发表机构
Lehigh University（理海大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对未知环境导航中探索与安全的矛盾，提出Splat-CBF，利用风险感知控制屏障函数和Fisher信息增益，在二次规划中实现硬安全约束与软感知目标，使机器人更快、更安全地收集信息。
AI 中文摘要
看哪里以及如何移动？在未映射环境中导航的机器人必须同时处理这两个问题，而这两个目标相互制约。最值得观察的区域是地图了解最少的区域，而这些区域恰恰是机器人无法信任其碰撞裕度的区域。我们通过引入Splat-CBF来解决这一矛盾，这是一种主动感知控制屏障函数，在将碰撞避免作为硬约束强制执行的同时，将相机引导至下一最佳视角。安全性由一种风险感知控制屏障函数强制执行，该函数将高斯场的平均风险价值转化为单一平滑的硬约束。感知由第二个屏障函数强制执行，该函数奖励在机器人规划路径附近具有高期望Fisher信息增益的相机朝向。两者在二次规划中相遇，其中安全性是硬性的，感知是软性的，松弛惩罚根据感知已被放松的频率以及机器人距离不确定区域的接近程度自适应调整。我们在室内仿真、Isaac Kinova机械臂以及阿克曼转向机器人的实验中验证了该方法。我们的结果表明，与仅考虑安全性和仅考虑感知的基线相比，机器人导航更快、收集更多信息、在线运行速度更快，仅在安全性需要时才放弃信息丰富的运动。
英文摘要
Where to look and how to move? A robot navigating an unmapped environment must do both at once, and the two goals pull against each other. The regions most worth observing are the ones the map knows least about, and those are exactly where the robot cannot trust its collision margins. We resolve this tension by introducing Splat-CBF, an active perception control barrier function that steers the camera toward the next best view while collision avoidance is enforced as a hard constraint. Safety is enforced by a risk-aware control barrier function that turns the Average Value-at-Risk of the Gaussian field into a single smooth hard constraint. Perception is enforced by a second barrier that rewards camera orientations with high expected Fisher information gain near the robot's planned path. The two meet in a quadratic program where safety is hard and perception is soft, with a slack penalty that adapts to how often perception has already been relaxed and how close the robot is to an uncertain region. We verify the method in indoor simulations, a Isaac Kinova manipulator and in experiments on an Ackermann-drive robot. Our results assert that robot navigates faster, gathers more information, and runs faster online than safety-only and perception-only baselines, giving up informative motion only when safety requires it.
