Drone racing involves high-speed navigation of three-dimensional paths, posing a substantial challenge in control engineering. This study presents a game-theoretic control framework, the nonlinear receding-horizon differential game (NRHDG), designed for competitive drone racing. NRHDG enhances robustness in adversarial settings by predicting and countering an opponent's worst-case behavior in real time. It extends standard nonlinear model predictive control (NMPC), which otherwise assumes a fixed opponent model. First, we develop a novel path-following formulation based on projection point dynamics, eliminating the need for costly distance minimization. Second, we propose a potential function that allows each drone to switch between overtaking and obstructing maneuvers based on real-time race situations. Third, we establish a new performance metric to evaluate NRHDG with NMPC under race scenarios. Simulation results demonstrate that NRHDG outperforms NMPC in terms of both overtaking efficiency and obstructing capabilities.
@article{arxiv.2502.01044,
title = {Nonlinear receding-horizon differential game for drone racing along a three-dimensional path},
author = {Kijin Sung and Kenta Hoshino and Akihiko Honda and Takeya Shima and Toshiyuki Ohtsuka},
journal= {arXiv preprint arXiv:2502.01044},
year = {2025}
}