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
该研究针对多自主飞行器避障问题，提出对抗性碰撞时间嵌入控制屏障函数的aTTC-CBF方法，经仿真验证其在提升航路点推进速度的同时降低了碰撞率。
AI 中文摘要
在动态、不确定且可能存在对抗性的环境中操作自主飞行器团队，需要可靠且具有选择性的安全协议，使智能体能够近距离飞行同时向任务目标推进。我们提出对抗性碰撞时间（aTTC）这一风险度量，它针对给定智能体，在假设对抗性意图的前提下，量化周围任意智能体到达该智能体的速度。我们将aTTC嵌入控制屏障函数（CBF）框架，直接在时间而非距离或速度上定义屏障。所得的aTTC-CBF具有内在的预见性：智能体调整自身速度的依据不是同伴是否处于碰撞航线，而是考虑自身动力学约束下发生碰撞的时间快慢。可微分神经网络代理使aTTC能在标准CBF二次规划中实时计算。在3D独立追踪和编队飞行场景的长时程仿真中，aTTC-CBF的航路点推进速度达到基于高阶距离的CBF基线的两倍，碰撞率仅为其一半。
英文摘要
Operating teams of autonomous aircraft in dynamic, uncertain, and potentially adversarial environments requires safety protocols that are reliable yet selective, and allow agents to fly in close proximity while making progress toward mission objectives. We introduce adversarial time-to-collision (aTTC), a risk metric that quantifies, for a given agent, how quickly any surrounding agent could reach it assuming adversarial intent. We embed aTTC into the control barrier function (CBF) framework, defining the barrier directly in time rather than distance or velocity. The resulting aTTC-CBF is inherently anticipatory: agents modulate their own velocity based not on whether a peer is on a collision course, but on how quickly one could reach collision given its dynamical constraints. A differentiable neural-network surrogate makes the aTTC computable in real time within a standard CBF quadratic program. Across long time-horizon simulations of 3D independent-pursuit and formation-flight scenarios, the aTTC-CBF achieves up to twice the waypoint progress at half the collision rate of a higher-order distance-based CBF baseline.
