based on Heuristic Reinforcement Learning.

With the rapid development of unmanned combat aerial vehicle (UCAV)-related technologies, UCAVs are playing an increasingly important role in military operations. It has become an inevitable trend in the development of future air combat battlefields that UCAVs complete air combat tasks independently to acquire air superiority. In this paper, the UCAV maneuver decision problem in continuous action space is studied based on the deep reinforcement learning strategy optimization method. The UCAV platform model of continuous action space was established. Focusing on the problem of insufficient exploration ability of Ornstein–Uhlenbeck (OU) exploration strategy in the deep deterministic policy gradient (DDPG) algorithm, a heuristic DDPG algorithm was proposed by introducing heuristic exploration strategy, and then a UCAV air combat maneuver decision method based on a heuristic DDPG algorithm is proposed. The superior performance of the algorithm is verified by comparison with different algorithms in the test environment, and the effectiveness of the decision method is verified by simulation of air combat tasks with different difficulty and attack modes.

Aerial combat maneuvering policy learning based on confrontation demonstrations and dynamic quality replay

Unmanned Combat Aerial Vehicles (UCAVs) is becoming a crucial platform to perform dangerous missions with the ability of autonomy and intelligence in making decisions. Recently, many researches have focused on UCAV air-to-air combat mission and attempted to solve maneuvering policy using deep reinforcement learning (DRL). Yet, previous studies on the sequential decision problem often have limitations due to the complexity of combat environment, overdependence on expert knowledge, and low learning efficiency from large-scale exploration space. In view of the lacks, the paper aims to automatically formulate counter maneuvers with the application of DRL and proposes a novel dynamic quality replay (DQR) method which is capable of guiding agent learn tactical policy from historical data efficiently, and can get rid of the dependence on traditional expert knowledge. Firstly, in order to accelerate model convergence, confrontation demonstrations generated with traditional algorithms are used for agent basic training before interacting with the environment. Moreover, the formal training is divided into four stages, in which DQR introduces a common framework to filter new interactive data for memory replay, including the process of classification, evaluation and sampling. The episodes are classified into different class based on opponent models and confrontation result. DQR can evaluate sample quality in each class and sample training batch with episode quality dynamically The new method is suitable for continuous action or discrete action space, and can makes pay-offs between specific demonstrations and exploration. Finally, we designed different initial engagement scenarios to train the agent models, whose basic algorithms are DQN, VPG, DDPG and SAC. In the test experiment, the winning rate of models can reach 0.6 or more, and the best model SAC can reach 0.8 for each stage, which means the feasibility and efficiency of the methods.