用对抗性时间到碰撞评估风险，让无人机群更安全地近距离飞行
A Temporal Barrier Framework for Collision Avoidance in Multi-Agent Autonomous Aerial Vehicles
- 基于对抗意图计算时间到碰撞，直接以时间而非距离设安全屏障
- 在3D追逐与编队仿真中，航点达成率提升一倍，碰撞率减半
- 适合需要高动态避障的多机无人机系统，尤其对抗环境
在动态、不确定且可能具有对抗性的环境中运行自主飞行器团队，需要既可靠又具选择性的安全协议，使飞行器能在靠近彼此的同时推进任务目标。本文提出对抗性时间到碰撞（aTTC），一种风险度量指标，用于量化给定飞行器在假设对手具有对抗意图时，周围飞行器到达其位置所需的时间。将aTTC嵌入控制屏障函数（CBF）框架中，直接以时间而非距离或速度定义屏障。由此产生的aTTC-CBF具有内在前瞻性：飞行器根据对手在动力学约束下达到碰撞所需时间，而非是否处于碰撞路径上，调节自身速度。通过可微神经网络近似，aTTC可在标准CBF二次规划中实时计算。在长时间3D独立追逐与编队飞行模拟中，aTTC-CBF相较更高阶的距离基基线，航点进度提升最多达两倍，碰撞率降低一半。
原文摘要 · Abstract (English)
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.
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