000106149 001__ 106149
000106149 005__ 20210902121926.0
000106149 0247_ $$2doi$$a10.1109/TRO.2020.2994881
000106149 0248_ $$2sideral$$a120557
000106149 037__ $$aART-2020-120557
000106149 041__ $$aeng
000106149 100__ $$aSpica, R.
000106149 245__ $$aA Real-Time Game Theoretic Planner for Autonomous Two-Player Drone Racing
000106149 260__ $$c2020
000106149 5060_ $$aAccess copy available to the general public$$fUnrestricted
000106149 5203_ $$aIn this article, we propose an online 3-D planning algorithm for a drone to race competitively against a single adversary drone. The algorithm computes an approximation of the Nash equilibrium in the joint space of trajectories of the two drones at each time step, and proceeds in a receding horizon fashion. The algorithm uses a novel sensitivity term, within an iterative best response computational scheme, to approximate the amount by which the adversary will yield to the ego drone to avoid a collision. This leads to racing trajectories that are more competitive than without the sensitivity term. We prove that the fixed point of this sensitivity enhanced iterative best response satisfies the first-order optimality conditions of a Nash equilibrium. We present results of a simulation study of races with 2-D and 3-D race courses, showing that our game theoretic planner significantly outperforms amodel predictive control (MPC) racing algorithm. We also present results of multiple drone racing experiments on a 3-D track in which drones sense each others'' relative position with onboard vision. The proposed game theoretic planner again outperforms the MPC opponent in these experiments where drones reach speeds up to 1.25m/s.
000106149 536__ $$9info:eu-repo/grantAgreement/ES/MCIU-AEI-FEDER/PGC2018-098719-B-I00
000106149 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000106149 590__ $$a5.567$$b2020
000106149 591__ $$aROBOTICS$$b4 / 28 = 0.143$$c2020$$dQ1$$eT1
000106149 592__ $$a2.027$$b2020
000106149 593__ $$aComputer Science Applications$$c2020$$dQ1
000106149 593__ $$aElectrical and Electronic Engineering$$c2020$$dQ1
000106149 593__ $$aControl and Systems Engineering$$c2020$$dQ1
000106149 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000106149 700__ $$aCristofalo, E.
000106149 700__ $$aWang, Z.J.
000106149 700__ $$0(orcid)0000-0002-5176-3767$$aMontijano, E.$$uUniversidad de Zaragoza
000106149 700__ $$aSchwager, M.
000106149 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000106149 773__ $$g36, 5 (2020), 1389-1403$$pIEEE Trans. Robot.$$tIEEE Transactions on Robotics$$x1552-3098
000106149 8564_ $$s8045748$$uhttps://zaguan.unizar.es/record/106149/files/texto_completo.pdf$$yPostprint
000106149 8564_ $$s3680876$$uhttps://zaguan.unizar.es/record/106149/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000106149 909CO $$ooai:zaguan.unizar.es:106149$$particulos$$pdriver
000106149 951__ $$a2021-09-02-10:51:42
000106149 980__ $$aARTICLE