CPS: Medium: Enabling Real-time Dynamic Control and Adaptation of Networked Robots in Resource-constrained and Uncertain Environments

对具有不同物理变量的河流、湖泊和水库进行近实时水质监测，对于防止受污染的水流入平民人口和及时部署解决方案，或至少发出预警以防止对人类和水生生物造成损害至关重要。为了做出最佳决策并迅速“闭环”，需要真实的实时收集、汇总和处理水数据。因此，该项目的目标是设计一个网络物理系统（CPS），其中无人机，如罗格斯多媒体导航仪，混合无人空中/水下航行器（HUA/UV）和自主水下机器人（例如，改进的BlueROV）可以（i）首先识别感兴趣区域（ROI）并进行测量，以及如果需要的话，从它们收集生物样品;（ii）然后，通过协作信息融合和集成，执行这些测量/原始数据到有价值信息的原位转换，并最终转换为知识。为达致上述目的，本项目将需要解决在任何CPS中对传感器数据进行现场处理时出现的不确定性问题。该项目将在CPS中提供更大的自主权和合作，同时，与传统的传感系统相比，将提高可扩展性，可靠性和及时性。实现本地和云资源之间动态协作的挑战将在任务1中处理，其中还将开发新颖的自适应采样解决方案，以最大限度地降低ROI的采样成本（在时间或能源支出方面）。在任务2中，将设计新的解决方案来处理本地资源中的模型不确定性，这是由于计算模型输入数据和资源可用性的不可预测行为。在任务3中，该项目旨在开发生物采样器，即，“机器人实验室”，使用现场测量并与云资源进行通信，以真实的时间给出水质结果;此外，将设计新的解决方案来优化Naviator当前的混合空气/水多旋翼平台/推进系统，以便它能够携带生物采样器并进行测试，同时也增加了其耐久性。最后，在任务4中，将在新泽西州拉里坦河进行综合现场测试，以验证算法并分析其可扩展性（从经济和可行性角度）和置信度/准确度性能。具体而言，导航员将通过多模式操作识别ROI，即，在水和空气中;然后蓝色遥控潜水器（在项目实施过程中，将实现自主，并将进行修改以携带机载水质传感器）将使用任务1中设计的算法在每个ROI中执行水下自适应采样。就更广泛的影响而言，云和本地资源之间的协作可以通过以下方式使任何CPS受益：（i）将计算外包给云将允许资源受限的车辆（在计算能力方面）满足使命期限，以及（ii）使用云是有代价的，因此，为了在预算限制内完成使命目标，只有当本地网络没有足够的计算资源来成功地执行任务时，才应该将构成工作流的计算任务从本地网络迁移到云（爆发）。在推广方面，该项目将开发一个多元化和计算机知识工程师的管道，他们将能够解决自我管理CPS问题。PI将1）创建一个实时原位分布式计算课程（研究生计算机工程和本科非工程专业）; 2）开发教学模块纳入重点高中活动; 3）利用现有的少数民族学生外展计划和网络在罗格斯大学; 4）纳入交流计划和团队教学方法;以及5）利用分布式教育技术，将其应用于机器人和网络。我们的电气/计算机和机械工程团队拥有理论和系统级技能，跨学科专业知识，以及富有成效的合作的可验证历史，以充分利用该项目的研究和教育潜力。

Near-real-time water-quality monitoring in rivers, lakes, and water reservoirs of different physical variables is critical to prevent contaminated water from reaching the civilian population and to deploy timely solutions, or at least to issue early warnings so as to prevent damage to human and aquatic life. In order to make optimal decisions and "close the loop" promptly, it is necessary to collect, aggregate, and process water data in real time. Therefore, the goal of this project is to design a Cyber Physical System (CPS) where drones such as the Rutgers multi-medium Naviator, a Hybrid Unmanned Air/Underwater Vehicle (HUA/UV), and autonomous underwater robots (e.g., modified BlueROVs) can (i) first identify Regions of Interest (RoIs) and take measurements and well as, if needed, collect biosamples from them; (ii) and then, through collaborative information fusion and integration, perform in-situ transformation of these measurements/raw data into valuable information and, finally, into knowledge. To achieve the above goal, this project will need to solve the problem of uncertainties that arise in in-situ processing of data from sensors in any CPS. This project will provide greater autonomy and cooperation in CPSs and, at the same time, will improve scalability, reliability, and timeliness in comparison to traditional sensing systems. The challenges to achieve dynamic collaboration between local and cloud resources will be handled in Task 1, in which novel adaptive-sampling solutions that minimize the sampling cost of a RoI (in terms of time or energy expenditure) will also be developed. In Task 2, novel solutions will be designed to handle model uncertainties in the local resources due to the unpredictable behavior of computational models to input data and resources' availability. In Task 3, the project aims at developing a biosampler, i.e., "lab-on-robot", that uses in-situ measurements and communicates with the cloud resources to give results in real time on the water quality; also, new solutions to optimize the Naviator's current hybrid air/water multirotor platform/propulsion system will be designed in order for it to be able to carry and perform testing with the biosampler while also increasing its endurance. Finally, in Task 4, integrated field testing on the Raritan River, NJ, will be performed so as to validate the algorithms as well as to analyze their scalability (from an economical and feasibility perspective) and confidence/accuracy performance. Specifically, the Naviators will identify the RoIs via multimodal operations, i.e., in water and air; and then the BlueROVs (which, during the course of the project, will be made autonomous and will be modified to carry on-board water-quality sensors) will perform underwater adaptive sampling in each of those RoIs using the algorithms designed in Task 1.In terms of broader impacts, the collaboration between cloud and local resources can benefit any CPS in the following ways: (i) outsourcing computation to the cloud will allow resource-constrained vehicles (in terms of computational capability) to meet mission deadlines, and (ii) using clouds comes at a price, hence, in order to accomplish the mission goals within budget constraints, the computational tasks composing a workflow should be migrated from the local network to the cloud only when the former does not have enough computational resources to execute successfully the tasks (outbursting). In terms of outreach, this project will develop a pipeline of diverse and computer literate engineers who will be able to solve self-management CPS problems. The PIs will 1) create a course on real-time in-situ distributed computing (for graduate computer engineering and undergraduate non-engineering majors); 2) develop teaching modules for incorporation into key high-school activities; 3) leverage existing minority student outreach programs and networks at Rutgers; 4) incorporate exchange programs and team-teaching approaches; and 5) utilize distributed education technologies with application to robotics and networking. Our electrical/computer and mechanical engineering team has the theoretical and system-level skills, cross-disciplinary expertise, as well as a verifiable history of fruitful collaboration to exploit fully this project's research and educational potential.