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Aerospace Systems Design Laboratory (ASDL)

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Author: Choi, Younghoon × Author: Choi, Youngjun × End date: 2019 × Start date: 2010 × search.filters.applied.f.isOrgUnitOfPublicationTitle: Daniel Guggenheim School of Aerospace Engineering × search.filters.applied.f.resourcesubtype: Paper ×

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Now showing 1 - 4 of 4
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Multi-UAS path-planning for a large-scale disjoint disaster management

2019-06 , Choi, Younghoon , Choi, Youngjun , Briceno, Simon , Mavris, Dimitri N.

A UAS-based disaster management method has been adopted to monitor the disaster impact and protect human lives since it can be rapidly deployed, execute an aerial imaging mission, and provide a cost-efficient operation. In the case of a wildfire disaster, a disaster management is highly complex because of large-scale wildfires that can occur simultaneously and disjointly in a large area. In order to effectively manage these large-scale wildfires, it requires multiple UAS with multiple ground stations. However, conventional UAS-based management methods relies on a single ground station that can have a limitation to handle the large-scale wildfire problem. This paper presents a new path-planning framework for UAS operations including a fleet of UAVs and multiple ground stations. The framework consists of two parts: creating coverage paths for each wildfire and optimizing routes for each UAV. To test the developed framework, this paper uses representative wildfire scenarios in the State of California.

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Three-dimensional UAS trajectory optimization for remote sensing in an irregular terrain environment

2018-06 , Choi, Youngjun , Choi, Younghoon , Briceno, Simon , Mavris, Dimitri N. ,

This paper presents a novel algorithm for three-dimensional UAS trajectory optimization for a remote sensing mission in an irregular terrain environment. The algorithm consists of three steps: terrain modeling, the selection of scanning waypoints, and trajectory optimization. The terrain modeling process obtains a functional model using a Gaussian process from terrain information with a point cloud. The next step defines scanning waypoints based on the terrain model information, sensor specifications, and the required image resolution. For the selection of the waypoints, this paper introduces two different approaches depending on the direction of the viewing angle: a normal offset method and a vertical offset method. In the trajectory optimization, the proposed algorithm solves a distance-constraint vehicle routing problem to identify the optimum scanning route based on the waypoints and UAS constraints. Numerical simulations are conducted with two different UAS trajectory scanning methods in a realistic scenario, Point Loma in San Diego.

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An Extended Savings Algorithm for UAS-based Delivery Systems

2019 , Choi, Younghoon , Choi, Youngjun , Briceno, Simon I. , Mavris, Dimitri N.

This paper presents an extended savings algorithm for a package delivery system using unmanned aircraft systems (UAS). The savings algorithm as a heuristic method solves a vehicle routing problem (VRP) that is commonly formulated by an operational plan for each vehicle. In general, package delivery systems need to establish an operational plan based on demand and preferred time to be visited for each customer. In UAS-based delivery systems, however, capacity and traveling time constraints must be additionally considered to create their operational schedules because of limited payload capacity and short endurance of unmanned aerial vehicles (UAVs). Because of these limitations, UAVs should be reused during operation hours to reduce acquisition costs. Thus, a recharging strategy should be included in the operational planning process. However, conventional savings algorithms cannot capture those properties at once because they have mainly focused on delivery systems with conventional vehicles such as trucks and passenger/cargo aircraft that have different vehicle features and operational characteristics, such as the endurance/speed of a vehicle and recharging strategy. To overcome the limitations of the conventional approaches, this paper proposes the extended savings algorithm, which can concurrently reflect the characteristics of both delivery systems and UAVs. To demonstrate the proposed extended savings algorithm this paper preforms numerical simulations with two representative scenarios in Annapolis, MD and Macon, GA.

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Coverage path planning for a UAS imagery mission using column generation with a turn penalty

2018-06 , Choi, Younghoon , Choi, Youngjun , Briceno, Simon , Mavris, Dimitri N.

This paper introduces a novel Coverage Path Planning (CCP) algorithm for a Unmanned Aerial Systems (UAS) imagery mission. The proposed CPP algorithm is a vehicle-routing-based approach using a column generation method. In general, one of the main issues of the traditional arc-based vehicle routing approaches is imposing a turn penalty in a cost function because a turning motion of vehicle requires the more amount of energy than a cruise motion. However, the conventional vehicle-routing-based approaches for the CPP cannot capture a turning motion of the vehicle. This limitation of the arc-based mathematical model comes from the property of turning motions, which should be evaluated from two arcs because a turn motion occurs at a junction of the arcs. In this paper, to mitigate the limitation, a route-based model using column generation approach with a turn penalty is proposed. To demonstrate the proposed CPP approach, numerical simulations are conducted with a conventional CPP algorithm.

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