Nonlinear receding-horizon differential game for drone racing along a three-dimensional path
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866917910055747584 |
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| author | Sung, Kijin Hoshino, Kenta Honda, Akihiko Shima, Takeya Ohtsuka, Toshiyuki |
| author_facet | Sung, Kijin Hoshino, Kenta Honda, Akihiko Shima, Takeya Ohtsuka, Toshiyuki |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_01044 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Nonlinear receding-horizon differential game for drone racing along a three-dimensional path Sung, Kijin Hoshino, Kenta Honda, Akihiko Shima, Takeya Ohtsuka, Toshiyuki Systems and Control 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. |
| title | Nonlinear receding-horizon differential game for drone racing along a three-dimensional path |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2502.01044 |