The use of flight simulation tools to reduce the schedule, risk, and required amount of flight testing for complex aerospace systems is a well-recognized benefit of these approaches. However, some special challenges arise when one attempts to obtain these benefits for the development and operation of an Experimental Uninhabited Aerial Vehicle (UAV) system. These types of UAV systems are characterized by the need for the need for continual checkout of experimental software and hardware. Also, flight-testing can be further leveraged by complementing research results with flight-test validated simulation results for the same experimental UAV. In this paper, flight simulation architectures for system design, integration, and operation of an experimental helicopter-based UAV, the GTMax, are explored, and the development of a simulation tool described. The chosen helicopter-based UAV platform (a Yamaha R-Max) is well instrumented: differential GPS, inertial measurement unit, sonar altimeter, radar altimeter, and a 3-axis magnetometer. One or two flight processors can be utilized.

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Short distance travel and commute being inevitable, safe route planning in pandemics for micro-mobility, i.e., cycling and walking, is extremely important for the safety of oneself and others. Hence, we propose an application-based solution using COVID-19 occurrence data and a multi-criteria route planning technique for cyclists and pedestrians. This study aims at objectively determining the routes based on various criteria on COVID-19 safety of a given route while keeping the user away from potential COVID-19 transmission spots. The vulnerable spots include places such as a hospital or medical zones, contained residential areas, and roads with a high connectivity and influx of people. The proposed algorithm returns a multi-criteria route modeled on COVID-19-modified parameters of micro-mobility and betweenness centrality considering COVID-19 avoidance as well as the shortest available safe route for user ease and shortened time of outside environment exposure. We verified our routing algorithm in a part of Delhi, India, by visualizing containment zones and medical establishments. The results with COVID-19 data analysis and route planning suggest a safer route in the context of the coronavirus outbreak as compared to normal navigation and on average route extension is within 8%–12%. Moreover, for further advancement and post-COVID-19 era, we discuss the need for adding open data policy and the spatial system architecture for data usage, as a part of a pandemic strategy. The study contributes new micro-mobility parameters adapted for COVID-19 and policy guidelines based on aggregated contact tracing data analysis maintaining privacy, security, and anonymity.

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