Probabilistic Inference Based Utility Evaluation and Path Generation for Active Autonomous Exploration of USVs in Unknown Confined Marine Environments

基于概率推理的效用评估和路径生成，用于未知受限海洋环境中 USV 主动自主探索

• 批准号: EP/Y000862/1
• 负责人: Yuanchang Liu
• 金额: $ 20.95万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY000862%2F1
• 关键词: Probabilistic; Inference; Based; Utility; Evaluation

Unmanned surface vessels (USVs) are water-borne vessels that are capable of operating on the surface of the water without any onboard human operators. USVs can operate in confined areas (ports, harbours, marinas, etc.) to conduct demanding and challenging missions such as port dredging survey, berth clearance monitoring and marine infrastructure maintenance, with significant benefits including reduced risk to personnel, improved spatial-temporal efficiency and increased operation endurance. However, when operating in confined marine environments, current USVs are usually remotely controlled. This is because in contrast to navigating in public waterways, confined marine environments are highly dynamic (the locations of docked/moored vessels in a port may be constantly changing) making static nautical charts or satellite and aerial imagery less useful for navigation. Such a factor makes the confined marine environment more highly unknown and associated with high levels of uncertainties. Autonomous exploration, as a process that can map an unknown confined environment in an automatic way, has therefore become critical to USV operation in unknown confined marine environments. Current state-of-the-art autonomous exploration strategy employed by USVs is to leverage the Simultaneous Localisation And Mapping (SLAM) technology to build a map of an environment using sensory data without any prior information. Since SLAM is a passive process, regular teleoperation with human operators guiding the map-building process is required for existing USV platforms, making the exploration not fully autonomous. To make the SLAM based autonomous exploration an active process, planning functionality including two modules, i.e., a utility evaluation module and a path generation/selection module, has to be integrated. However, current studies about utility evaluation and path generation cannot address the issues caused by the sparse landmarks in a marine environment, which will compromise the exploration accuracy and efficiency. This research therefore aims to develop a new active autonomous exploration framework using probabilistic inference based utility evaluation and path generation/selection. More specifically, we will construct a pseudo map which contains virtual landmarks as a proxy for an unknown confined marine environment with sparse real landmarks, and evaluate uncertainties as per marginal posterior distributions of poses and positions of virtual landmarks, respectively, using Bayesian probabilistic inference. We also propose to design a new Gaussian Process (GP) based path generation algorithm for autonomous exploration and solve the path generation problem as probabilistic inference on a factor graph. A cross-entropy optimisation method will be adapted to the path planning to enable efficient derivation of the GP mean and covariance updating rules by taking into account nonlinear constraints such as USVs' manoeuvrability. Of key importance for the success of this work is the international collaboration with a leading marine robotics expert, Prof. Brendan Englot, Stevens Institute of Technology, to jointly develop the framework. This work will also have a close collaboration with experienced industrial partners, including Port of London Authority (PLA) and BMT Group Ltd. By working closely with PLA and BMT, innovations generated from this research will be implemented on the Otter USV to conduct use-case demonstrations (e.g., hydrographic survey) on the Tidal Thames. And the long-term vision of this international collaboration is to establish a strong UK-US research consortium on future marine innovations in advanced sensors, AI/machine learning and robotics to work collaboratively with more academic institutions, companies and regulators/organisations.

无人水面船（usv）是一种能够在水面上作业的水上船只，没有任何船上的操作员。usv可以在狭窄的区域（港口，港口，码头等）执行苛刻和具有挑战性的任务，如港口疏浚调查，泊位间隙监测和海洋基础设施维护，具有显著的好处，包括降低人员风险，提高时空效率和增加操作耐久性。然而，当在狭窄的海洋环境中作业时，目前的无人潜航器通常是远程控制的。这是因为与在公共水道中航行相比，密闭的海洋环境是高度动态的（港口停靠/系泊船只的位置可能不断变化），使得静态海图或卫星和航空图像对导航的用处不大。这一因素使密闭的海洋环境更加不为人知，并与高度的不确定性联系在一起。因此，自主勘探作为一种可以自动绘制未知密闭环境的过程，对于无人潜航器在未知密闭海洋环境中的作业至关重要。usv目前采用的最先进的自主勘探策略是利用同步定位和测绘（SLAM）技术，在没有任何先验信息的情况下，使用传感数据构建环境地图。由于SLAM是一个被动的过程，现有的USV平台需要定期的远程操作，由人工操作员指导地图的构建过程，这使得探索不是完全自主的。为了使基于SLAM的自主探索成为一个主动的过程，必须集成规划功能，包括两个模块，即效用评估模块和路径生成/选择模块。然而，目前关于效用评估和路径生成的研究无法解决海洋环境中地标稀疏所带来的问题，这将影响勘探的准确性和效率。因此，本研究旨在利用基于概率推理的效用评估和路径生成/选择，开发一种新的主动自主探索框架。更具体地说，我们将构建一个包含虚拟地标的伪地图，作为具有稀疏真实地标的未知受限海洋环境的代理，并使用贝叶斯概率推理分别根据虚拟地标的姿态和位置的边际后验分布评估不确定性。我们还提出了一种新的基于高斯过程（GP）的自主探索路径生成算法，并将路径生成问题作为因子图上的概率推理来解决。交叉熵优化方法将适用于路径规划，通过考虑非线性约束（如usv的机动性），实现GP均值和协方差更新规则的有效推导。这项工作取得成功的关键是与史蒂文斯理工学院领先的海洋机器人专家Brendan Englot教授的国际合作，共同开发框架。这项工作还将与经验丰富的工业合作伙伴密切合作，包括伦敦港务局（PLA）和BMT集团有限公司。通过与PLA和BMT的密切合作，这项研究产生的创新将在Otter USV上实施，以在Tidal Thames上进行用例演示（例如，水文测量）。这项国际合作的长期愿景是建立一个强大的英美研究联盟，研究先进传感器、人工智能/机器学习和机器人技术的未来海洋创新，与更多的学术机构、公司和监管机构/组织合作。