Advancing Autonomous Underwater Vehicle Capability for Assessment of Marine Pollution

提高自主水下航行器评估海洋污染的能力

• 批准号: RGPIN-2021-02506
• 负责人: Bose, Neil
• 金额: $ 1.97万
• 依托单位: Memorial University of Newfoundland
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=741806
• 关键词: Advancing; Autonomous; Underwater; Vehicle; Capability

Marine pollution is a global environmental problem that has severe adverse environmental and socio-economic impacts. Oil spills are one form of pollution; further chemical run-off and plastics in the ocean are others. Climate change and severe degradation of our ecosystem are some of the outcomes from this pollution. Increased risks, the Deepwater Horizon blowout in the Gulf of Mexico, the MV Wakashio in Mauritius, spills off Newfoundland in 2018 and 2019, have broadened the gap between the potential need for oil spill countermeasures and current response capabilities. Information about oil and other pollution in the water column is critical to understanding of subsurface plume behavior which in turn enables timely and effective mitigation. Following the Gulf of Mexico blowout, the autonomous underwater vehicle (AUV) Sentry was successfully used to characterize submerged oil that was trapped at a depth of approximately 1200 m and provided a comprehensive analysis of the impacted areas. In this research AUV capabilities will be advanced specifically for the understanding of marine pollution and response: Firstly, an innovative adaptive mission planning approach for discontinuous patchy plumes made up of non-soluble oil droplet clouds or other potential items of interest such as micro-plastics, will be researched to improve performance of AUVs and their onboard sensors in delineating subsurface plumes. Using adaptive sampling, the AUV autonomously modifies its mission path in real-time based on features of the plumes detected by on-board sensors. Hence the AUV path is concentrated within the areas of interest and in information-rich areas identified by the sensors, optimizing the AUV response. Secondly, long-range AUV capability will be researched in conjunction with Canadian AUV manufacturers to enable extensive high-endurance marine pollution monitoring tasks to be completed with the aim to put in place watchdog and alerts that lead to mitigation measures. Renewable-energy battery charging at the water surface coupled with autonomous navigation and collision avoidance systems, similarly to those being developed for autonomous shipping, will be incorporated to enable these new designs of AUVs to operate safely over long periods in the ocean surface environment.  Thirdly, risk-based analysis of these remote long range operations required for the monitoring of marine pollution will be researched and new methods will be developed to ensure that the risks of AUV deployment to other ocean users, such as people, ships and boats, as well as to the environment through underwater noise and the potential for pollution, are minimized. This is an essential component as an increasing number of autonomous vehicles populates the ocean surface.  The target community of this research includes regulators, marine pollution agencies, oil spill response organizations, the offshore petroleum industry, AUV manufacturers, operators and oceanographers.

海洋污染是一个全球性环境问题，会造成严重的环境和社会经济影响；石油泄漏是污染的一种形式；海洋中的化学物质流失和塑料污染也是其中之一。 2018 年和 2019 年墨西哥湾深水地平线井喷、毛里求斯 MV Wakashio 号、纽芬兰岛漏油事件等风险增加，扩大了潜在漏油需求之间的差距。有关水体中的石油和其他污染的信息对于了解地下羽流行为至关重要，从而能够在墨西哥湾井喷之后及时有效地进行缓解。用于表征大约 1200 m 深度的水下石油，并对受影响区域进行全面分析。在这项研究中，AUV 的能力将专门用于了解海洋污染和响应：首先，创新的自适应任务规划。的方法将研究由不溶性油滴云或其他潜在感兴趣的物质（例如微塑料）组成的不连续的片状羽流，以提高 AUV 及其机载传感器在描绘地下羽流方面的性能，AUV 使用自适应采样自动修改其自身。根据机载传感器检测到的羽流特征实时确定任务路径，因此 AUV 路径集中在感兴趣的区域和传感器识别的信息丰富的区域，从而优化了 AUV。其次，将与加拿大 AUV 制造商合作研究远程 AUV 能力，以完成广泛的高耐久性海洋污染监测任务，目的是实施监管和警报，从而采取可再生能源缓解措施。水面电池充电与自主导航和防撞系统相结合，类似于为自主航运开发的系统，使这些新设计的 AUV 能够在海洋表面环境中长期安全运行。基于这些分析将研究海洋污染监测所需的远程远距离操作，并开发新方法，以确保AUV部署对其他海洋用户（例如人员、船舶和船只）以及水下噪声对环境造成的风险随着越来越多的自动驾驶车辆出现在海洋表面，这是一个重要组成部分。这项研究的目标群体包括监管机构、海洋污染机构、溢油响应组织、海上石油工业、AUV。制造商、运营商和海洋学家。