发表机构
Massachusetts Institute of Technology; MIT Lincoln Laboratory（麻省理工学院; 麻省理工学院林肯实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出基于对抗性时间到碰撞的控制障碍函数，通过神经网络代理实现实时二次规划控制，在2D和3D追逃中优于距离基线，有效对抗速度优势追击者。
AI 中文摘要
自主系统的碰撞避免约束通常在位置或速度空间中表述，隐式地对几何接近度做出反应。我们提出了一种基于对抗性时间到碰撞（aTTC）的替代范式：即考虑到对手的动力学约束，对手能够实现碰撞的最短时间。通过直接在时域中定义控制障碍函数（CBF），所得控制器固有地具有前瞻性。逃避代理不仅对追击者是否处于碰撞航线上做出响应，还对其可能达到碰撞的速度做出响应。这种表述使得能够利用追击者的动力学限制作为逃避策略进行速度调节，这是标准基于距离的CBF所不能捕获的行为。由于精确的aTTC计算需要整合完整的系统动力学，我们采用了一个轻量级神经网络代理，该代理支持基于实时二次规划的控制器。我们在2D对比研究和3D多智能体追逃场景中验证了该方法，其中基于aTTC的CBF通过更有效地利用时间对抗具有显著速度优势的优越追击者，优于高阶基于距离的基线方法。
英文摘要
Collision avoidance constraints for autonomous systems are typically formulated in position or velocity space, implicitly reacting to geometric proximity. We propose an alternative paradigm based on the adversarial time-to-collision (aTTC): the minimum time in which an adversary could achieve a collision given its dynamical constraints. By defining a control barrier function (CBF) directly in the time domain, the resulting controller is inherently anticipatory. The evading agent responds not only to whether a pursuer is on a collision course, but to how quickly it could reach one. This formulation enables velocity modulation that exploits the pursuers dynamic limits as an evasive strategy, a behavior not captured by standard distance-based CBFs. Since exact aTTC computation requires integrating the full system dynamics, we employ a lightweight neural network surrogate that admits a real-time quadratic program-based control law. We validate the approach in a 2D comparative study and a 3D multi-agent pursuit-evasion scenario, where the aTTC-based CBF outperforms a higher-order distance-based baseline by more effectively buying time against superior pursuers with a significant speed advantage.
CommentsAccepted for presentation at the 65th IEEE Conference on Decision and Control (CDC 2026), Honolulu, Hawaii, USA
