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1. A Utility-Based Path Planning for Safe UAS Operations with a Task-Level Decision-Making Capability

   https://doi.org/10.1109/SMC.2019.8914226  

   Kaya, Uluhan C.; Dogan, Atilla; Huber, Manfred (October 2019, 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC))

   Unmanned aircraft systems (UAS) are being used more and more every day in almost any area to solve challenging real-life problems. Increased autonomy and advancements in low-cost high-computing technologies made these compact autonomous solutions accessible to any party with ease. However, this ease of use brings its own challenges that need to be addressed. In an autonomous flight scenario over a public space, an autonomous operation plan has to consider the public safety and regulations as well as the task specific objectives. In this work, we propose a generic utility function for the path planning of UAS operations that includes the benefits of accomplishing the goals as well as the safety risks incurred along the flight trajectories, with the purpose of making task-level decisions through the optimization of the carefully constructed utility function for a given scenario. As an optimizer, we benefited from a multi-tree variant of the optimal T-RRT * (Multi-T-RRT * path planning algorithm. To illustrate its operation, results of simulation of a UAS scenario are presented. 

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2. Large-scale urban building function mapping by integrating multi-source web-based geospatial data

   https://doi.org/10.1080/10095020.2023.2264342  

   Chen, Wei; Zhou, Yuyu; Stokes, Eleanor C.; Zhang, Xuesong (October 2023, Geo-spatial Information Science)

   Morphological (e.g. shape, size, and height) and function (e.g. working, living, and shopping) information of buildings is highly needed for urban planning and management as well as other applications such as city-scale building energy use modeling. Due to the limited availability of socio-economic geospatial data, it is more challenging to map building functions than building morphological information, especially over large areas. In this study, we proposed an integrated framework to map building functions in 50 U.S. cities by integrating multi-source web-based geospatial data. First, a web crawler was developed to extract Points of Interest (POIs) from Tripadvisor.com, and a map crawler was developed to extract POIs and land use parcels from Google Maps. Second, an unsupervised machine learning algorithm named OneClassSVM was used to identify residential buildings based on landscape features derived from Microsoft building footprints. Third, the type ratio of POIs and the area ratio of land use parcels were used to identify six non-residential functions (i.e. hospital, hotel, school, shop, restaurant, and office). The accuracy assessment indicates that the proposed framework performed well, with an average overall accuracy of 94% and a kappa coefficient of 0.63. With the worldwide coverage of Google Maps and Tripadvisor.com, the proposed framework is transferable to other cities over the world. The data products generated from this study are of great use for quantitative city-scale urban studies, such as building energy use modeling at the single building level over large areas. 

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3. Probabilistic wind uplift resistance framework for the relative evaluation of wood-frame load paths

   https://doi.org/10.1016/j.engstruct.2023.116984  

   Rittelmeyer, Brandon M; Roueche, David B (January 2024, Engineering Structures)

   Past failure risk analyses of wind-impacted wood-frame structural load paths have tended to consider simplified resistance models that account for a few key load path connections, in which connection capacity distributions are generally based on benchmark experimental results. However, recent post-storm reconnaissance studies have demonstrated that connections in the load path of light wood-frame structures are themselves composed of multiple elements with many configurations and possible failure modes. This study presents a flexible approach for modeling wind uplift resistance in wood-frame load paths that includes a more exhaustive set of potential failure points yet is computationally efficient and readily adaptable to various load paths composed of different assemblages of structural members and connections. In this framework, ultimate capacities of connections and wood members are either based on design equations provided in the National Design Specification for Wood Construction or another applicable standard or computed from a comparable mechanics-based model. Analytical capacity estimates for roof sheathing, roof-to-wall connections, and wall-to-slab-foundation connections accord well with the range of published experimental results for these connections. Capacities of connections that act in parallel are summed to transform the load path into an analogous load chain of series components. System-level wind uplift resistance, defined by the weakest component in series, is evaluated by Monte Carlo simulation. By providing a more complete description of resistance than previous simplified models have done while avoiding the expense of a detailed finite-element or other solid mechanics model, the method proposed here holds promise as a rapid, consistent, and accurate way to quantify wind resistance in any arbitrary wood-frame load path, with applications including insurance risk analysis, hybrid data science frameworks utilizing post-storm reconnaissance data, and estimation of hazard intensity from structural damage observations. 

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4. A Predictive Control Framework for UAS Trajectory Planning Considering 4G/5G Communication Link Quality

   https://doi.org/10.1109/ICNS58246.2023.10124315  

   Zuo, Rui; Wang, Zixi; Caicedo Bastidas, Carlos E.; Cenk Gursoy, M.; Solomon, Adrian; Qiu, Qinru (April 2023, Integrated Communication, Navigation and Surveillance Conference (ICNS))

   A reliable command and control (C2) data link is required for unmanned aircraft systems (UAS) operations in order to monitor the status and support the control of UAS. A practical realization of the C2 communication and mission data links for commercial UAS operations is via LTE/5G networks. While the trajectory of each UAS directly determines the flight distance and mission cost in terms of energy dissipation, it also has a strong correlation to the quality of the communication link provided by a serving base station, where quality is defined as the achieved signal-to-interference-plus-noise ratio (SINR) required to maintain the control link of the UAS. Due to signal interference and the use of RF spectrum resources, the trajectory of a UAS not only determines the communication link quality it will encounter, but also influences the link quality of other UAS in its vicinity. Therefore, effective UAS traffic management must plan the trajectory for a group of UAS taking into account the impact to the interference levels of other base stations and UAS communication links. In this paper, an SINR Aware Predictive Planning (SAPP) framework is presented for trajectory planning of UAS leveraging 4G/5G communication networks in a simulated environment. The goal is to minimize flight distance while ensuring a minimum required link quality for C2 communications between UAS and base stations. The predictive control approach is proposed to address the challenges of the time varying SINR caused by the interference from other UAS’s communication. Experimental results show that the SAPP framework provides more than 3dB improvements on average for UAS communication parameters compared to traditional trajectory planning algorithms while still achieving shortest path trajectories and collision avoidance. 

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5. Airspace Geofencing and Flight Planning for Low-Altitude, Urban, Small Unmanned Aircraft Systems

   https://doi.org/10.3390/app12020576  

   Kim, Joseph; Atkins, Ella (January 2022, Applied Sciences)

   Airspace geofencing is a key capability for low-altitude Unmanned Aircraft System (UAS) Traffic Management (UTM). Geofenced airspace volumes can be allocated to safely contain compatible UAS flight operations within a fly-zone (keep-in geofence) and ensure the avoidance of no-fly zones (keep-out geofences). This paper presents the application of three-dimensional flight volumization algorithms to support airspace geofence management for UTM. Layered polygon geofence volumes enclose user-input waypoint-based 3-D flight trajectories, and a family of flight trajectory solutions designed to avoid keep-out geofence volumes is proposed using computational geometry. Geofencing and path planning solutions are analyzed in an accurately mapped urban environment. Urban map data processing algorithms are presented. Monte Carlo simulations statistically validate our algorithms, and runtime statistics are tabulated. Benchmark evaluation results in a Manhattan, New York City low-altitude environment compare our geofenced dynamic path planning solutions against a fixed airway corridor design. A case study with UAS route deconfliction is presented, illustrating how the proposed geofencing pipeline supports multi-vehicle deconfliction. This paper contributes to the nascent theory and the practice of dynamic airspace geofencing in support of UTM. 

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