000222495 001__ 222495
000222495 005__ 20180913063935.0
000222495 037__ $$aCONF
000222495 245__ $$aA Comparative Study of Collision Avoidance Algorithms for Unmanned Aerial Vehicles: Performance and Robustness to Noise
000222495 269__ $$a2016
000222495 260__ $$c2016
000222495 336__ $$aConference Papers
000222495 520__ $$aOver the past years, the field of small unmanned aerial vehicles has grown significantly and several applications have appeared, requiring always more autonomous flight. An important remaining challenge for fully autonomous unmanned aerial vehicles is collision avoidance between aircraft. In this work, we will compare two collision avoidance algorithms in terms of performance and robustness to sensor noise. We will leverage both experiments with real vehicles and calibrated, realistic simulations to get an insight of the effect of noise on collision avoidance. Our results show that although algorithms that use velocity as input are better in minimizing velocity variation and generally produces more efficient trajectories, they are less robust to perception noise. On the other hand, position-based algorithms that typically generate slower and longer avoidance maneuvers, become competitive at high levels of sensor noise.
000222495 700__ $$0246514$$aRoelofsen, Steven Adriaan$$g178296
000222495 700__ $$0241784$$aGillet, Denis$$g105245
000222495 700__ $$0241071$$aMartinoli, Alcherio$$g105782
000222495 7112_ $$a13th International Symposium on Distributed Autonomous Robotic Systems$$cNatural History Museum, London, UK$$dNovember 6-9, 2016
000222495 8564_ $$s5325048$$uhttps://infoscience.epfl.ch/record/222495/files/DARS_2016.pdf$$yn/a$$zn/a
000222495 909C0 $$0252406$$pREACT$$xUS09295
000222495 909C0 $$0252151$$pDISAL$$xU11904
000222495 909CO $$ooai:infoscience.tind.io:222495$$pconf$$pSTI$$pENAC
000222495 917Z8 $$x178296
000222495 937__ $$aEPFL-CONF-222495
000222495 973__ $$aEPFL$$rREVIEWED$$sACCEPTED
000222495 980__ $$aCONF