论文标题
学习障碍功能具有记忆力可靠的安全导航
Learning Barrier Functions with Memory for Robust Safe Navigation
论文作者
论文摘要
控制屏障功能被广泛用于在机器人运动计划和控制中执行安全性。但是，在网上构建障碍功能和合成可以处理相关不确定性的安全控制器的问题很少受到关注。本文使用车载范围传感在线构建控制屏障功能在线构建控制障碍功能。为了表示环境中的不同对象，我们使用距离测量值来通过重播内存逐步训练签名距离函数的神经网络近似。这使我们能够制定一种新型的鲁棒控制屏障安全约束，该限制考虑了估计距离场及其梯度中的误差。我们的配方导致了二阶锥体程序，从而在先验未知的环境中实现了安全稳定的控制合成。
Control barrier functions are widely used to enforce safety properties in robot motion planning and control. However, the problem of constructing barrier functions online and synthesizing safe controllers that can deal with the associated uncertainty has received little attention. This paper investigates safe navigation in unknown environments, using onboard range sensing to construct control barrier functions online. To represent different objects in the environment, we use the distance measurements to train neural network approximations of the signed distance functions incrementally with replay memory. This allows us to formulate a novel robust control barrier safety constraint which takes into account the error in the estimated distance fields and its gradient. Our formulation leads to a second-order cone program, enabling safe and stable control synthesis in a priori unknown environments.
