A Real-Time Game Theoretic Planner for Autonomous Two-Player Drone Racing

In 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...

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Vydáno v:IEEE transactions on robotics Ročník 36; číslo 5; s. 1389 - 1403
Hlavní autoři: Spica, Riccardo, Cristofalo, Eric, Wang, Zijian, Montijano, Eduardo, Schwager, Mac
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.10.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Aerial robotics
Algorithms
Collision avoidance
Computer simulation
drone racing
Drones
Game theory
Games
Iterative methods
motion and path planning
Nash equilibrium
path planning for multiple mobile robot systems
Prediction algorithms
Predictive control
Racetracks
Racing
Robots
Sensitivity
Sensitivity enhancement
Trajectory
vision-based pose estimation
ISSN:1552-3098, 1941-0468
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Shrnutí:In 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 a model 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 <inline-formula><tex-math notation="LaTeX">{1.25\,}\mathrm{{m}}/\mathrm{{s}}</tex-math></inline-formula>.
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ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2020.2994881