Cooperative autonomous marine vehicles for adaptive passive acoustic monitoring

该研究项目旨在开发一群能够对近岸环境进行声学监测的自主海洋车辆。这包括设计机器人系统的硬件和安装智能软件，使机器人能以合作的方式执行任务。机器人潜水器将利用水听器传感器被动监测海洋环境，以探测和定位海面以下的个别噪音源（即海洋哺乳动物、潜水器、水下涡轮机等）。最初，一系列用于实现群机器人智能导航的当前方法将根据需要交换的信息量以及导航过程中机器人的行为进行检查和评估。在此基础上，将创造新的方法。为了实现新的方法，将使用机器学习，试图将联合收割机传统的人工智能与群体机器人相结合。根据迄今为止所做的文献综述，似乎这是一项尚未得到足够关注的任务（Brambilla et al.，2013年）的报告。因此，将产生的想法可以推进该领域的新方向。群体智能算法将使用简单的模拟环境进行测试，以确保合作行为背后的基本思想得到正确实施。机器人平台的行为将使用机器人模拟软件进行模拟。在项目的后期阶段，将在真实的机器人平台上测试该软件，在项目期间，将使用国家海洋学中心提供的设备和设施（NOC，2017），作为NEXUSS计划的一部分，该项目是其中的一部分。目标：虽然群机器人文献包括许多提出的导航算法，但在真实的环境中测试的并不多（Brambilla等人，（Tan and Zheng，2013）。该项目旨在通过创建能够展示所用算法能力的真实的平台来填补差距。创建一个通用的导航算法，用于其他群体机器人项目的开发。这将是在群体机器人领域实现这种规模的首次尝试之一。挑战：平台复杂性/平台数量：为了使平台尽可能便宜且易于组装，需要限制其硬件复杂性。对于这个项目，这可以通过为集群使用更多的平台来补偿。另一方面，这可能增加导航和通信任务的复杂性。因此，需要确定硬件复杂性和软件复杂性之间的适当比率。分布式处理/集中式处理：分布式处理意味着每个代理都自己做出决定，这可能需要复杂的软件才能使群正常运行。另一方面，集中式处理表明，其中一个代理是领导者，其他代理都服从其命令。这可能有自己的挑战，比如领导者发生了什么事情，团队无法完成任务。确定系统在分布式/集中式处理频谱中的最佳位置将是该项目的一大挑战。Ferrante，E.，比拉塔里湾Dorigo，M.（2013年）。群体机器人：从群体工程学的角度回顾。Swarm Intelligence，7（1），pp.1-41.谭，Y。和Zheng，Z.（2013年）。群机器人研究进展国防技术，9（1），第18 -39页。NOC。（2017年）。的主页.国家海洋学中心官方网站：https://noc.ac.uk/

This research project will aim to develop a swarm of autonomous marine vehicles that will be able to perform acoustic monitoring of the near-shore environment. This will include both the design of the hardware of the robotic systems and the implementation of the intelligent software that will allow the vehicles to perform the task in a cooperative manner.The robotic vehicles will make use of hydrophone sensors to passively monitor the marine environment, in order to detect and locate individual sources of noise below the surface of the sea (i.e. marine mammals, sub-marine vehicles, under-water turbines etc.).Initially, a range of current methods used to implement intelligent navigation of swarm robots will be examined and evaluated based the amount of information required to be exchanged, as well as the behaviour of the robots during navigation. New methods will be created based on the results.For the implementation of new methods, machine learning will be used, in an attempt to combine conventional artificial intelligence with swarm robotics. Based on the literature review done so far, it seems that this is a task that has not yet received enough attention (Brambilla et al., 2013). Therefore the ideas that will be produced can advance the field in new directions.The swarm intelligence algorithms will be tested using simple simulated environments, to ensure that the basic ideas behind the cooperative behaviour are implemented properly. The behaviour of the robotic platforms will be simulated using robot simulation software. In the later stages of the project, the software will be tested on real robotic platforms.During the project, there will be use of equipment and facilities provided by the National Oceanography Centre (NOC, 2017), as part of the NEXUSS program, that this project is part of.Objectives: Although the swarm robotics literature consists of many proposed navigation algorithms, not many have been tested in real environments (Brambilla et al., 2013) (Tan and Zheng, 2013). This project will aim to fill the gap by creating real platforms that will be able to demonstrate the capabilities of the algorithms used. The creation of a general navigation algorithm for use in the development of other swarm robotics projects. This will be one of the first attempts in achieving something of this scale in the field of swarm robotics.Challenges: Platform Complexity/ Platform Quantity: For the platforms to be as cheap and easily assembled as possible, their hardware complexity will need to be limited. For this project, this can be compensated by using a larger number of platforms for the swarm. On the other hand this can increase the complexity of the navigation and communication tasks. Therefore, a proper ratio between hardware complexity and software complexity needs to be identified. Distributed Processing/ Centralised Processing: Distributed processing suggests that each agent makes decisions for itself by itself, which can require complex software in order for the swarm to operate properly. On the other hand, centralised processing suggests that one of the agents is the leader and every other agent is subject to its commands. This can have its own challenges, like the case that something happens to the leader and the group is unable to finish the task. Identifying where the system should optimally lie in the distributed/centralised processing spectrum will be a big challenge of this project.References: Brambilla, M., Ferrante, E., Birattari, M. and Dorigo, M. (2013). Swarm robotics: a review from the swarm engineering perspective. Swarm Intelligence, 7(1), pp.1-41. Tan, Y. and Zheng, Z. (2013). Research Advance in Swarm Robotics. Defence Technology, 9(1), pp.18-39. NOC. (2017). Home Page. Retrieved from National Oceanography Centre Official Website: https://noc.ac.uk/