Competitive drone racing using asymmetric games

Amin Almozel, Jeff S. Shamma

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

Abstract

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 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages349-356
Number of pages8
ISBN (Electronic)9781665438995
DOIs
StatePublished - Jul 12 2021
Event18th International Conference on Ubiquitous Robots, UR 2021 - Gangneung-si, Gangwon-do, Korea, Republic of
Duration: Jul 12 2021Jul 14 2021

Publication series

Name2021 18th International Conference on Ubiquitous Robots, UR 2021

Conference

Conference18th International Conference on Ubiquitous Robots, UR 2021
Country/TerritoryKorea, Republic of
CityGangneung-si, Gangwon-do
Period7/12/217/14/21

ASJC Scopus subject areas

  • Biomedical Engineering
  • Mechanical Engineering
  • Control and Optimization
  • Artificial Intelligence

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