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        <title>Frontiers in Aerospace Engineering | Intelligent Aerospace Systems section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/aerospace-engineering/sections/intelligent-aerospace-systems</link>
        <description>RSS Feed for Intelligent Aerospace Systems section in the Frontiers in Aerospace Engineering journal | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-05-14T12:53:43.557+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fpace.2026.1708392</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fpace.2026.1708392</link>
        <title><![CDATA[A digital twin-based fault simulation framework for aircraft electromechanical actuators: method and demonstration]]></title>
        <pubdate>2026-04-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chengwei Wang</author><author>Ip-Shing Fan</author><author>Stephen P. King</author>
        <description><![CDATA[Electromechanical actuators have become key components in next-generation aircraft architectures, particularly under More Electric Aircraft and Power-by-Wire paradigms. However, their operational complexity, compounded by mechanical-electrical interactions, introduces failure modes that are both difficult to detect and insufficiently represented in existing datasets. This paper presents a comprehensive digital twin-based framework developed to simulate and analyse EMA behaviour under both nominal and faulty conditions. Implemented using MATLAB Simulink and Simscape, the framework comprises modular voltage and load profiles, structured fault injection mechanisms, and labelled data generation tools. This work investigates the signal responses to various fault types, including mechanical backlash, voltage drop, and electrical resistance anomalies, both in isolation and combination. The simulation output enables systematic feature extraction and evaluation for diagnostics and health indicator development. The framework generated a high-fidelity dataset of 70,000 labelled samples, which demonstrated excellent feature separability for both single and compound faults under Principal Component Analysis. This research addresses the aerospace industry’s pressing need for synthetic, fault-labelled data to train and validate diagnostic algorithms and offers a scalable methodology applicable to diverse actuator configurations. The resulting openable, labelled dataset and modular scripts enable reproducible benchmarking for EMA health monitoring and will be extended to physics-informed prognostics in subsequent work.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fpace.2026.1736392</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fpace.2026.1736392</link>
        <title><![CDATA[“Interaction Twin in the middle”: a distributed digital twin architecture to model team interactions and dynamics for deep space missions]]></title>
        <pubdate>2026-03-27T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Patrick K. Pischulti</author><author>Min Young Hwang</author><author>Christopher McComb</author><author>Katya Arquilla</author>
        <description><![CDATA[NASA’s Moon to Mars campaign emphasizes the need for crews and habitat systems to operate with increasing autonomy as communication delays with Earth grow beyond 5 minutes. The digital twin framework has emerged as a promising solution to monitor, diagnose, predict, and optimize space systems, but prior aerospace applications have largely centered on system autonomy rather than crew autonomy. As a result, current approaches under-represent the interaction dynamics needed by mission control to continuously evolve procedure and accomplish mission objectives. This work introduces an Interaction Digital Twin (IDT) framework that twins the interactions between humans and systems rather than focusing only on individual entities. Built on a distributed digital twin architecture with bidirectional information flow, the framework integrates three complementary types of twins: Digital Twins for habitat systems, Human Digital Twins (HDTs) for individual crew members, and Interaction Digital Twins that capture emergent phenomena such as team cohesion, trust calibration, coordination, and adaptive autonomy. Twinning the interactions moves aspects of command and control on-board, giving crew mission-control-like capabilities even during periods of communication delay. We apply the framework to an Artemis Phase II mission scenario, demonstrating how interaction-level twinning extends system-level modeling to support cognitive workload management, information sharing, and human–autonomy teaming. By elevating interactions to first-class, inference-capable elements within the digital twin architecture, this framework bridges the gap between technical system models and the human teaming constructs essential for self-sufficient deep space exploration.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fpace.2025.1463425</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fpace.2025.1463425</link>
        <title><![CDATA[Formal verification of a machine learning tool for runway configuration assistance]]></title>
        <pubdate>2025-07-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Pouria Razzaghi</author><author>Milad Memarzadeh</author><author>Krishna Kalyanam</author>
        <description><![CDATA[This study explores the use of formal verification techniques to evaluate the efficacy of suggestions made by the Runway Configuration Assistance (RCA) tool, a machine learning-based decision support system that our group developed independently. By using model-checking approaches, in particular Computation Tree Logic (CTL), this study verifies the compliance of the RCA tool with predefined safety regulations under different conditions of surface winds. By simulating a range of scenarios at three major US airports, Charlotte Douglas International Airport (CLT), Denver International Airport (DEN), and Dallas-Fort Worth International Airport (DFW), we thoroughly test the predictions of the tool to ensure that they meet strict safety margins with respect to crosswind and tailwind. The application of formal verification methods provides a strict analysis of the RCA tool, enhancing its validity and utility for possible implementation in an operational environment. Initially, a Monte Carlo simulation is carried out to analyze all possible wind conditions both velocity-wise and direction-wise. This part is intended to rigorously test the model against extreme, worst-case conditions to evaluate its performance. Second, we improve our methodology by performing simulations driven by realistic scenarios informed by actual historical data. This approach allows for a more accurate reflection of typical wind conditions (seen in the test airport) and provides a robust assessment of the model’s effectiveness in maintaining safety standards under realistic environmental conditions. The model-checking reveals that overall 70% and 94% of the predictions satisfy the safety criteria in worst-case and realistic wind scenarios, respectively.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fpace.2025.1522006</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fpace.2025.1522006</link>
        <title><![CDATA[Pruning Bayesian networks for computationally tractable multi-model calibration]]></title>
        <pubdate>2025-05-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Nicolas Gratius</author><author>Mario Bergés</author><author>Burcu Akinci</author>
        <description><![CDATA[Anomaly response in aerospace systems increasingly relies on multi-model analysis in digital twins to replicate the system’s behaviors and inform decisions. However, computer model calibration methods are typically deployed on individual models and are limited in their ability to capture dependencies across models. In addition, model heterogeneity has been a significant issue in integration efforts. Bayesian Networks are well suited for multi-model calibration tasks as they can be used to formulate a mathematical abstraction of model components and encode their relationship in a probabilistic and interpretable manner. The computational cost of this method however increases exponentially with the graph complexity. In this work, we propose a graph pruning algorithm to reduce computational cost while minimizing the loss in calibration ability by incorporating domain-driven metrics for selection purposes. We implement this method using a Python wrapper for BayesFusion software and show that the resulting prediction accuracy outperforms existing pruning approaches which rely primarily on statistics.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fpace.2025.1454832</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fpace.2025.1454832</link>
        <title><![CDATA[Competency self-assessment for a learning-based autonomous aircraft system]]></title>
        <pubdate>2025-02-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Nicholas Conlon</author><author>Aastha Acharya</author><author>Jamison McGinley</author><author>Trevor Slack</author><author>Camron A. Hirst</author><author>Marissa D’Alonzo</author><author>Mitchell R. Hebert</author><author>Christopher Reale</author><author>Eric W. Frew</author><author>Rebecca Russell</author><author>Nisar R. Ahmed</author>
        <description><![CDATA[IntroductionFuture concepts for airborne autonomy point toward human operators moving out of the cockpit and into supervisory roles. Urban air mobility, airborne package delivery, and military intelligence, surveillance, and reconnaissance (ISR) are all actively exploring such concepts or currently undergoing this transition. Supervisors of these systems will be faced with many challenges, including platforms that operate outside of visual range and the need to decipher complex sensor or telemetry data in order to make informed and safe decisions with respect to the platforms and their mission. A central challenge to this new paradigm of non-co-located mission supervision is developing systems which have explainable and trustworthy autonomy and internal decision-making processes.MethodsCompetency self-assessments are methods that use introspection to quantify and communicate important information pertaining to autonomous system capabilities and limitations to human supervisors. We first discuss a computational framework for competency self-assessment: factorized machine self-confidence (FaMSeC). Within this framework, we then define the generalized outcome assessment (GOA) factor, which quantifies an autonomous system’s ability to meet or exceed user-specified mission outcomes. As a relevant example, we develop a competency-aware learning-based autonomous uncrewed aircraft system (UAS) and evaluate it within a multi-target ISR mission.ResultsWe present an analysis of the computational cost and performance of GOA-based competency reporting. Our results show that our competency self-assessment method can capture changes in the ability of the UAS to achieve mission critical outcomes, and we discuss how this information can be easily communicated to human partners to inform decision-making.DiscussionWe argue that competency self-assessment can enable AI/ML transparency and provide assurances that calibrate human operators with their autonomous teammate’s ability to meet mission goals. This in turn can lead to informed decision-making, appropriate trust in autonomy, and overall improvements to mission performance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fpace.2024.1475139</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fpace.2024.1475139</link>
        <title><![CDATA[ML meets aerospace: challenges of certifying airborne AI]]></title>
        <pubdate>2024-11-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Bastian Luettig</author><author>Yassine Akhiat</author><author>Zamira Daw</author>
        <description><![CDATA[Artificial Intelligence (AI) technologies can potentially revolutionize the aerospace industry with applications such as remote sensing data refinement, autonomous landing, and drone-based agriculture. However, safety concerns have prevented the widespread adoption of AI in commercial aviation. Currently, commercial aircraft do not incorporate AI components, even in entertainment or ground systems. This paper explores the intersection of AI and aerospace, focusing on the challenges of certifying AI for airborne use, which may require a new certification approach. We conducted a comprehensive literature review to identify common AI-enabled aerospace applications, classifying them by the criticality of the application and the complexity of the AI method. An applicability analysis was conducted to assess how existing aerospace standards - for system safety, software, and hardware - apply to machine learning technologies. In addition, we conducted a gap analysis of machine learning development methodologies to meet the stringent aspects of aviation certification. We evaluate current efforts in AI certification by applying the EASA concept paper and Overarching Properties (OPs) to a case study of an automated peripheral detection system (ADIMA). Aerospace applications are expected to use a range of methods tailored to different levels of criticality. Current aerospace standards are not directly applicable due to the manner in which the behavior is specified by the data, the uncertainty of the models, and the limitations of white box verification. From a machine learning perspective, open research questions were identified that address validation of intent and data-driven requirements, sufficiency of verification, uncertainty quantification, generalization, and mitigation of unintended behavior. For the ADIMA system, we demonstrated compliance with EASA development processes and achieved key certification objectives. However, many of the objectives are not applicable due to the human-centric design. OPs helped us to identify and uncover several defeaters in the applied ML technology. The results highlight the need for updated certification standards that take into account the unique nature of AI and its failure types. Furthermore, certification processes need to support the continuous evolution of AI technologies. Key challenges remain in ensuring the safety and reliability of AI systems, which calls for new methodologies in the machine learning community.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fpace.2023.1278726</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fpace.2023.1278726</link>
        <title><![CDATA[Trajectory generation based on power for urban air mobility]]></title>
        <pubdate>2023-10-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Russell A. Paielli</author>
        <description><![CDATA[A method of generating trajectories based on power is proposed for Urban Air Taxis. The method is simpler and more direct than traditional methods because it does not require a detailed aircraft model or a flight control model. Instead, it allows the user to specify the route, the static longitudinal profile (altitude as a function of distance), and a power model to determine the progress in time along that profile. The power model can be determined from a recorded or simulated trajectory of the same aircraft type. This capability allows a trajectory to be generated or reshaped to avoid conflicts while preserving the basic performance characteristics. Net or excess power is defined as the rate of change of mechanical (kinetic and potential) energy, and it is modeled as a function of airspeed. The time steps between discrete points in space along the trajectory are used to yield a specified power as a function of airspeed, and they are determined by solving a cubic polynomial at each point. An elliptical profile is used to generate an example trajectory. The dependence of trip flight time on various parameters is analyzed and plotted.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fpace.2023.1281522</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fpace.2023.1281522</link>
        <title><![CDATA[Grand challenges in intelligent aerospace systems]]></title>
        <pubdate>2023-09-12T00:00:00Z</pubdate>
        <category>Specialty Grand Challenge</category>
        <author>Kelly Cohen</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fpace.2023.1270551</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fpace.2023.1270551</link>
        <title><![CDATA[Editorial: Enabling technologies for advanced air mobility]]></title>
        <pubdate>2023-08-11T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Krishna M. Kalyanam</author><author>Kelly Cohen</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fpace.2023.1214115</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fpace.2023.1214115</link>
        <title><![CDATA[Comparison and synthesis of two aerospace case studies to develop human-autonomy teaming requirements]]></title>
        <pubdate>2023-07-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Güliz Tokadlı</author><author>Michael C. Dorneich</author>
        <description><![CDATA[This paper developed human-autonomy teaming (HAT) characteristics and requirements by comparing and synthesizing two aerospace case studies (Single Pilot Operations/Reduced Crew Operations and Long-Distance Human Space Operations) and the related recent HAT empirical studies. Advances in sensors, machine learning, and machine reasoning have enabled increasingly autonomous system technology to work more closely with human(s), often with decreasing human direction. As increasingly autonomous systems become more capable, their interactions with humans may evolve into a teaming relationship. However, humans and autonomous systems have asymmetric teaming capabilities, which introduces challenges when designing a teaming interaction paradigm in HAT. Additionally, developing requirements for HAT can be challenging for future operations concepts, which are not yet well-defined. Two case studies conducted previously document analysis of past literature and interviews with subject matter experts to develop domain knowledge models and requirements for future operations. Prototype delegation interfaces were developed to perform summative evaluation studies for the case studies. In this paper, a review of recent literature on HAT empirical studies was conducted to augment the document analysis for the case studies. The results of the two case studies and the literature review were compared and synthesized to suggest the common characteristics and requirements for HAT in future aerospace operations. The requirements and characteristics were grouped into categories of team roles, autonomous teammate types, interaction paradigms, and training. For example, human teammates preferred the autonomous teammate to have human-like characteristics (e.g., dialog-based conversation, social skills, and body gestures to provide cue-based information). Even though more work is necessary to verify and validate the requirements for HAT development, the case studies and recent empirical literature enumerate the types of functions and capabilities needed for increasingly autonomous systems to act as a teammate to support future operations.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fpace.2023.1176812</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fpace.2023.1176812</link>
        <title><![CDATA[Platooning in UAM airspace structures: applying trajectory shaping guidance law and exploiting cooperative localization]]></title>
        <pubdate>2023-06-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Melody N. Mayle</author><author>Rajnikant Sharma</author>
        <description><![CDATA[A novel control technique for the platooning of aerial vehicles is here introduced, and its stability is analyzed. The controller applies a missile guidance law that was initially adapted for path-following and subsequently extended to platooning. The positions of all agents within a platoon employing this controller are estimated by exploiting cooperative localization, and these estimated positions are fed back into the controller. Using simulation, the agents within a platoon are demonstrated to follow their desired path and avoid collision, even in environments with intermittent Global Positioning System signals and limited sensing ranges.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fpace.2023.1176969</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fpace.2023.1176969</link>
        <title><![CDATA[Traffic management protocols for advanced air mobility]]></title>
        <pubdate>2023-05-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Christopher Chin</author><author>Victor Qin</author><author>Karthik Gopalakrishnan</author><author>Hamsa Balakrishnan</author>
        <description><![CDATA[The demand for advanced air mobility (AAM) operations is expected to be at a much larger scale than conventional aviation. Additionally, AAM flight operators are likely to compete in providing a range of on-demand services in congested airspaces, with varying operational costs. These characteristics motivate the need for the development of new traffic management algorithms for advanced air mobility. In this paper, we explore the use of traffic management protocols (“rules-of-the-road” for airspace access) to enable efficient and fair operations. First, we show that it is possible to avoid gridlock and improve efficiency by leveraging the concepts of cycle detection and backpressure. We then develop a cost-aware traffic management protocol based on the second-price auction. Using simulations of representative advanced air mobility scenarios, we demonstrate that our traffic management protocols can help balance efficiency and fairness, in both the operational and the economic contexts.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fpace.2023.1184094</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fpace.2023.1184094</link>
        <title><![CDATA[Predicting sUAS conflicts in the national airspace with interacting multiple models and Haversine-based conflict detection system]]></title>
        <pubdate>2023-05-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>James Z. Wells</author><author>Manish Kumar</author>
        <description><![CDATA[In this paper, a conflict detection system for small Unmanned Aerial Vehicles (sUAS), composed of an interacting multiple model state predictor and a Haversine-distance based conflict detector, is proposed. The conflict detection system was developed and tested via a random recursive simulation in the ROS-Gazebo physics engine environment. The simulation consisted of ten small unmanned aerial vehicles flying along randomly assigned way-point navigation missions within a confined airspace. Way-points are generated from a uniform distribution and then sent to each vehicle. The interacting multiple model state predictor runs on a ground-based system and only has access to current vehicle positional information. It does not have access to the future way-points of individual vehicles. The state predictor is based on Kalman filters that utilize constant velocity, constant acceleration, and constant turn models. It generates near-future position estimates for all vehicles operating within an airspace. These models are probabilistically fused together and projected into the near-future to generate state predictions. These state predictions are then passed to the Haversine distance-based conflict detection algorithm to compare state estimates and identify probable conflicts. The conflicts are detected and flagged based on tunable threshold values which compare distances between predictions for the vehicles operating within the airspace. This paper discusses the development of the random recursive simulation for the ROS-Gazebo framework and the derivation of the interacting multiple model along-with the Haversine-based future conflict detector. The results are presented via simulation to highlight mid-air conflict detection application for sUAS operations in the National Airspace.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fpace.2023.1046177</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fpace.2023.1046177</link>
        <title><![CDATA[A multi-fidelity model management framework for multi-objective aerospace design optimisation]]></title>
        <pubdate>2023-02-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ben Parsonage</author><author>Christie Maddock</author>
        <description><![CDATA[This paper presents a multi-fidelity meta-modelling and model management framework designed to efficiently incorporate increased levels of simulation fidelity from multiple, competing sources into early-stage multidisciplinary design optimisation scenarios. Phase specific/invariant low-fidelity physics-based subsystem models are adaptively corrected via iterative sampling of high(er)-fidelity simulators. The correction process is decomposed into several distinct parametric/non-parametric stages, each leveraging alternate aspects of the available model responses. Globally approximating surrogates are constructed at each degree of fidelity (low, mid, and high) via an automated hyper-parameter selection and training procedure. The resulting hierarchy drives the optimisation process, with local refinement managed according to a confidence-based multi-response adaptive sampling procedure, with bias given to global parameter sensitivities. An application of this approach is demonstrated via the aerodynamic response prediction of a parametrized re-entry vehicle, subjected to a static/dynamic parameter optimisation for three separate single-objective problems. It is found that the proposed data correction process facilitates increased efficiency in attaining a desired approximation accuracy relative to a single-fidelity equivalent model. When applied within the proposed multi-fidelity management framework, clear convergence to the objective optimum is observed for each examined design optimisation scenario, outperforming an equivalent single-fidelity approach in terms of computational efficiency and solution variability.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fpace.2022.1076271</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fpace.2022.1076271</link>
        <title><![CDATA[Hybrid A* path search with resource constraints and dynamic obstacles]]></title>
        <pubdate>2023-01-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Alán Cortez</author><author>Bryce Ford</author><author>Indranil Nayak</author><author>Sriram Narayanan</author><author>Mrinal Kumar</author>
        <description><![CDATA[This paper considers path planning with resource constraints and dynamic obstacles for an unmanned aerial vehicle (UAV), modeled as a Dubins agent. Incorporating these complex constraints at the guidance stage expands the scope of operations of UAVs in challenging environments containing path-dependent integral constraints and time-varying obstacles. Path-dependent integral constraints, also known as resource constraints, can occur when the UAV is subject to a hazardous environment that exposes it to cumulative damage over its traversed path. The noise penalty function was selected as the resource constraint for this study, which was modeled as a path integral that exerts a path-dependent load on the UAV, stipulated to not exceed an upper bound. Weather phenomena such as storms, turbulence and ice are modeled as dynamic obstacles. In this paper, ice data from the Aviation Weather Service is employed to create training data sets for learning the dynamics of ice phenomena. Dynamic mode decomposition (DMD) is used to learn and forecast the evolution of ice conditions at flight level. This approach is presented as a computationally scalable means of propagating obstacle dynamics. The reduced order DMD representation of time-varying ice obstacles is integrated with a recently developed backtracking hybrid A∗ graph search algorithm. The backtracking mechanism allows us to determine a feasible path in a computationally scalable manner in the presence of resource constraints. Illustrative numerical results are presented to demonstrate the effectiveness of the proposed path-planning method.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fpace.2022.978261</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fpace.2022.978261</link>
        <title><![CDATA[Negotiation of the global grid inspection UAV with random delay uncertainty in an information communication network based on a robust fault tolerance mechanism]]></title>
        <pubdate>2023-01-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jie Shen</author><author>Wen qi Dong</author><author>Zhi-fang Wang</author><author>Jing Wang</author><author>Yang Wang</author><author>Han min Liu</author><author>Haiyan Li</author>
        <description><![CDATA[To accurately simulate the interference mechanism of information communication between unmanned aerial vehicles (UAVs) in the future global grid system, a type of control based on dynamic simulation of the satellite communication network and robust fault tolerance with a stochastic delay uncertain network system is proposed. Based on the imaginary future of the global energy Internet, with unknown information and communication interference, we established a UAV model from sensor to actuator network delay using a robust, fault-tolerant control algorithm and a satellite communication network model that combined the controller’s mathematical model. The simulation results showed improved power transmission capability and communication coverage ability of UAVs by using the network fault-tolerant control mechanism with uncertain network delay and information communication interference. The stability and anti-interference performance was also significantly improved. This algorithm provides a strategy for the future development of global energy Internet.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fpace.2022.1071793</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fpace.2022.1071793</link>
        <title><![CDATA[Exploring online and offline explainability in deep reinforcement learning for aircraft separation assurance]]></title>
        <pubdate>2022-12-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Wei Guo</author><author>Yifei Zhou</author><author>Peng Wei</author>
        <description><![CDATA[Deep Reinforcement Learning (DRL) has demonstrated promising performance in maintaining safe separation among aircraft. In this work, we focus on a specific engineering application of aircraft separation assurance in structured airspace with high-density air traffic. In spite of the scalable performance, the non-transparent decision-making processes of DRL hinders human users from building trust in such learning-based decision making tool. In order to build a trustworthy DRL-based aircraft separation assurance system, we propose a novel framework to provide stepwise explanations of DRL policies for human users. Based on the different needs of human users, our framework integrates 1) a Soft Decision Tree (SDT) as an online explanation provider to display critical information for human operators in real-time; and 2) a saliency method, Linearly Estimated Gradient (LEG), as an offline explanation tool for certification agencies to conduct more comprehensive verification time or post-event analyses. Corresponding visualization methods are proposed to illustrate the information in the SDT and LEG efficiently: 1) Online explanations are visualized with tree plots and trajectory plots; 2) Offline explanations are visualized with saliency maps and position maps. In the BlueSky air traffic simulator, we evaluate the effectiveness of our framework on case studies with complex airspace route structures. Results show that the proposed framework can provide reasonable explanations of multi-agent sequential decision-making. In addition, for more predictable and trustworthy DRL models, we investigate two specific patterns that DRL policies follow based on similar aircraft locations in the airspace.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fpace.2022.1064142</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fpace.2022.1064142</link>
        <title><![CDATA[Wind-optimal lateral trajectories for a multirotor aircraft in urban air mobility]]></title>
        <pubdate>2022-11-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Priyank Pradeep</author><author>Gano B. Chatterji</author><author>Todd A. Lauderdale</author><author>Kapil Sheth</author><author>Chok Fung Lai</author><author>Heinz Erzberger</author><author>Banavar Sridhar</author>
        <description><![CDATA[The primary motivation for this paper is to quantify the operational benefits (energy consumption and flight duration) of flying wind-optimal lateral trajectories for short flights (less than 60 miles) anticipated in the urban environment. The optimal control model presented includes a wind model for quantifying the effect of wind on the lateral trajectory. The optimal control problem is numerically solved using the direct collocation method. Energy consumption and flight duration flying wind-optimal lateral trajectories are compared with corresponding values obtained flying great-circle paths between the same origin and destination pairs to determine the operational benefits of wind-optimal routing for short flights. The flight duration results for different scenarios are validated using a simulation tool designed and developed at NASA for exploring advanced air traffic management concepts. This research study suggests that for short flights in an urban environment, operational benefits of the wind-optimal lateral trajectories over the corresponding great-circle trajectories in terms of energy consumption and flight duration per flight are dependent on: i) wind field’s spatial variability, ii) wind magnitude, iii) the direction of route relative to the wind field, and iv) cruise segment length. The operational benefits observed in realistic flyable wind scenarios are less than 2.5%; these could be translated to an equivalent of a maximum of 2 min of cruise flight duration savings in the urban air mobility environment. As expected, headwinds and tailwinds along the flight route most significantly impact energy consumption and flight duration.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fpace.2022.1036642</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fpace.2022.1036642</link>
        <title><![CDATA[Bayesian state estimation in partially-observed dynamic multidisciplinary systems]]></title>
        <pubdate>2022-11-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Negar Asadi</author><author>Seyede Fatemeh Ghoreishi</author>
        <description><![CDATA[Multidisciplinary systems comprise several disciplines that are connected to each other with feedback coupled interactions. These coupled multidisciplinary systems are often observed through sensors providing noisy and partial measurements from these systems. A large number of disciplines and their complex interactions pose a huge uncertainty in the behavior of multidisciplinary systems. The reliable analysis and monitoring of these partially-observed multidisciplinary systems require an accurate estimation of their underlying states, in particular the coupling variables which characterize their stability. In this paper, we present a probabilistic state-space formulation of coupled multidisciplinary systems and develop a particle filtering framework for state estimation of these systems through noisy time-series measurements. The performance of the proposed framework is demonstrated through comprehensive numerical experiments using a coupled aerostructural system and a fire detection satellite. We empirically analyze the impact of monitoring a single discipline on state estimation of the entire coupled system.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fpace.2022.982808</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fpace.2022.982808</link>
        <title><![CDATA[Minimal length multi-segment clothoid return paths for vehicles with turn rate constraints]]></title>
        <pubdate>2022-10-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Theodore Tuttle</author><author>Jay P. Wilhelm</author>
        <description><![CDATA[Continuous curvature recovery paths are needed to accurately return a fixed wing autonomous vehicle with turn rate constraints back to a missions path in the correct direction after collision avoidance. Clothoid paths where curvature is linearly dependent to arc length can be used to make multi-segment splines with continuous curvature, but require optimization to ensure that the path is of minimal length while meeting curvature and sharpness limits. The present work considers the problem of returning a fixed wing aircraft back to its original path facing the correct direction after a leaving it during collision avoidance by presenting a method of optimizing a three segment clothoid spline to be of minimal length while meeting fixed wing turn rate constraints and targeting a path function. The impact of this work is enabling accurate path recovery after collision avoidance with minimal length paths that minimize the time spent off a missions planned route, giving better control over time of arrival for the planned route and more time to complete mission objectives.]]></description>
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