Dynamic Mission Research Articles

A Heterogeneous Multi-Satellite Dynamic Mission Planning Method Based on Metaheuristic Algorithms

The increasing demand for satellite communication necessitates efficient resource management, especially as next-generation high-throughput satellites (HTSs) face challenges in optimizing user connections. This paper presents a novel method that integrates a discretely improved Particle Swarm Optimization (PSO) algorithm with a dynamic task management framework to enhance satellite resource allocation efficiency. The PSO algorithm is adapted for discrete selection problems to maximize a fitness function based on user priority, user preference, and capacity satisfaction, thus ensuring accurate user–satellite matching. A main loop function iteratively updates user data and connection statuses, thus achieving continuous optimization of satellite connections. Through simulation studies, we validated the effectiveness of this method under dynamically changing user demands, demonstrating that the Discrete PSO algorithm significantly outperforms Simulated Annealing (SA) and the Genetic Algorithm (GA) in user satisfaction, maintaining levels above 0.996 even under high demand. Additionally, it effectively prioritizes high-demand users, ensuring their satisfaction remains above 0.95. Overall, our method enhances the management of daily communication tasks, significantly improving service quality and user satisfaction.

Advancements in Cooperative Mobile Robots Control Strategies for Large-Scale Material Transport

This paper explores groundbreaking advancements in control strategies for cooperative mobile robots used in large-scale material transport, a critical aspect of modern industrial, manufacturing, logistics, and construction sectors. It delves into the development of sophisticated systems that enable seamless coordination among multiple mobile robot systems. The research presents a novel hierarchical finite state automaton for dynamic mission adaptation and a null space-based control scheme for precise task execution and enhanced system resilience. The introduction of Mecanum wheels facilitates flexible movement and manipulation of materials, thereby increasing operational efficiency and safety. Cutting-edge sensory technology, including LiDAR, and the implementation of the Robot Operating System are highlighted for their roles in enhancing autonomous navigation and intelligent operation. Additionally, the paper discusses the impact of centralized and decentralized control methods in ensuring safe, cooperative object transport. The findings contribute to the vision of Industry 4.0 by promoting the integration of automation and robotic cooperation in complex environments and present a foundational blueprint for further research. Challenges for future work such as scalability, communication efficiency, collision avoidance, and energy efficiency are also considered, underscoring the need for ongoing development of robust and scalable robotic systems to address modern transport challenges.

A Hybrid Preference Interaction Mechanism for Multi-Satellite Imaging Dynamic Mission Planning

The existing multi-satellite dynamic mission planning system hardly satisfies the requirements of fast response time and high mission benefit in highly dynamic situations. In the meantime, a reasonable decision-maker preference mechanism is an additional challenge for multi-satellite imaging dynamic mission planning based on user preferences (MSDMPUP). Therefore, this study proposes the hybrid preference interaction mechanism and knowledge transfer strategy for the multi-objective evolutionary algorithm (HPIM–KTSMOEA). Firstly, an MSDMPUP model based on a task rolling window is constructed to achieve timely updating of the target task importance degree through the simultaneous application of periodic triggering and event triggering methods. Secondly, the hybrid preference interaction mechanism is constructed to plan according to the satellite controller’s preference-based commands in different phases of the optimal search of the mission planning scheme to effectively respond to the dynamic changes in the environment. Finally, a knowledge transfer strategy for the multi-objective evolutionary algorithm is proposed to accelerate population convergence in new environments based on knowledge transfer according to environmental variability. Simulation experiments verify the effectiveness and stability of the method in processing MSDMPUP. This study found that the HPIM–KTSMOEA algorithm has high task benefit, short response time, and high task completion when processing MSDMPUP.

Study on the Influence of an UGV Suspension System on Camera Motion of the Teleoperation System

In the following article, the results of a study on the impact of the suspension system used in Unmanned Ground Vehicles (UGVs) on the kinematic excitation of cameras in teleoperation systems are presented. As indicated by preliminary reconnaissance studies, these excitations significantly affect the operator’s ability to perceive the environment and recognize images while driving. Currently, there is a lack of publications and guidelines in the literature regarding the design of UGV suspensions and their evaluation in terms of improving operator perception in teleoperation systems. The studies were conducted in a simulated environment using multibody systems, where various suspension structure variants were developed. The tests were carried out on the ISO 5008 rough test track. The evaluation of the tested suspension structures was carried out using a proprietary method, enabling parametric analysis and the selection of favorable solutions for improving image recognition by the UGV operator. Future research can focus on adjustment of the UGV suspension characteristics which could have significant influence on situational awareness and the operator’s ability to act effectively, especially during dynamic missions.

Context-attentive robot reconfiguration for human-machine space missions

With humankind’s aspiration of extraterrestrial inhabitation, the need for robotic support is eminent. To master the emerging challenge of providing astronauts with assistive space robots possessing the required adaptability to new situations and tasks under the constraints of severely limited supply chains in extraterrestrial missions, the utilization of existing hardware to its fullest is required. We propose the combination of a concept with an architecture to jointly support context-aware collaboration between humans and robotic systems for implementing effective resource utilization in space missions. The underlying framework is an implementation of a flexible architecture that enables context awareness for robotic systems. It supports processing nodes for retrieval of context information from the raw sensor values as well as further inferences using the output of nodes. Context information spans across three entities and describes the current state of the environment, the astronaut, and the robot itself. This information then can be used to infer the human’s current intention as well as influence the behaviour to be executed next. Additionally, dynamic changes in the data processing chains are handled by the framework to facilitate an adequate adaptation of the system to situational events. We combine this architecture with the concept of space robots as a composition from building blocks, which are supported and made accessible by a software toolkit. The modular design approach enables online self-reconfiguration of robotic hardware and software components. In combination with dynamic mission planning, based on ontological descriptions of available resources and functionalities, robots are able to adapt their physical and computational appearance dynamically during a mission according to different tasks and goals. By incorporating these two developments in a joint deployment we envision raising the efficiency of robotic systems in human-machine interaction through the usage of self-reconfiguration as a reactive behavior in order to adapt to a specific task, recognized or derived from a human’s intention. Transfer of the proposed idea back to Earth may help to abate resource dissipation caused by deploying specialized monolithic systems with a narrow range of capabilities through utilizing the adaptivity of reconfigurable robots.

Unmasking overestimation: a re-evaluation of deep anomaly detection in spacecraft telemetry

As the volume of telemetry data generated by satellites and other complex systems continues to grow, there is a pressing need for more efficient and accurate anomaly detection methods. Current techniques often rely on human analysis and preset criteria, presenting several challenges including the necessity for expert interpretation and continual updates to match the dynamic mission environment. This paper critically examines the use of deep anomaly detection (DAD) methods in addressing these challenges, evaluating their efficacy on real-world spacecraft telemetry data. It exposes limitations in current DAD research, highlighting the tendency for performance results to be overestimated and suggesting that simpler methods can sometimes outperform more complex DAD algorithms. By comparing established metrics for anomaly detection with newly proposed ones, this paper aims to improve the evaluation of DAD algorithms. It underscores the importance of using less accuracy-inflating metrics and offers a comprehensive comparison of DAD methods on popular benchmark datasets and real-life satellite telemetry data. Among the DAD methods examined, the LSTM algorithm demonstrates considerable promise. However, the paper also reveals the potential limitations of this approach, particularly in complex systems that lack a single, clear predictive failure channel. The paper concludes with a series of recommendations for future research, including the adoption of best practices, the need for high-quality, pre-split datasets, and the investigation of other prediction error methods. Through these insights, this paper contributes to the improved understanding and application of DAD methods, ultimately enhancing the reliability and effectiveness of anomaly detection in real-world scenarios.

A dynamic mission abort policy for transportation systems with stochastic dependence by deep reinforcement learning

A dynamic mission abort policy for transportation systems with stochastic dependence by deep reinforcement learning

UAV SWARM SYSTEMS

The rapid development of technology has introduced new and groundbreaking approaches to unmanned aerial vehicles (UAVs). One such innovation is the swarm UAV systems. These systems represent a technology where multiple small and often autonomous aircraft collaborate to perform specific tasks. An important component of this system is artificial intelligence. Swarm UAV systems can operate more effectively and coordinatedly by utilising artificial intelligence algorithms. Each UAV is equipped with autonomous control systems and artificial intelligence software. Through data analysis, object recognition, route planning, environmental adaptation and other complex tasks, artificial intelligence aids these UAVs in successfully completing tasks. As a result, swarm UAVs are capable of performing more complex and dynamic missions. The integration of artificial intelligence into swarm UAV systems holds great potential for several application fields. For instance, in search and rescue operations, AI can perform image analysis to detect individuals trapped under debris. In agriculture applications, AI can detect plant diseases or pests and optimize irrigation requirements. In this article, we will examine what swarm UAV systems are, how they operate, and the contribution of integrating artificial intelligence to this technology. In this article, we will examine what swarm UAV systems are, how they operate, and the contribution of integrating artificial intelligence to this technology. In this article, we will examine what swarm UAV systems are, how they operate, and the contribution of integrating artificial intelligence to this technology. Additionally, we will focus on the potential future application areas of swarm UAV systems and the potential impacts of this integration. Swarm UAV systems and artificial intelligence represent a significant transformation in the world of technology, and this article will provide a resource to better understand these exciting developments. Keywords: Research of UAVs, integration of artificial intelligence, flight programs, autonomous control.

Multilevel preventive replacement for a system subject to internal deterioration, external shocks, and dynamic missions

Multilevel preventive replacement for a system subject to internal deterioration, external shocks, and dynamic missions

Resolving the tidal weather of the thermosphere using GDC

NASA’s Geospace Dynamics Constellation (GDC) mission is a six satellite constellation to make in situ measurements of important ionospheric and thermospheric variables to better understand the processes that govern Earth’s near space environment. Scheduled for a 2029 launch into high inclination orbits ∼82° at ∼380 km, the satellite orbit planes will separate over time to provide almost continuous local solar time coverage every day towards the end of the 3 year baseline GDC mission. As such, the neutral temperature and neutral wind measurements of GDC will likely allow the heliophysics community to make significant progress towards resolving the tidal weather of the thermosphere, that is, day-to-day tidal variability, and how it is driven by meteorological processes near the surface and in situ forcing in the ionosphere-thermosphere system. To assess the GDC ability to accurately resolve the tides each day and when in the mission this can be achieved, we conduct an Observational Simulation System Experiment (OSSE) using SD-WACCM-X and the predicted GDC orbits. Our results show that GDC can provide closure on the tidal variability (mean, diurnal and semidiurnal, migrating and nonmigrating) at orbit height in mission phase 4 and throughout most parts of mission phase 3. We also perform Hough Mode Extension fitting of relevant tidal components to study possible connections between the GDC observations and the tides at 200 km, to assess synergies between GDC and the forthcoming DYNAMIC mission (scheduled to be co-launched with GDC) that will measure altitude-resolved winds and temperatures in the ∼100–200 km height range.

A Survey on Digital Twins: Architecture, Enabling Technologies, Security and Privacy, and Future Prospects

By interacting, synchronizing, and cooperating with its physical counterpart in real time, digital twin is promised to promote an intelligent, predictive, and optimized modern city. Via interconnecting massive physical entities and their virtual twins with inter-twin and intra-twin communications, the Internet of digital twins (IoDT) enables free data exchange, dynamic mission cooperation, and efficient information aggregation for composite insights across vast physical/virtual entities. However, as IoDT incorporates various cutting-edge technologies to spawn the new ecology, severe known/unknown security flaws and privacy invasions of IoDT hinders its wide deployment. Besides, the intrinsic characteristics of IoDT such as decentralized structure, information-centric routing and semantic communications entail critical challenges for security service provisioning in IoDT. To this end, this paper presents an in-depth review of the IoDT with respect to system architecture, enabling technologies, and security/privacy issues. Specifically, we first explore a novel distributed IoDT architecture with cyber-physical interactions and discuss its key characteristics and communication modes. Afterward, we investigate the taxonomy of security and privacy threats in IoDT, discuss the key research challenges, and review the state-of-the-art defense approaches. Finally, we point out the new trends and open research directions related to IoDT.

Dynamic Mission Planning Algorithm for UAV Formation in Battlefield Environment

The limited time duration and spatial instability of TSTs increase the difficulty of mission planning for unmanned aerial vehicle (UAV) formations, which in turn affects the task execution abilities of UAVs. Aiming at this problem, a multi-UAV dynamic mission planning algorithm based on the time window mechanism is proposed under a distributed formation structure. The proposed algorithm comprehensively considers a variety of complex constraints involved in actual application scenarios and constructs a UAV formation mission planning model with minimum task execution cost and path planning cost as the optimization goal. In this work, particle swarm optimization algorithm is improved by introducing a specific particle coding method and a competitive co-evolutionary population updating strategy, to enhance the solution speed and accuracy of the mission planning model. In this way, the optimal task allocation strategy and task execution path are obtained to meet the mission requirements, and to achieve the multi-UAV cooperative reconnaissance, strike and evaluation of multiple TSTs. Furthermore, the effectiveness of the proposed algorithm is validated via simulations.

WI-FI COMMUNICATION SYSTEM FOR A FIXED-WING TWIN-ENGINE AIRPLANE UAV

Unmanned Aerial Vehicle (UAV) systems have automated pilots that can be configured to suit various mission requirements. In this study, we assess, develop, and evaluate a bidirectional Wi-Fi communication link for an automated piloting system, utilizing the Cube Orange architecture that is mounted on a fixed-wing twin-engine airplane UAV. This Wi-Fi link enables seamless data exchange between the UAV and the ground control station. Using this bidirectional communication link, the user gains access to telemetry data, providing real-time insights into the UAV's flight parameters. Additionally, the user can send commands to the UAV, ensuring dynamic mission control and adaptability, while, the Wi-Fi link facilitates the retrieval of live video data from the UAV's onboard camera, enabling display on the command station screen or recording for future analysis and utilization. This comprehensive Wi-Fi communication system enhances the overall functionality and efficiency of the UAV's automated piloting, enabling smooth and reliable operations for various mission scenarios.

Performance analysis of meta-morphing wing

To adapt to a dynamic mission environment, morphing wings change their outer shape dramatically. As the airfoil thickness grows, the leading-edge radius advances, making the leading-edge smoother and allowing the laminar air to resist full swing separation at greater angles of attack. In this paper, the gear rod mechanism with rack and pinion is installed on the wings. In addition to the gear rod mechanism, corrugated shapes given at the trailing edge for elastic deformation. Flaps are used primarily on the leading and trailing edges of the wing to increase the lift coefficient and camber of the wing. A morphing mechanism would achieve leading and trailing edge deflection as well as airfoil thickness deflection at the same time. The morphing wing model is designed in CATIA V5 software with NACA 2415 airfoil coordinates. Designed model is analyzed in ANSYS workbench with various angles of attack such as -100, 00, 50 and 100 with velocity 50 m/s as boundary conditions. Aerodynamic efficiency CL/Cd is calculated for morphing wing and compared with convention wing. Fuel consumption is one of the performance parameters that might be developed using the morphing wing concept. This morphing wing is a brand-new mechanism concept that focuses only on the mechanism rather than the materials.

A dynamic mission abort policy for the swarm executing missions and its solution method by tailored deep reinforcement learning

A dynamic mission abort policy for the swarm executing missions and its solution method by tailored deep reinforcement learning

Designing two-level rescue depot location and dynamic rescue policies for unmanned vehicles

Unmanned vehicles are often required to execute critical missions in harsh environment, causing system failure which may occur during the mission execution or rescue procedure. Existing research has focused primarily on policies during mission execution to reduce failure risk. The paper investigates risk evaluation and control policies both in the mission execution and rescue phases, and proposes two-level rescue depot location policies and dynamic rescue policies including mission abort and maintenance policies for unmanned vehicle systems. Specifically, considering multi-state characteristics of unmanned vehicle systems, two-level rescue depots are introduced to satisfy maintenance demands by providing different types of maintenance. To improve survivability in the mission execution, dynamic mission abort policies are executed when the number of failed components reaches predetermined thresholds, and then a rescue procedure is initiated. During the rescue, elaborate designed rescue depots location and dynamic maintenance policies are developed to reduce the risk of failure in the rescue. After a successful rescue, unmanned vehicles can re-attempt missions. The recursive algorithm and discretization algorithm are used to derive and evaluate mission success probability, system survivability, and expected cost of losses, and optimization models based on three indicators are formulated. The superiority of the proposed policies is demonstrated by a case study of a UAV system.

A Hierarchical Approach to Intelligent Mission Planning for Heterogeneous Fleets of Autonomous Underwater Vehicles

The rapid development of marine robotic technology in recent decades has resulted in significant improvements in the self-sufficiency of autonomous underwater vehicles (AUVs). However, simple scenario-based approaches are no longer sufficient when it comes to ensuring the efficient interaction of multiple autonomous vehicles in complex dynamic missions. The necessity to respond cooperatively to constant changes under severe operating constraints, such as energy or communication limitations, results in the challenge of developing intelligent adaptive approaches for planning and organizing group activities. The current study presents a novel hierarchical approach to the group control system designed for large heterogeneous fleets of AUVs. The high-level core of the approach is rendezvous-based mission planning and is aimed to effectively decompose the mission, ensure regular communication, and schedule AUVs recharging activities. The high-level planning problem is formulated as an original acyclic variation of the inverse shift scheduling problem, which is NP-hard. Since regular schedule adjustments are supposed to be made by the robots themselves right in the course of the mission, a meta-heuristic hybrid evolutionary algorithm is developed to construct feasible sub-optimal solutions in a short time. The high efficiency of the proposed approach is shown through a series of computational experiments.

Impacts of Aeolus horizontal Line‐Of‐Sight (HLOS) wind assimilation on the Korean integrated model (KIM) forecast system

AbstractThe Korean Integrated Model (KIM) forecast system, based on a hybrid four‐dimensional ensemble‐variational method, was extended to assimilate Horizontal Line‐Of‐Sight (HLOS) wind observations from the Atmospheric Laser Doppler Instrument (ALADIN) on board the Atmospheric Dynamic Mission Aeolus satellite. In a global cycling experiment, assimilation of Aeolus HLOS wind observations led to reductions in the average root‐mean‐square error of 0.8 and 0.5% for the zonal and meridional wind analyses when compared against European Center for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) analyses. Even though the observed variable is wind, there was also an overall beneficial impact on analyses of the mass variables. In the Southern Hemisphere (SH), the reduced analysis errors led to forecast skill improvements out to 72 h. In contrast, in the Northern Hemisphere (NH) there was relatively little reduction of analysis errors, but wind forecasts were nevertheless improved, and these positive impacts lasted longer – out to 120 h rather than 72 h. Experiments suggest that the relatively poor long‐range performance in the SH high latitudes was due to problems with the mass increments derived from Aeolus wind increments via the ensemble‐based part of the hybrid background error covariance (B), which eventually led to adverse effects on the wind variables as forecasts progressed in the SH. This study shows that it is necessary to estimate the ensemble B in the Antarctic region with its high elevation more accurately in order to effectively use the Aeolus observation information.

Remaining useful life prediction of manufacturing system based on fuzzy Quality State Task Network

Accurate prediction of operational state and remaining useful life (RUL) is the premise for PHM (Prognostic and Health Management) implementation and predictive maintenance decision of intelligent manufacturing system. The quality state of the produced WIP (work-in-process) is also a core indicator of the RUL of the running manufacturing system, which can have an impact on the degradation of the downstream machine. However, few studies have fused the quality data of WIP(work-in-process) into the RUL prediction considering the fuzzy operational mechanism of the manufacturing system. Therefore, an RUL prediction method of intelligent manufacturing systems based on the fuzzy Quality State Task Network (QSTN) model is proposed. First, the principles of the RUL prediction of manufacturing system is expounded by taking fuzzy mission reliability as the indicator of system health state. Second, a fuzzy QSTN model that can simultaneously characterize the performance degradation of manufacturing equipment and the quality degradation of WIP is established. Third, a dynamic mission reliability and remaining useful life prediction method of the manufacturing system based on dynamic Bayesian networks is proposed. Finally, the effectiveness of the method is verified by taking the continuous stamping process of a bogie manufacturing system as an example.

Expediting recovery of autonomous underwater vehicles in dynamic mission environments: A system‐of‐systems challenge for underwater warfare

AbstractAs autonomous underwater vehicle (AUV) adoption increases, operators demand advanced behaviors from commercial off‐the‐shelf systems. However, new behaviors can often only be deployed operationally once assured. This paper overviews research into expediting recovery to an operator's vessel through a custom homing behavior, demonstrating technology advancement in conjunction with test and evaluation activities. Advanced docking infrastructure and frameworks are still under development, yet current AUV operations require rapid and reliable recovery when mission factors change. Homing is achieved with a directional acoustic transponder providing range and bearing data to the AUV from the operator's vessel. A converted measurement Kalman filter processes range and bearing data that generates dynamic waypoints for the AUV through MOOS‐IvP as a backseat driver; a universal approach and filtering that is unique from prior AUV research. Results from simulations and field trials were analysed through a modular and experimental Test & Evaluation framework that was adopted specifically to help verify and validate the new AUV behavior, including systematic variations in recovery boat manoeuvres. The process includes documented use for the first time in AUV research of combinatorial screening (high throughput testing) and an average standardized residual metric to focus development early, encourage constructive iteration and build operational and engineering trust. Consistent homing was demonstrated with localization within 0.3 m and homing within 1.8 m of a moving digital acoustic transponder.