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A bayesian approach to supervisory risk control of auvs applied to under-ice operations

机译：贝叶斯探险风险控制适用于冰欠冰行动的方法

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摘要

Autonomous underwater vehicles (AUVs) are efficient sensor-carrying platforms for mapping and monitoring undersea ice. However, under-ice operations impose demanding requirements to the system, as it must deal with uncertain and unstructured environments, harsh environmental conditions, and reduced capabilities of the navigational sensors. This paper proposes a Bayesian approach to supervisory risk control, with the objective of providing risk management capabilities to the control system. First, an altitude guidance law for following a contour of an ice surface via pitch control using measurements from a Doppler velocity log (DVL) is proposed. Furthermore, a Bayesian network (BN) for probabilistic reasoning over the current state of risk during the operation is developed. This is then extended to a decision network (DN) for autonomously adapting the behavior of the AUV in order to maximize the mission utility, subject to a constraint on the predicted risk from the risk model. The vehicle is thus able to autonomously adapt its behavior in response to its current belief about the risk. The goal of this work is to improve the AUV performance and likelihood of mission success. Results from a simulation study are presented in order to demonstrate the performance of the proposed method.
机译：自动水下车辆（AUV）是用于映射和监测海底冰的有效传感器的平台。然而，ICE欠冰行动对系统施加了要求要求的要求，因为它必须处理不确定和非结构化的环境，恶劣的环境条件和导航传感器的减少能力。本文提出了一种贝叶斯方面的监督风险控制方法，目的是向控制系统提供风险管理能力。首先，提出了通过使用来自多普勒速度日志（DVL）的测量值，通过俯仰控制在冰表面轮廓上进行的高度指导法。此外，开发了一种贝叶斯网络（BN），用于在操作期间对当前风险状态的概率推理。然后将其扩展到决策网络（DN），用于自主地调整AUV的行为，以最大化任务实用程序，但受到风险模型的预测风险的约束。因此，车辆能够自主地适应其行为，以响应其当前对风险的信念。这项工作的目标是提高AUV表现和使命成功的可能性。提出了仿真研究的结果，以证明该方法的性能。

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  • 来源
    《Oceanographic Literature Review》 |2020年第10期|2312-2312|共1页
  • 作者

    J.E. Bremnes; C.A. Thieme; A.J. Sørensen;

  • 作者单位

    Department of Marine Technology Centre for Autonomous Marine Systems and Operations (AMOS) Norwegian University of Science and Technology (NTNU) Norway;

    Department of Marine Technology Centre for Autonomous Marine Systems and Operations (AMOS) Norwegian University of Science and Technology (NTNU) Norway;

    Department of Marine Technology Centre for Autonomous Marine Systems and Operations (AMOS) Norwegian University of Science and Technology (NTNU) Norway;

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