000209299 001__ 209299
000209299 005__ 20190317000221.0
000209299 0247_ $$2doi$$a10.1016/j.robot.2015.06.003
000209299 022__ $$a0921-8890
000209299 02470 $$2ISI$$a000359170500024
000209299 037__ $$aARTICLE
000209299 245__ $$aEvaluation of control strategies for fixed-wing drones following slow-moving ground agents
000209299 269__ $$a2015
000209299 260__ $$bElsevier$$c2015$$aAmsterdam
000209299 300__ $$a10
000209299 336__ $$aJournal Articles
000209299 520__ $$aThere are many situations where fixed-wing drones may be required to track ground moving agents, such as humans or cars, which are typically slower than drones. Some control strategies have been proposed and validated in simulations using the average distance between the target and the drone as a performance metric. However, besides the distance metric, energy expenditure of the flight also plays an important role in assessing the overall performance of the flight. In this paper, we propose a new methodology that introduces a new metric (energy expenditure), we compare existing methods on a large set of target motion patterns and present a comparison between the simulation and field experiments on proposed target motion patterns. Using this new methodology we examine the performance of three control strategies: the Lyapunov Guidance Vector Field strategy, the Bearing-only strategy and the Oscillatory strategy. Among the three strategies considered, we demonstrate that the Lyapunov Guidance Vector Field strategy has the best performance for all target motion patterns. Field experiments with fixed-wing drones provide additional insights into the benefits and shortcomings of each strategy in practice.
000209299 6531_ $$aTarget Tracking
000209299 6531_ $$aFixed-wing UAVs
000209299 6531_ $$aAerial Robotics
000209299 700__ $$0245553$$g205906$$aVarga, Maja
000209299 700__ $$0240674$$g104340$$aZufferey, Jean-Christophe
000209299 700__ $$g212078$$aHeitz, Grégoire Hilaire Marie$$0246544
000209299 700__ $$aFloreano, Dario$$g111729$$0240742
000209299 773__ $$tRobotics and Autonomous Systems
000209299 8564_ $$uhttps://infoscience.epfl.ch/record/209299/files/1-s2.0-S0921889015001311-main.pdf$$zPreprint$$s2157745$$yPreprint
000209299 909C0 $$xU10370$$0252161$$pLIS
000209299 909CO $$ooai:infoscience.tind.io:209299$$qGLOBAL_SET$$pSTI$$particle
000209299 917Z8 $$x205906
000209299 917Z8 $$x255330
000209299 937__ $$aEPFL-ARTICLE-209299
000209299 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000209299 980__ $$aARTICLE