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A Real-Time Game Theoretic Planner for Autonomous Two-Player Drone Racing
Robotics · Submitted 8 Jan 2018
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We demonstrate that our solution effectively competes against alternative strategies in a large number of drone racing simulations.
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The abstract, sentence by sentence
To be successful in multi-player drone racing, a player must not only follow the race track in an optimal way, but also compete with other drones through strategic blocking, faking, and opportunistic passing while avoiding collisions. Since unveiling one's own strategy to the adversaries is not desirable, this requires each player to independently predict the other players' future actions. Nash equilibria are a powerful tool to model this and similar multi-agent coordination problems in which the absence of communication impedes full coordination between the agents. In this paper, we propose a novel receding horizon planning algorithm that, exploiting sensitivity analysis within an iterated best response computational scheme, can approximate Nash equilibria in real time. We also describe a vision-based pipeline that allows each player to estimate its opponent's relative position. We demonstrate that our solution effectively competes against alternative strategies in a large number of drone racing simulations. Hardware experiments with onboard vision sensing prove the practicality of our strategy.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | To be successful in multi-player drone racing, a player must not only follow the race track in an optimal way, but also compete with other drones through strategic blocking, faking, and opportunistic passing while avoiding collisions. | background 1.00 | 0.06 | 0.03 | asserted 1.00 |
| s1 | Since unveiling one's own strategy to the adversaries is not desirable, this requires each player to independently predict the other players' future actions. | background 0.99 | 0.07 | 0.03 | asserted 0.99 |
| s2 | Nash equilibria are a powerful tool to model this and similar multi-agent coordination problems in which the absence of communication impedes full coordination between the agents. | background 0.99 | 0.06 | 0.04 | asserted 1.00 |
| s3 | In this paper, we propose a novel receding horizon planning algorithm that, exploiting sensitivity analysis within an iterated best response computational scheme, can approximate Nash equilibria in real time. | contribution 0.62 | 0.95 | 0.34 | asserted 0.96 |
| s4 | We also describe a vision-based pipeline that allows each player to estimate its opponent's relative position. | method 0.85 | 0.94 | 0.04 | asserted 0.99 |
| s5 | We demonstrate that our solution effectively competes against alternative strategies in a large number of drone racing simulations. | result 0.99 | 0.96 | 0.43 | asserted 1.00 |
| s6 | Hardware experiments with onboard vision sensing prove the practicality of our strategy. | result 0.95 | 0.96 | 0.22 | asserted 1.00 |
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The paper
Original abstract
To be successful in multi-player drone racing, a player must not only follow the race track in an optimal way, but also compete with other drones through strategic blocking, faking, and opportunistic passing while avoiding collisions. Since unveiling one's own strategy to the adversaries is not desirable, this requires each player to independently predict the other players' future actions. Nash equilibria are a powerful tool to model this and similar multi-agent coordination problems in which the absence of communication impedes full coordination between the agents. In this paper, we propose a novel receding horizon planning algorithm that, exploiting sensitivity analysis within an iterated best response computational scheme, can approximate Nash equilibria in real time. We also describe a vision-based pipeline that allows each player to estimate its opponent's relative position. We demonstrate that our solution effectively competes against alternative strategies in a large number of drone racing simulations. Hardware experiments with onboard vision sensing prove the practicality of our strategy.
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How the literature uses it
6 arXiv papers cite this in 12 sentences
Papers citing it · 8 sentences without a confident label
Cites it as background 2 papers
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Nash equilibria have been investigated in [ref] .
ALGAMES: A Fast Solver for Constrained Dynamic Games · Equilibrium Selection · 0.96
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For example, in autonomous driving [ref] , power system control [ref] , drone and car racing [ref] etc.
LUCIDGames: Online Unscented Inverse Dynamic Games for Adaptive Trajectory Prediction and Planning · Game-Theoretic Trajectory Optimization · 0.93
What it cites
Compares with 1 paper
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However, while in our work these behaviors naturally emerge from the use of sensitivity analysis, in [ref] these are hardcoded in the cost function optimized by the players.
A Non-Cooperative Game Approach to Autonomous Racing · Game theoretic control using a Nash information pattern · 1.00
Cites as background 1 paper
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In the context of car racing, the authors of [ref] investigate both Stackelberg and Nash equilibria.
A Non-Cooperative Game Approach to Autonomous Racing · Game theoretic control using a Nash information pattern · 0.85
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