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An integrated multi-sensor data fusion algorithm and autopilot implementation in an uninhabited surface craft

机译：无人水面航行器中集成的多传感器数据融合算法和自动驾驶仪实现

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Unmanned surface vehicles (USVs) are able to accomplish difficult and challenging tasks both in civilian and defence sectors without endangering human lives. Their ability to work round the clock makes them well-suited for matters that demand immediate attention. These issues include but not limited to mines countermeasures, measuring the extent of an oil spill and locating the source of a chemical discharge. A number of USV programmes have emerged in the last decade for a variety of aforementioned purposes. Springer USV is one such research project highlighted in this paper. The intention herein is to report results emanating from data acquired from experiments on the Springer vessel whilst testing its advanced navigation, guidance and control (NGC) subsystems. The algorithms developed for these systems are based on soft-computing methodologies. A novel form of data fusion navigation algorithm has been developed and integrated with a modified optimal controller. Experimental results are presented and analysed for various scenarios including single and multiple waypoints tracking and fixed and time-varying reference bearings. It is demonstrated that the proposed NGC system provides promising results despite the presence of modelling uncertainty and external disturbances.
机译：无人水面车辆（USV）能够在民用和国防领域完成艰巨而富挑战性的任务，而不会危及人类生命。他们全天候工作的能力使他们非常适合需要立即关注的事情。这些问题包括但不限于地雷对策，衡量漏油的程度以及确定化学排放源的位置。在过去的十年中，出于各种上述目的，出现了许多USV计划。 Springer USV是本文重点介绍的此类研究项目之一。本文的目的是报告在测试其先进的导航，制导和控制（NGC）子系统时，从施普林格船上的实验获得的数据中得出的结果。为这些系统开发的算法基于软计算方法。已经开发出一种新颖形式的数据融合导航算法，并将其与改进的最优控制器集成在一起。给出并分析了各种情况下的实验结果，包括单点和多点跟踪以及固定和时变参考方位。结果表明，尽管存在建模不确定性和外部干扰，但提出的NGC系统仍可提供令人鼓舞的结果。

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