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arXiv cs.AI· Minh Bui, Simon Monckton, Mo Chen·· 13 小时前AI 评分18

基于分解方法的无人机 Reach-Avoid 微分博弈与可达性分析

Reach-Avoid Differential game with Reachability Analysis for UAVs: A decomposition approach

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研究提出一种降维框架,将三维空间中的 Reach-Avoid 博弈分解为水平与垂直两个子博弈,分别用 Hamilton-Jacobi 可达性分析求解,并考虑防御者加速度的二阶动力学。作者为每个子博弈引入基于 HJ 的跟踪控制算法,证明捕获保证得以维持,并在 Gazebo 物理模拟器中首次实现四旋翼飞行器的三维成功捕获。

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Abstract:Reach-avoid (RA) games have significant applications in security and defense, particularly for unmanned aerial vehicles (UAVs). These problems are inherently challenging due to the need to consider obstacles, consider the adversarial nature of opponents, ensure optimality, and account for nonlinear dynamics. Hamilton-Jacobi (HJ) reachability analysis has emerged as a powerful tool for tackling these challenges; however, while it has been applied to games involving two spatial dimensions, directly extending this approach to three spatial dimensions is impossible due to high dimensionality. On the other hand, alternative approaches for solving RA games lack the generality to consider games with three spatial dimensions involving agents with non-trivial system dynamics. In this work, we propose a novel framework for dimensionality reduction by decomposing the problem into a horizontal RA sub-game and a vertical RA sub-game. We then solve each sub-game using HJ reachability analysis and consider second-order dynamics that account for the defender's acceleration. To reconstruct the solution to the original RA game from the sub-games, we introduce a HJ-based tracking control algorithm in each sub-game that not only guarantees capture of the attacker but also tracking of the attacker thereafter. We prove the conditions under which the capture guarantees are maintained. The effectiveness of our approach is demonstrated via numerical simulations, showing that the decomposition maintains optimality and guarantees in the original problem. Our methods are also validated in a Gazebo physics simulator, achieving successful capture of quadrotors in three spatial dimensions space for the first time to the best of our knowledge.
Comments: Paper version 1 accepted to the Journal of Guidance, Control, and Dynamics (JGCD). Version 2 strengthens the horizontal invariance and updates figures in the experiment
Subjects: Systems and Control (eess.SY); Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT)
Cite as: arXiv:2512.22793 [eess.SY]
  (or arXiv:2512.22793v2 [eess.SY] for this version)
  https://doi.org/10.48550/arXiv.2512.22793

arXiv-issued DOI via DataCite

Related DOI: https://doi.org/10.2514/1.G009391

DOI(s) linking to related resources

Submission history

From: Minh Bui [view email]
[v1] Sun, 28 Dec 2025 05:34:11 UTC (879 KB)
[v2] Mon, 26 Oct 2026 18:25:22 UTC (1,213 KB)

来源:arXiv cs.AI · arxiv.org