Learning Barrier Functions With Memory for Robust Safe Navigation
Learning Barrier Functions With Memory for Robust Safe Navigation
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DOI：
10.1109/lra.2021.3070250
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发表时间：
2020-11
影响因子：
5.2
通讯作者：
Kehan Long;Cheng Qian;J. Cortés;Nikolay A. Atanasov
中科院分区：
文献类型：
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作者：
Kehan Long;Cheng Qian;J. Cortés;Nikolay A. Atanasov
在机器人运动规划和控制中，控制障碍函数被广泛应用于增强安全性能。然而，在线构造障碍函数和合成能够处理相关不确定性的安全控制器的问题却很少受到关注。这封信研究了未知环境中的安全导航，使用车载距离传感来构建在线控制屏障功能。为了表示环境中的不同物体，我们使用距离测量值来训练带有重放记忆的带符号距离函数的神经网络逼近。这使我们能够制定一种新的鲁棒控制屏障安全约束，该约束考虑了估计距离场及其梯度的误差。我们的公式导致二阶锥体程序，使在先前未知环境下的控制合成安全稳定。
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.