Mission Completion Research Articles

Multi‐target cognitive electronic reconnaissance for unmanned aerial vehicles based on scene reconstruction

AbstractModel‐free deep reinforcement learning (DRL) is regarded as an effective approach for multi‐target cognitive electronic reconnaissance (MCER) missions. However, DRL networks with poor generalisation can significantly reduce mission completion rates when parameters such as reconnaissance area size, target number, and platform speed vary slightly. To address this issue, this paper introduces a novel scene reconstruction method for MCER missions and a mission group adaptive transfer deep reinforcement learning (MTDRL) algorithm. The algorithm enables quick adaptation of reconnaissance strategies for varied mission scenes by transferring strategy templates and compressing multi‐target perception states. To validate the method, the authors developed a transfer learning model for unmanned aerial vehicle (UAV) MCER. Three sets of experiments are conducted by varying the reconnaissance area size, the target number, and the platform speed. The results show that the MTDRL algorithm outperforms two commonly used DRL algorithms, with an 18% increase in mission completion rate and a 5.49 h reduction in training time. Furthermore, the mission completion rate of the MTDRL algorithm is much higher than that of a typical non‐DRL algorithm. The UAV demonstrates stable hovering and repeat reconnaissance behaviours at the radar detection boundary, ensuring flight safety during missions.

Adaptive communication power control for enhancing attack resilience in UAV networks

A swarm of Unmanned Aerial Vehicles (UAVs) comprises multiple UAVs that are capable of completing tasks beyond the capabilities of a single UAV. Due to the unique challenges of UAV missions, these vehicles often operate far from base stations, making network connectivity crucial for the successful completion of UAV swarm missions. However, existing methods do not account for strategies to maintain the overall connectivity of the UAV network when nodes are under attack. To address this issue, we propose a method named Adaptive Communication Power Control (ACPC) that dynamically adjusts UAV communication power to mitigate potential connectivity losses caused by node failures or malicious attacks. This adjustment ensures that the network can maintain information exchange among the remaining UAVs even in the event of disruptions. Additionally, we introduce a novel evaluation method to assess the overall connectivity of the network and validate ACPC through simulations of UAV swarm missions where some UAVs experience failures. Using this evaluation method, we measured the network’s state before and after attacks and recovery and calculated the additional energy consumption required by the UAVs. The results indicate that our method can increase the resilience of the UAV network by up to 5.36 times, while only raising the total communication energy consumption to 1.53 times.

Optimizing Effectiveness and Defense of Drone Surveillance Missions via Honey Drones

This work aims to develop a surveillance mission system using unmanned aerial vehicles (UAVs) or drones when Denial-of-Service (DoS) attacks are present to disrupt normal operations for mission systems. In particular, we introduce the concept of cyber deception using honey drones (HDs) to protect the mission system from DoS attacks. HDs exhibit fake vulnerabilities and employ stronger signal strengths to lure DoS attacks, unlike the legitimate drones called mission drones (MDs) deployed for mission execution. This research formulates an optimization problem to identify an optimal set of signal strengths of HDs and MDs to best prevent the system from DoS attacks while maximizing mission performance under the resource constraints of UAVs. To solve this optimization problem, we leverage deep reinforcement learning (DRL) to achieve these multiple objectives of the mission system concerning system security and performance. Particularly, for efficient and effective parallel processing in DRL, we utilize a DRL algorithm called the Asynchronous Advantage Actor-Critic (A3C) algorithm to model attack-defense interactions. We employ a physical engine-based simulation testbed to consider realistic scenarios and demonstrate valid findings from the realistic testbed. The extensive experiments proved that our HD-based approach could achieve up to a 32% increase in mission completion, a 20% reduction in energy consumption, and a 62% decrease in attack success rates compared to existing defense strategies.

An integrated optimization strategy for mission planning and control resource allocation for long-endurance unmanned aerial vehicle

PurposeWith burgeoning interest in the low-altitude economy, applications of long-endurance unmanned aerial vehicles (LE-UAVs) have increased in remote logistics distribution. Given LE-UAVs’ advantages of wide coverage, strong versatility and low cost, in addition to logistics distribution, they are widely used in military reconnaissance, communication relay, disaster monitoring and other activities. With limited autonomous intelligence, LE-UAVs require regular periodic and non-periodic control from ground control resources (GCRs) during flights and mission execution. However, the lack of GCRs significantly restricts the applications of LE-UAVs in parallel.Design/methodology/approachWe consider the constraints of GCRs, investigating an integrated optimization problem of multi-LE-UAV mission planning and GCR allocation (Multi-U&G IOP). The problem integrates GCR allocation into traditional multi-UAV cooperative mission planning. The coupling decision of mission planning and GCR allocation enlarges the decision space and adds complexities to the problem’s structure. Through characterizing the problem, this study establishes a mixed integer linear programming (MILP) model for the integrated optimization problem. To solve the problem, we develop a three-stage iterative optimization algorithm combining a hybrid genetic algorithm with local search-variable neighborhood decent, heuristic conflict elimination and post-optimization of GCR allocation.FindingsNumerical experimental results show that our developed algorithm can solve the problem efficiently and exceeds the solution performance of the solver CPLEX. For small-scale instances, our algorithm can obtain optimal solutions in less time than CPLEX. For large-scale instances, our algorithm produces better results in one hour than CPLEX does. Implementing our approach allows efficient coordination of multiple UAVs, enabling faster mission completion with a minimal number of GCRs.Originality/valueDrawing on the interplay between LE-UAVs and GCRs and considering the practical applications of LE-UAVs, we propose the Multi-U&G IOP problem. We formulate this problem as a MILP model aiming to minimize the maximum task completion time (makespan). Furthermore, we present a relaxation model for this problem. To efficiently address the MILP model, we develop a three-stage iterative optimization algorithm. Subsequently, we verify the efficacy of our algorithm through extensive experimentation across various scenarios.

A Balanced Mission Planning for Multiple Unmanned Underwater Vehicles in Complex Marine Environments

The collaboration of a multiple unmanned underwater vehicles (multi-UUVs) system has attracted widespread attention in recent years, as it can overcome the limitations of a single UUV and enhance mission completion efficiency. Oriented towards patrol and exploration missions with multiple waypoints, this paper proposes a balanced mission planning strategy, aiming to improve mission quality while reducing mission time for multi-UUVs. Firstly, due to the uneven performance of the two optimization objectives, a quick initialization screening method is employed specifically for mission quality to reduce the mission space. Secondly, to ensure mission load distribution and collaboration among multi-UUVs, and ease the difficulty in solving the issues of mission allocation and route planning, a balanced bi-level mission planning method based on regional segmentation is proposed. Finally, applicable weight evaluation criteria are utilized to evaluate the feasible solution set and determine the optimal solution. The efficacy of the balanced mission planning strategy is substantiated through comprehensive numerical simulations in a complex 2D marine environment.

Performance Analysis of Reconnaissance Coverage for HUAV Swarms under Communication Interference Based on Different Architectures

In environments with unknown communication interference, the mission efficiency of heterogeneous unmanned aerial vehicle (HUAV) swarms is often impacted by communication disruptions due to regions of strong interference encountered when executing reconnaissance and coverage missions. Existing research has rarely focused on communication interference or on the differences in HUAV characteristics under various control architectures; thus, in this paper we explore the performance differences between HUAV swarms based on centralized, distributed, and centralized-distributed architectures when executing reconnaissance and coverage missions in environments with unknown communication interference. First, a communication model in an unknown interference environment is constructed to reflect the real-time communication status of the swarm. Second, in response to the limitations of the traditional artificial potential field (APF) algorithm in this environment, a coverage-oriented artificial potential field (COAPF) algorithm is proposed. Finally, based on the COAPF algorithm, a multi-dimensional comparison of the mission completion efficiency of HUAV swarms with three different architectures is conducted. Our simulation results indicate that the distributed architecture is suitable for large-scale environments with strong interference, while the centralized–distributed architecture performs better in small-scale environments with weak interference. Conversely, the centralized architecture exhibits poor performance in all interference scenarios due to its lack of decision-making capabilities.

Optimal Selection of Active Jet Parameters for a Ducted Tail Wing Aimed at Improving Aerodynamic Performance

The foldable tail of the box-type launch vehicle poses a risk of mechanical jamming during the launch process, which is not conducive to the smooth completion of the flight mission. The integrated nonfolding ducted tail proposed in this article can solve the problem of storing the tail in the launch box. Moreover, traditional mechanical control surfaces have been eliminated, and active jet control has been adopted to control the pitch direction of the flight attitude, which can improve the structural reliability of the tail wing. By studying the effects of parameters such as momentum coefficient, jet hole position, jet hole height, and jet angle on improving the aerodynamic performance of ducted tail wing, relatively good jet parameters are selected. Research has found that compared with jet hole height and jet angle, momentum coefficient and jet hole position are more effective in improving the aerodynamic performance of ducted tail wings. Under a trailing edge jet, a relatively good jet condition occurs when the jet hole height is equal to0.25% of the aerodynamic chord length, and the jet angle is equal to 0°. At this time, with the increase of the jet momentum coefficient, the effect of increasing the lift of the ducted tail wing is the best. Finally, a comparative analysis is conducted on the lift and drag characteristics between the ducted tail wing and traditional tail wing, and it is found that the ducted tail wing can generate lift at a 0° attack angle and will not stall in the high attack angle range of 12°~22°, with broad application prospects.

Transparency improves the accuracy of automation use, but automation confidence information does not.

Increased automation transparency can improve the accuracy of automation use but can lead to increased bias towards agreeing with advice. Information about the automation's confidence in its advice may also increase the predictability of automation errors. We examined the effects of providing automation transparency, automation confidence information, and their potential interacting effect on the accuracy of automation use and other outcomes. An uninhabited vehicle (UV) management task was completed where participants selected the optimal UV to complete missions. Low or high automation transparency was provided, and participants agreed/disagreed with automated advice on each mission. We manipulated between participants whether automated advice was accompanied by confidence information. This information indicated on each trial whether automation was "somewhat" or "highly" confident in its advice. Higher transparency improved the accuracy of automation use, led to faster decisions, lower perceived workload, and increased trust and perceived usability. Providing participant automation confidence information, as compared with not, did not have an overall impact on any outcome variable and did not interact with transparency. Despite no benefit, participants who were provided confidence information did use it. For trials where lower compared to higher confidence information was presented, hit rates decreased, correct rejection rates increased, decision times slowed, and perceived workload increased, all suggestive of decreased reliance on automated advice. Such trial-by-trial shifts in automation use bias and other outcomes were not moderated by transparency. These findings can potentially inform the design of automated decision-support systems that are more understandable by humans in order to optimise human-automation interaction.

Conceptual Aspect of Measuring the Efficiency of Cargo Unmanned Aerial System

This article examines the conceptual framework for measuring the efficiency of unmanned systems, focusing on a task-oriented and problem-solving approach to evaluating the performance of cargo unmanned aerial vehicles. The efficiency of cargo unmanned aerial vehicles pertains to their ability to complete missions on time and cost-effectively, while maximizing utilization, minimizing resource loss, and maintaining an acceptable level of flight safety. It is important to highlight that the efficiency of cargo unmanned aerial vehicles is multifaceted, encompassing technical, operational, economic, environmental, and regulatory dimensions. Progress in each of these areas contributes to the successful deployment and optimization of cargo drones across various applications, from last-mile deliveries to supply chain operations in remote regions. The article presents two methodologies for assessing the efficiency of cargo drones.

A Bilevel Programming Approach for Optimizing Multi-Satellite Collaborative Mission Planning.

With the burgeoning of remote sensing and space technology, multi-satellite collaborative mission planning, which is the key to achieving efficient Earth observation, has become increasingly intricate due to the expanding complexity and volume of observation missions. Addressing the multi-satellite collaborative mission planning problem, which is characterized by its two-stage decision-making process involving mission assignment and resource scheduling, this study investigates a comprehensive joint decision making that encompasses both mission assignment and resource scheduling and comprehensively optimizes the mission completion rate, the mission profit rate, and the satellite resource utilization rate. Considering the interaction of these decisions, we formulate the problem as a bilevel programming model from a game-theoretic perspective and propose a nested bilevel improved genetic algorithm (NBIGA) for its solution. Simulation experiments substantiate the applicability of the bilevel programming model in joint decision making for the stages of mission assignment and resource scheduling in multi-satellite collaborative mission planning, as well as the robustness of the NBIGA. A comparative analysis with the nested bilevel genetic algorithm (NBGA) confirms that the algorithm proposed in this study can achieve superior optimization outcomes and higher solving efficiency.

Recovery control of autonomous underwater vehicles based on modified delay-product-type functional

This paper focuses on the recovery process of autonomous underwater vehicles, emphasizing a hierarchical framework in which autonomous underwater vehicles are categorized into a mothership and sub-vessels. In the recovery phase, following the completion of an underwater mission, sub-vessels navigate towards a location designated by the mothership. The crux of the recovery hinges on the design of the controller for adapting communication delays induced by environmental in underwater communication transmissions. The mothership and sub-vessels constitute a collaborative multi-autonomous underwater vehicles network equipped with these controllers, making them operate through the synchronized adjustment of their states represented in error terms. A delay-dependent controller condition criterion is proposed based on the modified delay-product-type Lyapunov-Krasovskii functional. The controller with the gain obtained from the criterion manages the system effectively and ensures successful recovery. The effectiveness of the proposed approach is demonstrated through a case study involving a network comprising one leading and four following autonomous underwater vehicles.

Insight into wave propagation in polyimide films and resistive grid sandwich structures towards a hybrid monitoring of hypervelocity impact

Micro-Meteoroid and Orbital Debris pose a significant threat to the safe operation of orbiting spacecraft, potentially leading to mission failure in space exploration. Quantitative characterization of hypervelocity impact (HVI) is crucial to ensure the safety and successful completion of on-orbit missions. Firstly, this study designed a three-layer sandwich structure of polyimide film with orthogonally laid resistive wires, combined with piezoelectric and resistive wire sensors, for the simultaneous acquisition of acoustic emission (AE) signals generated by HVI and measurement of perforation dimensions. Secondly, a semi-analytical finite element (SAFE) analysis of wave dispersion properties in the periodic sandwich structure is conducted with Bloch’s theorem, together with a hybrid model based on three-dimensional smoothed particle hydrodynamics and finite element methods (SPH-FEM) to comprehensively understand the AE waves and damage characteristics induced by HVI. The resulting anisotropic wave propagation characteristics with SAFE and SPH-FEM are closely matched. Thirdly, a time delay-multiplication (TDM) imaging algorithm considering wave velocity anisotropy is proposed for accurate real-time “visualization” of HVI locations. Lastly, correlations are established between projectile and perforation dimensions. The proposed algorithm for HVI multi-parameter quantification and damage detection helps evaluate the space HVI environment and HVI-induced damage to spacecraft.

Моделі самоорганізації колективу однорідних безпілотних літальних апаратів при рішенні слабоформалізованих завдань

The development and modernization of unmanned aerial vehicles (UAVs), their control systems, safety and regulation, expansion of functionality, miniaturization and increase of sensor performance, improvement of energy capabilities and routing process, increase of autonomy, contributes to the improvement of the performance of tasks. In order to obtain the best performance, customize the process of UAV team self-organization, and reduce risks for operators, the article proposes to consider the proposed method of rules for self-organization of a team of homogeneous UAVs in solving poorly formalized tasks. The basis for writing a scientific paper is the tendency to constantly increase the relevance of UAVs due to their efficiency and feasibility, as well as the constant development of artificial intelligence and information technology. By thoroughly studying, researching and implementing these areas into UAV team management systems, a number of problems related to the construction of UAV teams using existing methods and models are solved. Such as: the vulnerability of the built UAV team, the inability to continue the task due to the destruction or loss of communication with the main UAV or operator (control point), economic inexpediency, reduction of the cognitive load on operators, and others. It should also be noted that in the realities of war, it is necessary to get ahead of the enemy’s scientific research and create an advantage over him in the field of robotic technologies. A model of rules for the self-organization of a team of homogeneous UAVs in solving poorly formalized tasks is proposed, in which a number of functions (route planning, role distribution, determination of optimal actions, obtaining and processing information) assigned to the onboard system of a robotic air complex can be performed by each element of the UAV team system through their self-organization. The practicality of this method lies in the fact that the artificial intelligence of the UAV will constantly self-learn and improve through the use of machine and deep learning. Thus, the results and time required to complete missions will improve significantly, and the number of control operators will decrease. A number of problems and shortcomings related to the organization of the control system, route planning, role assignment, speed and completeness of information receipt, processing and transmission are being addressed, which in turn improves the security and performance of the system.

Path planning for Multi-USV target coverage in complex environments

This research introduces a path planning methodology aimed at coordinating multiple unmanned surface vehicles (USVs) to accomplish target coverage task in obstacle-rich environments. Leveraging the locking sweeping method (LSM), our proposed method constructs task target point distance fields that integrates environmental constraints, alongside a task target point distance matrix. During the task allocation phase, we design a cost assessment function to assess the generated allocation solutions. And a greedy allocation strategy is employed to determine the optimal number of USVs required for mission completion, ensuring evenly distribution of target task points among them. Subsequently, we improve the ant colony optimization (ACO) method by redesigning the heuristic function and pheromone update rules, considering environmental constraints and task execution sequence constraints. This refinement facilitates the generation of optimized task execution sequences and safe navigation paths in obstacle environments. The effectiveness of the proposed method is validated through multiple sets of simulation experiments and compared with existing methods. The results demonstrate the practicality and efficacy of the method in addressing the challenges of USVs target coverage task in obstacle environments.

A transfer learning model for cognitive electronic reconnaissance of unmanned aerial vehicle: Experiments

Applying Deep Reinforcement Learning (DRL) technologies to Unmanned Aerial Vehicle (UAV) electronic reconnaissance is one of the current research hotspots. However, simulation and engineering practice show that due to poor generalization of DRL models, the performance of Cognitive Electronic Reconnaissance (CER) policies based on training will significantly decrease when the mission scene undergoes slight changes. To address this issue, we thoughtfully combine the mission area segmentation technique with transfer DRL and propose a difference-adaptive transfer DRL algorithm. This algorithm involves mission subarea segmentation, subarea pre-training, multi-subarea policy transfer, and multi-subarea splicing, providing an efficient solution to the convergence problem of the DRL algorithm caused by mission space expansion and reward sparsity. Additionally, a general CER transfer learning simulator is developed based on the analysis of the capabilities of the maneuvering platform and electronic reconnaissance payload. Multiple sets of CER policy transfer learning experiments are designed for different mission spaces, mission difficulties, and UAV characteristics. Compared with the algorithm baseline, our designed policy model significantly outperforms: the mission completion rate of UAVs in multi-scale mission spaces improves by up to 37.4%, reaching 97.5%, while the training time is reduced by 2.46 h. Further behavior analysis shows that this policy model enables UAVs to exhibit target tracking behaviors such as hovering and sustained approach.

Hybrid energy‐Efficient distributed aided frog leaping dynamic A* with reinforcement learning for enhanced trajectory planning in UAV swarms large‐scale networks

SummaryUAVs are emerging as a critical asset in the field of data collection from extensive wireless sensor networks (WSNs) on a large scale. UAVs can be used to deploy energy‐efficient nodes or recharge nodes, but it should not compromise the network's coverage and connectivity. This paper proposes a comprehensive approach to optimize UAV trajectories within large‐scale WSNs, utilizing Multi‐Objective Reinforcement Learning (MORL) to balance critical objectives such as coverage, connectivity, and energy efficiency. This research investigates the configuration of a Wireless Sensor Network (WSN) assisted by a pen_spark UAV. In this network, Cluster Heads (CHs) act as central points for collecting data from their assigned sensor nodes. A predefined path is established for the UAV to efficiently gather data from these CHs. The Hybrid Threshold‐sensitive Energy Efficient Network (Hy‐TEEN) encompasses sophisticated algorithms for CH selection, dynamic A* for 3D trajectory planning and leverages reinforcement learning for multi‐objective optimization. The experimental results and analysis demonstrate the effectiveness and efficiency of the proposed approach in improving UAV performance and energy efficiency. The results demonstrate that the proposed methodology's trajectories are capable of achieving a time savings of 3.52% in mission completion when contrasted with conventional baseline methods.

Failure risk management: adaptive performance control and mission abort decisions.

The failure behavior of safety-critical systems typically depends on the system performance level, which offers opportunities to control system failure risk through dynamic performance adjustment. Moreover, mission abort serves as an intuitive way to mitigate safety hazards during mission execution. Our study focuses on systems that execute successive missions with random durations. To balance mission completion probability and system failure risk, we examine two decision problems: when to abort missions and how to select the performance level prior to mission abort. Our objective is to maximize the expected revenue through dynamic performance control and mission abort (PCMA) decisions. We consider condition-based PCMA decisions and formulate the joint optimization problem into a Markov decision process. We establish the monotonicity and concavity of the value function. Based on this insight, we show that optimizing the mission abort policy requires a series of control limits. In addition, we provide conditions under which the performance control policies are monotone. For comparative purposes, we analytically evaluate the performances of some heuristic policies. Finally, we present a case study involving unmanned aerial vehicles executing power line inspections. The results indicate the superiority of our proposed risk control policies in enhancing operational performance for safety-critical systems. Dynamic performance adjustment and mission abort decisions provide opportunities to reduce the failure risk and increase operational rewards of safety-criticalsystems.

Design of Emergency Destruction System for Long-range Surface-to-Air Missile Flight Test

An Emergency Destruction System is inevitable for ensuring safety both at sea and in populated areas, particularly during emergency detonations triggered by abnormal missile flight or upon mission completion. This paper introduces a novel method for developing an Emergency Destruction System capable of precisely calculating the Instantaneous Impact Point(IIP) during high-speed, maneuverable long-range surface-to-air missile flight tests. The Emergency Destruction System designed for long-range surface-to-air missile flight tests generates impact position tables that meticulously incorporate wind errors and navigation equations based on the Earth’s ellipsoidal model. Factors such as the Coriolis effect and the direction of the gravitational acceleration vector are accounted for, significantly enhancing the accuracy of IIP determination amidst highly variable missile speed and attitude.

Expiration analysis of the International Space Station formulary for exploration mission planning

Effective medications will be required to maintain human health for long-duration space operations. Previous studies have explored the stability and potency of several of the medications used on the International Space Station (ISS). This study is a comprehensive analysis of the expected terrestrial shelf-lives of the entire 2023 ISS formulary using 4 international registries. Of the 106 medications in the ISS formulary, shelf-life data was found in at least 1 of the registries for 91 (86%) medications. Of these 91 medications, 54 have an estimated terrestrial shelf-life of ≤36 months when stored in their original packaging. 14 will expire in less than 24 months. The results of this study provide operational insight to supplying a pharmacy for an exploration mission, optimize therapeutic outcomes, and prevent diseases associated with extended spaceflight operations. Ultimately, those responsible for the health of spaceflight crews will have to find ways to extend the expiration of medications to the complete mission duration or accept the elevated risk associated with administration of an expired medication.

Implementation of a Gamification-Based Metaverse Exhibition: A Case Study of the Farewell Museum

The increasing adoption of metaverse exhibitions aims to overcome the limitations of traditional offline exhibitions by enhancing accessibility and creating imaginative spaces without temporal and spatial constraints. This study explores how incorporating gamification into metaverse exhibitions can leverage the strengths of online and offline experiences. By integrating metaverse elements and gamification, we designed an interactive metaverse space and evaluated its effectiveness through user feedback. Our findings indicate that guiding visitors to complete missions within the metaverse enhances their understanding of the offline exhibition narratives, thereby increasing their engagement with the exhibition themes. Furthermore, online experiences in the metaverse can stimulate interest in offline exhibitions, promoting visitor attendance. The metaverse space also encourages users to contribute personal stories to the exhibition content, fostering continuous content expansion. This approach strengthens the connection between online and offline exhibitions, increases visitor engagement, and promotes repeat visits, thereby addressing the limitations of traditional exhibitions. Additionally, metaverse exhibitions enhance the preservation and accessibility of cultural heritage through digital archiving, offering inclusive participation opportunities and promoting social sustainability. This study underscores the potential of metaverse exhibitions to evolve beyond technological innovation toward contributing to social sustainability.