We present a dual-barrier control barrier function (CBF) safety filter for real-time, safety-critical velocity control of holonomic robots operating in incrementally built occupancy grid maps. As a robot explores an unknown environment, unmapped regions introduce irreducible uncertainty, since obstacle geometry beyond the explored frontier is unknown, making entry into such regions a source of collision risk, especially with front-facing sensors. To address this, we enforce two constraints: avoidance of mapped obstacles and restriction from unexplored regions. Both constraints are derived analytically from the occupancy grid's signed distance field, yielding a closed-form safety filter that requires only a small linear system solve per cycle. On resource-constrained platforms such as the Raspberry Pi, where SLAM and planning already consume significant compute, the low overhead of the proposed filter preserves resources. An adaptive gain schedule relaxes the frontier constraint in information-rich regions and tightens it in well-mapped areas, improving exploration efficiency while maintaining safety. The filter operates in velocity space as a minimally invasive correction and composes with arbitrary nominal controllers, including learning-based methods. Hardware flight experiments on a PX4-controlled quadrotor demonstrate zero collisions across multiple indoor runs.

翻译：我们提出了一种双障碍控制障碍函数（CBF）安全滤波器，用于在增量构建的占据栅格地图中运行的全向机器人进行实时、安全关键的速度控制。当机器人探索未知环境时，未映射区域会引入不可约的不确定性，因为超出已探索前沿的障碍物几何形状是未知的，这使得进入此类区域成为碰撞风险的来源，尤其是对于前向传感器而言。为解决这一问题，我们强制执行两个约束：避开已映射障碍物以及限制进入未探索区域。这两个约束均基于占据栅格的符号距离场解析推导得出，从而得到一个闭式安全滤波器，该滤波器每个周期仅需求解一个小型线性系统。在资源受限的平台（如树莓派）上，当同时定位与建图（SLAM）和规划已消耗大量计算资源时，所提滤波器的低开销特性能够节省资源。一种自适应增益调度机制在信息丰富区域放松前沿约束，并在良好映射区域收紧约束，从而在保持安全性的同时提高探索效率。该滤波器在速度空间中作为最小侵入性修正运行，并可兼容包括基于学习方法在内的任意名义控制器。在PX4控制的四旋翼飞行器上进行的硬件飞行实验表明，在多次室内运行中实现了零碰撞。