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 letter investigates safe navigation in unknown environments, using on-board 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 prior unknown environments.

中文翻译：

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通过记忆学习障碍功能，实现稳固的安全导航

控制屏障功能被广泛用于在机器人运动计划和控制中强制执行安全属性。但是，在线构造屏障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。这封信调查了在未知环境中的安全导航，使用机载范围感应在线构建了控制屏障功能。为了表示环境中的不同对象，我们使用距离测量值以重播记忆逐步训练有符号距离函数的神经网络近似值。这使我们能够制定出一种新颖的鲁棒控制屏障安全约束，其中考虑了估计距离场中的误差及其坡度。我们的公式导致了一个二阶锥程序，