(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.
(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.
(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.