自主水下航行器挑战:设计和建造一个中型的、人工智能支持的低成本原型

Dimitrios Paraschos, N. Papadakis
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引用次数: 1

摘要

介绍了一种物理尺寸为1100 mm × 700 mm × 330 mm、重量为55 kg的自主水下航行器(AUV)的设计。本文介绍了名为Synoris的AUV原型机的设计、材料、流体力学和系统架构,该原型机是一种低成本、中等规模的测试平台。Synoris通过6个无刷电机移动,深度可达200米,自主性约为6小时,采用模块化设计,可用于多种有效载荷选择。稳定性控制、自主运动、避障温度/压力传感和视频/图像捕获通过利用一组车载计算机同时执行,这些计算机将在第4节中简要介绍。整个平台建立在名为ROS(机器人操作系统)的开源软件之上,ROS通过提供底层设备控制、消息解析、数据融合和系统集成等服务,为编写机器人软件提供了灵活的框架。Synoris是水下应用和任务的理想选择,涉及机器学习和计算机视觉功能。由于操作环境的复杂性和严酷性,水下航行器的开发通常会遇到高成本的解决方案。即使是最具成本效益的解决方案也需要大量的资源。本文描述了整个开发过程,以及相对低成本的方法如何为涉及人工智能和机器学习功能的许多水下应用提供可靠的AUV。
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Autonomous underwater vehicle challenge: design and construction of a medium-sized, AI-enabled low-cost prototype
The design of an autonomous underwater vehicle (AUV) with physical dimensions of 1100 mm × 700 mm × 330 mm, and weight of 55 kg, is introduced herein. This paper describes the design, materials, hydrodynamics, and system architecture of an AUV prototype named Synoris, developed as a low-cost and medium-scale testbed platform. Synoris moves via six brushless motors, can reach up to 200 m depth, has an autonomy estimated around 6 hours and a modular design for multiple payload options. Stability control, autonomous movement, obstacle avoidance temperature/pressure sensing, and video/image capturing are simultaneously performed by exploiting a set of onboard computers that are described briefly in Section 4. The whole platform is built on top of the open source software called ROS (robotic operating system) that provides a flexible framework for writing robot software by providing services such as low-level device control, message parsing, data fusion, and system integration. Synoris is ideal for underwater applications and missions, involving machine learning and computer vision features. AUV development in general meets high-cost solutions due to the complexity and harshness of the operational environment. Even the most cost-effective solutions demand plentiful resources. This paper describes the entire process of development and how a relatively low-cost approach can provide a reliable AUV for many underwater applications, involving AI and machine-learning capabilities.
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来源期刊
CiteScore
2.80
自引率
12.50%
发文量
40
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