Incorporating Observability via Control Barrier Functions with Application to Range-based Target Tracking
Incorporating Observability via Control Barrier Functions with Application to Range-based Target Tracking
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DOI：
10.1109/aim46487.2021.9517467
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
Demetris Coleman;S. Bopardikar;Xiaobo Tan
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文献类型：
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作者：
Demetris Coleman;S. Bopardikar;Xiaobo Tan
在非线性系统中，控制输入往往直接影响系统的可观测性。在本文中，我们研究了使用控制障碍函数（CBFs），以加强在目标跟踪的移动的机器人的可观测性，当只有到目标的距离是测量。这个问题是出于实际应用的自主机器人在GPS拒绝的环境中运行时。为了解决定位精度和跟踪性能之间的权衡，跟踪控制器被基于可观测性度量的控制障碍函数增强。两个例子被用来显示该方法的有效性，一个是在一个平面上的unicandroid动态，另一个是基于滑翔机器鱼复杂的3D动态。在这项工作中所采取的方法相比，模型预测控制器，优化联合成本函数的跟踪误差和可观测性度量。虽然这两种方法被证明保持可观测性和使跟踪，CBF为基础的方法被证明有几个优点
In nonlinear systems, the control input often directly impacts observability of the system. In this paper, we investigate the use of control barrier functions (CBFs) for enforcing observability of a mobile robot in target tracking, when only the distance to the target is measured. The problem is motivated by practical applications for autonomous robots when operating in GPS-denied environments. To address the tradeoffs between localization accuracy and tracking performance, a tracking controller is augmented by a control barrier function based on an observability metric. Two examples are used to show the efficacy of the approach, one with unicycle dynamics on a plane, and the other based on gliding robotic fish with complex 3D dynamics. The approach taken in this work is compared to a model predictive controller that optimizes a joint cost function of tracking error and observability metric. While both approaches are shown to maintain observability and enable tracking, the CBF-based approach is shown to have several advantages