Sensor driven online coverage planning for autonomous underwater vehicles
Sensor driven online coverage planning for autonomous underwater vehicles
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DOI：
10.1109/iros.2012.6385838
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发表时间：
2012-12
期刊：
影响因子：
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通讯作者：
L. Paull;Sajad Saeedi;M. Seto;Howard Li
中科院分区：
文献类型：
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作者：
L. Paull;Sajad Saeedi;M. Seto;Howard Li
目前，自主水下航行器（AUV）水雷对抗（MCM）调查是由操作员使用梯子或之字形路径预先规划的。此类调查通常使用侧视声纳传感器进行，其性能取决于许多环境因素以及 AUV 轨道的横向范围。这项研究提出了一种传感器驱动的 MCM 海底覆盖在线方法。提出了一种使用多目标优化自适应地规划路径的方法。信息论与基于六角形单元分解的新概念分支熵相结合。其结果是规划算法通常会产生比传统方法更短的路径，并且还能够考虑现场检测到的环境因素。在 IVER2 AUV 上进行的硬件在环仿真和水中试验表明了该方法的有效性。
At present, autonomous underwater vehicle (AUV) mine countermeasure (MCM) surveys are pre-planned by operators using ladder or zig-zag paths. Such surveys are often conducted with side-looking sonar sensors whose performance is dependant on a number of environment factors, as well as lateral range from the AUV track. This research presents a sensor driven online approach to seabed coverage for MCM. A method is presented where paths are planned adaptively using a multi-objective optimization. Information theory is combined with a new concept coined branch entropy based on a hexagonal cell decomposition. The result is a planning algorithm that often produces shorter paths than conventional means and is also capable of accounting for environmental factors detected in situ. Hardware-in-the-loop simulations and in water trials conducted on the IVER2 AUV show the effectiveness of the proposed method.