Rapid advancement in Unmanned Aerial Vehicles (UAVs) and their increasing utility in different areas of human life raises a need for collision-free flying in airspace during operation. Especially when thousands of UAVs operates in specific airspace of few kilometres then it is difficult to manage collision free operations of these UAVs. However, each UAV can choose from many possible trajectories, speeds, or altitudes to avoid conflicts. As the number of UAVs increases, it becomes computationally infeasible to find an optimal and collision-free solution by examining all possible configurations. As a result, this is classified as NP-complete problem, meaning that there is no known conventional algorithm can provide a solution in feasible time. It means that the classical algorithms require exponential time to solve it for large problem instances. On the other hand quantum computing could provide potential speed up for solving such problem. Here, we present a quantum method for reduce the time complexity of solving the problem. Our contribution in this paper are to design a quantum algorithm for unmanned aerial vehicles conflict resolution (UAVs-CR) problem, to design an explicit oracle to solve this problem as well as encoding the problem information into basis states in order to use quantum search algorithm, known for its quadratic speed-up in unstructured database search. A series of experiments were carried out using the IBM Quantum computer and the AerSimulator running locally to examine the scalability, correctness, and runtime efficiency of the proposed quantum methodology.
Rapid advancement in Unmanned Aerial Vehicles (UAVs) and their increasing utility in different areas of human life raises a need for collision-free flying in airspace during operation. Especially when thousands of UAVs operates in specific airspace of few kilometres then it is difficult to manage collision free operations of these UAVs. However, each UAV can choose from many possible trajectories, speeds, or altitudes to avoid conflicts. As the number of UAVs increases, it becomes computationally infeasible to find an optimal and collision-free solution by examining all possible configurations. As a result, this is classified as NP-complete problem, meaning that there is no known conventional algorithm can provide a solution in feasible time. It means that the classical algorithms require exponential time to solve it for large problem instances. On the other hand quantum computing could provide potential speed up for solving such problem. Here, we present a quantum method for reduce the time complexity of solving the problem. Our contribution in this paper are to design a quantum algorithm for unmanned aerial vehicles conflict resolution (UAVs-CR) problem, to design an explicit oracle to solve this problem as well as encoding the problem information into basis states in order to use quantum search algorithm, known for its quadratic speed-up in unstructured database search. A series of experiments were carried out using the IBM Quantum computer and the AerSimulator running locally to examine the scalability, correctness, and runtime efficiency of the proposed quantum methodology.
Mission planning for multiple unmanned aerial vehicles (UAVs) is a complex problem that is expected to be solved by quantum computing. With the increasing application of UAVs, the demand for efficient conflict management strategies to ensure airspace safety continues to increase. In the era of noisy intermediate-scale quantum (NISQ) devices, variational quantum algorithms (VQA) for optimizing parameterized quantum circuits with the help of classical optimizers are currently one of the most promising strategies to gain quantum advantage. In this paper, we propose a mathematical model for the UAV collision avoidance problem that maps the collision avoidance problem to a quadratic unconstrained binary optimization (QUBO) problem. The problem is formulated as an Ising Hamiltonian, then the ground state is solved using two kinds of VQAs: the variational quantum eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA). We select conditional value-at-risk (CVaR) to further promote the performance of our model. Four examples are given to validate that with our method the probability of obtaining a feasible solution can exceed 90% based on appropriate parameters, and our method can enhance the efficiency of a UAVs' collision avoidance model.
Quantum computing is expected to mature in the coming years; however, traditional secure schemes, which might be used for computations and communications on unmanned aerial vehicles (UAVs), will need better solutions than simply updating passwords while disregarding specific systems like navigation or collision avoidance. In this paper, we propose an original method for improving the security of UAVs by embedding quantum-resistant cryptographic algorithms. Secure communication using lattice-based cryptography (Kyber) and message authentication using hash-based signatures (SPHINCS+) were selected based on the UAV hardware hierarchy for which these post-quantum systems are optimized. The developed system was tested in different flight conditions, including high-density UAV swarms, with the help of a simulation environment. The results show that secure communication between quantum-safe navigation and collision avoidance modules is possible with low latency, which does not cause a significant increase in computational cost or battery life consumption.
In recent years, with the development of Unmanned Aerial Vehicle (UAV) technology and its widespread application in military, logistics, agriculture, and other fields, the problem of UAV swarm trajectory planning has received extensive attention. Traditional optimization algorithms such as simulated annealing, genetic algorithms, and particle swarm optimization can achieve good results in some cases. However, when dealing with larger and more complex UAV swarm tasks, they often face issues such as low computational efficiency and getting stuck in local optima. Quantum annealing, which has the unique advantage of quantum tunneling, can effectively avoid local optima. Therefore, this study proposes a UAV swarm trajectory planning algorithm based on quantum annealing. The trajectory planning problem is converted into a Quadratic Unconstrained Binary Optimization (QUBO) problem. Using a two-stage processing strategy, the quantum annealing method clusters the task points and simulates the trajectory for each category, effectively reducing time complexity. Results show that quantum annealing has a higher probability of finding better paths than simulated annealing, demonstrating a better ability to escape the local optima problem. Additionally, the study considers four common scenarios that UAV swarms encounter during missions, designs corresponding dynamic task allocation schemes and modifies the objective function and constraints of quantum annealing. Results indicate that the UAV swarm trajectory planning algorithm can handle common scenarios effectively, ensuring that the UAV swarm can flexibly respond and efficiently complete tasks collaboratively.
The utilization of unmanned aerial vehicles (UAVs) in defense for functions, such as delivery and surveillance, has surged due to the fast progress of technology. UAV tracking and navigation system optimization is critical as UAV operations become more complicated. Quantum computing (QC) can significantly improve optimization procedures for real-time tracking, overcoming computational challenges and enhancing efficiency in UAV operations by handling large amounts of data. The study’s objective is to establish how QC performs in the optimization of UAV tracking. A novel Dynamic Kookaburra-tuned Malleable Convolutional Neural Network (DK-MCNN) is proposed to improve the tracking performance of UAVs. The data collected from video feeds from cameras mounted on UAVs, readings from GPS and other sensors outlining the specific paths and maneuvers the UAV will perform, ensuring diverse scenarios, including various terrains and environments. The data preprocessed using data cleaning and normalization involve removing erroneous or redundant data to ensure high-quality datasets. The study specifically focuses on leveraging QC into UAV tracking systems and promises to enhance performance, including speed, optimization, data handling and robustness. This technology has the potential to transform UAV operations, making them more efficient, reliable and capable of handling complex tasks in dynamic environments. The study evaluates the tracking accuracy, computational efficiency and resilience of the proposed method compared to traditional approaches across a range of operating situations. Extensive simulations of several tracking scenarios, encompassing urban, rural and disaster response situations, are employed to assess the efficacy of UAVs in monitoring and navigating across dynamic terrain. The outcomes illustrate the potential for real-time applications in UAV systems, by showing that the suggested technique dramatically decreases processing time and improves tracking accuracy, when compared to conventional algorithms.
Unmanned aerial vehicles (UAVs) have accordingly been successfully integrated into different industries, including military, healthcare, and education, because of their diverse applications and sophisticated capabilities. Nevertheless, UAVs are increasingly confronted with significant security and operational issues related to communications and collision avoidance. This chapter will discuss in detail the convergence of quantum cryptography with UAVs for enhanced security against these problems. Starting with a critical examination of the literature on the subject at hand, the paper traces the historical evolution of cryptography and discusses the present trends in UAV technology. The existing security standards are discussed, including their shortcomings, paving the way for the need for stricter ones. The principles of quantum cryptography, including quantum key distribution (QKD), are briefly summarized below in relation to classical cryptography. In the case of UAV communications, challenges include uniqueness threats, the real-time context, and the recently emerging problem of collision, all of which should be addressed. The proposed layered architecture here will include quantum cryptography to guarantee data security while enhancing communication efficiency. This will provide a framework for future development by explaining in detail the design, implementation, and security mechanisms of the proposed solution. Its performance will be evaluated in various case studies of military, healthcare, and education environments. The performance evaluation parameters have been addressed in detail to emphasize the strengths of the system and areas for improvement. The research raises the newly arising collision problem with UAVs and offers quantum solutions to improve the collision avoidance mechanism. This chapter will describe in depth how quantum cryptography merges with UAVs for increased protection against these issues. Beginning with a critical study of the available literature on the topic at hand, the paper follows the historical development of cryptography and discusses the current trends in UAVs. The prevailing security standards are discussed, along with their limitations, leading to the necessity for stricter ones. The quantum cryptographic principles, such as QKD, are briefly summarized below as applied to classical cryptography. Uniqueness threats, the real-time context, and the recently emerging problem of collision are challenges that should be addressed in the case of UAV communications.
The increasing availability of quantum hardware has stimulated interest in applying quantum algorithms to combinatorial optimization problems. Collision avoidance for unmanned aerial vehicles (UAVs) is one such challenge due to the computational complexity of coordinating multiple agents in shared airspace. In this paper, we formulate the problem as a Quadratic Unconstrained Binary Optimization (QUBO) model that captures route selection and collision constraints, enabling the use of the Quantum Approximate Optimization Algorithm (QAOA). To improve efficiency, we introduce a constrained QAOA Ansatz employing an XY-mixer that preserves one-hot route assignment constraints and restricts the search space to valid solutions. We evaluate the approach through noiseless simulations and device-based noise models, analyzing its behavior under realistic conditions and highlighting the opportunities and limitations of near-term quantum hardware for multi-agent routing problems.