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Daniel Guggenheim School of Aerospace Engineering
Daniel Guggenheim School of Aerospace Engineering
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ItemMulti-UAS path-planning for a large-scale disjoint disaster management(Georgia Institute of Technology, 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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ItemA framework for concurrent design and route planning optimization of unmanned aerial vehicle based urban delivery systems(Georgia Institute of Technology, 2019-05-21) Choi, YounghoonWith the emergence of new technologies for small Unmanned Aircraft Systems (sUAS), such as lightweight sensors and high-efficiency batteries, the operation of small Unmanned Aerial Vehicles (sUAVs) has expanded from military use to commercial use. A promising commercial application of sUAS is package delivery because of its potential to reduce acquisition and operating costs of the last-mile delivery system while enabling new services such as same-day delivery. Furthermore, in urban areas, sUAVs can deliver packages to customers without negatively affecting street traffic. This thesis addresses an extended sizing and synthesis process that considers the performance of a sUAS in its total cost optimized routing to size the vehicles for sUAS-based delivery systems. This problem is called the concurrent aircraft design and routing problem. Based on decomposition approaches, this problem can be divided into three parts: the sizing, the route planning, and the integration of the two. However, the existing methods have mainly focused either UAV design or optimization of operations of UAVs. Although a concurrent UAV design and routing problem is addressed, only simple routing problems are studied without considering the obstructed environment like an urban area. To fully address the concurrent aircraft design and routing problem for sUAS-based delivery systems, this thesis presents a novel modular framework including all three parts of this problem: the UAV design module, the UAV routing module, and the integration method. First, for the UAV routing module, this thesis presents an endurance-constrained Multi-Trip Vehicle Routing Problem with time windows (MTVRPTW) optimization model that is an extension of the MTVRPTW optimization model. The MTVRPTW model builds a vehicle's schedule including a reuse plan for an on-demand delivery system. However, the MTVRPTW model does not consider the property of sUAV's limited endurance. To alleviate the limitation of the MTVRPTW model, the endurance-constrained MTVRPTW model employs maximum endurance constraints that trace flight time of each vehicle and restrict flight time to vehicle's maximum endurance. Moreover, to address the urban environment with the optimization model, this thesis presents a framework for creating a two-layered urban flight network as an input graph of a Vehicle Routing Problem (VRP) optimization model. The urban flight network is built by feeding airborne Light Detection And Ranging (LiDAR) sensor data into an algorithm that uses a Voronoi diagram to create collision-free paths. By integrating the endurance-constrained MTVRPTW model with the two-layered urban flight network, vehicle's schedule for sUAS-based urban delivery is created. Second, for the UAV design module, a component-based sizing and synthesis process for small fixed-wing VTOL UAVs is implemented. The sizing and synthesis process is an extension of traditional fixed-wing aircraft sizing and synthesis tool. The implemented process can consider vertical flight capacity and provide a take-off weight optimized combination of components of the propulsion system. The main intent of implementing the sizing and synthesis process is to make the framework for the concurrent UAV design and routing problem. Thus, the framework can be extended by integrating other sizing and synthesis tools if the interface is matched. Lastly, to integrate the UAV design module with the UAV routing module, existing methods have used a sequential approach; after conducting the sizing module, the vehicle routing problem is solved. However, the input and output of the two modules are coupled each other. Thus, the methods cannot address a converged solution for both modules. To alleviate the limitation, this thesis presents a novel modular framework for the concurrent aircraft design and routing problem for sUAS-based delivery systems, which is based on a Fixed Point Iteration (FPI) method to find a converged solution of the coupled problem. The presented framework can provide an optimal vehicle design and routing for the sUAS-based delivery system concurrently. This thesis uses the developed framework for concurrent UAV design and routing to study a possible package delivery using sUAS in San Diego, CA. The result shows that the developed framework can take into account both planning vehicle operation on the flight network in which it will be operating and designing the flight network capable of addressing the obstructed environment as part of the vehicle design process.
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ItemMulti-UAV Trajectory Optimization Utilizing a NURBS-Based Terrain Model for an Aerial Imaging Mission(Georgia Institute of Technology, 2019-05) Choi, Youngjun ; Chen, Mengzhen ; Choi, Younghoon ; Briceno, Simon ; Mavris, Dimitri N.Trajectory optimization precisely scanning an irregular terrain is a challenging problem since the trajectory optimizer needs to handle complex geometry topology, vehicle performance, and a sensor specification. To address these problems, this paper introduces a novel framework of a multi-UAV trajectory optimization method for an aerial imaging mission in an irregular terrain environment. The proposed framework consists of terrain modeling and multi-UAV trajectory optimization. The terrain modeling process employs a Non-Uniform Rational B-Spline (NURBS) surface fitting method based on point cloud information resulting from an airborne LiDAR sensor or other sensor systems. The NURBS-based surface model represents a computationally efficient terrain topology. In the trajectory optimization method, the framework introduces a multi-UAV vehicle routing problem enabling UAV to scan an entire area of interest, and obtains feasible trajectories based on given vehicle performance characteristics, and sensor specifications, and the approximated terrain model. The proposed multi-UAV trajectory optimization algorithm is tested by representative numerical simulations in a realistic aerial imaging environment, namely, San Diego and Death Valley, California.
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ItemEnergy-Constrained Multi-UAV Coverage Path Planning for an Aerial Imagery Mission Using Column Generation(Georgia Institute of Technology, 2019-03) Choi, Younghoon ; Choi, Youngjun ; Briceno, Simon ; Mavris, Dimitri N.This paper presents a new Coverage Path Planning (CPP) method for an aerial imaging mission with multiple Unmanned Aerial Vehicles (UAVs). In order to solve a CPP problem with multicopters, a typical mission profile can be defined with five mission segments: takeoff, cruise, hovering, turning, and landing. The traditional arc-based optimization approaches for the CPP problem cannot accurately estimate actual energy consumption to complete a given mission because they cannot account for turning phases in their model, which may cause non-feasible routes. To solve the limitation of the traditional approaches, this paper introduces a new route-based optimization model with column generation that can trace the amount of energy required for all different mission phases. This paper executes numerical simulations to demonstrate the effectiveness of the proposed method for both a single UAV and multiple UAV scenarios for CPP problems.
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ItemAn Extended Savings Algorithm for UAS-based Delivery Systems(Georgia Institute of Technology, 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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ItemCoverage path planning for a UAS imagery mission using column generation with a turn penalty(Georgia Institute of Technology, 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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ItemThree-dimensional UAS trajectory optimization for remote sensing in an irregular terrain environment(Georgia Institute of Technology, 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.