Competitive drone racing using asymmetric games

Amin Almozel, Jeff S. Shamma

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper presents a game theoretic approach to solve the problem of drone racing. A game theory planner (GTP) strategizes against an opponent by using an iterated best response learning method from game theory. To complement the functionality of the GTP, a minimum jerk polynomial trajectory generation algorithm is used to generate a reference track. Moreover, a time-varying linear model predictive controller (MPC) is used to execute the strategic path generated by the GTP. The performance of the GTP is compared against a pure MPC, a Policy Improvement (PI) racer, and itself under different parameters. Intuitive competitive behaviors such as blocking and overtaking came naturally as a result of the algorithm. Also, interesting match-up and lead-dependent performance advantage is observed.
Original languageEnglish (US)
Title of host publication2021 18th International Conference on Ubiquitous Robots (UR)
Number of pages8
ISBN (Print)9781665438995
StatePublished - Jul 12 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-08-24


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