Real time path-planning of an AUV based on characteristics of passive acoustic landmarks for visual mapping of shallow vent fields

T. Maki, Hayato Mizushimat, H. Kondo, T. Ura, T. Sakamaki, Masao Yanagisawat
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引用次数: 27

Abstract

Although underwater vent fields are of great scientific interest, accurate visual mapping is difficult because of the presence of bubble plumes that degrade the accuracy of conventional acoustic positioning systems such as long base line (LBL) and super short base line (SSBL). The authors had proposed a visual mapping method of shallow vent fields with an autonomous underwater vehicle (AUV) equipped with a profiling sonar, where positioning is based on vertical rod-shaped acoustic reflectors and bubble plumes. Although performance was verified through a series of experiments, there remain two challenges as follows. Firstly, observation is terminated if the vehicle was surrounded by bubble plumes, since the vehicle tries to avoid collision with not only artificial reflectors but also collision-safe bubble plumes. Secondly, the observation area drifts by disturbance since the waypoints are defined relative to the vehicle's position after descending close to seafloor. This paper proposes a real-time path planning method of an AUV as a part of the proposed observation method. The path of the vehicle is defined based on the types of landmarks as well as the geometric relationship between the vehicle and the landmarks. The vehicle can distinguish landmark types using a sheet laser and a camera. The proposed method was implemented on the AUV Tri-Dog 1 and a series of experiments were carried out in order to verify its performance.
基于被动声地标特征的水下航行器实时路径规划,用于浅层喷口场视觉映射
尽管水下喷口场具有重大的科学意义,但由于气泡羽流的存在,降低了传统声波定位系统(如长基线(LBL)和超短基线(SSBL))的精度,因此很难进行精确的视觉定位。作者提出了一种利用配备剖面声纳的自主水下航行器(AUV)对浅喷口场进行可视化测绘的方法,该方法基于垂直杆状声反射器和气泡羽流进行定位。虽然通过一系列实验验证了性能,但仍然存在以下两个挑战。首先,如果车辆被气泡羽流包围,则观测终止,因为车辆不仅要避免与人工反射器碰撞,还要避免与碰撞安全气泡羽流碰撞。其次,由于航路点是在接近海底下降后相对于车辆位置定义的,因此观测区域受到干扰而漂移。本文提出了一种水下航行器实时路径规划方法,作为该观测方法的一部分。根据地标的类型以及车辆与地标之间的几何关系来定义车辆的路径。该车辆可以使用激光片和摄像头来区分地标类型。在水下机器人Tri-Dog 1上实施了该方法,并进行了一系列实验验证其性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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