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

对不同物理变量的河流、湖泊和水库进行近实时的水质监测，对于防止受污染的水进入平民人口并及时部署解决方案至关重要，或者至少是发出早期预警，以防止对人类和水生生物的损害。为了做出最优决策并迅速“闭合回路”，有必要对水数据进行实时收集、汇总和处理。因此，该项目的目标是设计一个网络物理系统，在其中，罗格斯大学多媒体导航器、混合式无人驾驶飞机/水下机器人和自主水下机器人(例如改装的BlueROV)等无人机可以(1)首先确定感兴趣区域并进行测量，然后根据需要收集其中的生物样本；(2)然后通过协作信息融合和整合，将这些测量/原始数据就地转化为有价值的信息，最后转化为知识。为了实现上述目标，该项目将需要解决在现场处理来自任何CP的传感器的数据时出现的不确定性问题。该项目将在CPSS中提供更大的自主权和合作，同时与传统的传感系统相比，将提高可伸缩性、可靠性和及时性。任务1将处理实现本地资源和云资源之间的动态协作的挑战，其中还将开发新的自适应采样解决方案，以最大限度地减少ROI的采样成本(在时间或能量支出方面)。在任务2中，将设计新的解决方案来处理由于计算模型输入数据和资源可用性的不可预测行为而导致的本地资源中的模型不确定性。在任务3中，该项目旨在开发一种生物采样器，即“机器人实验室”，它使用现场测量并与云资源通信，实时给出水质结果；此外，将设计新的解决方案，以优化导航器目前的空气/水混合多旋翼平台/推进系统，使其能够携带和执行生物采样器的测试，同时还增加其耐久性。最后，在任务4中，将在新泽西州Raritan River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/精度性能。具体地说，导航员将通过多式联运，即在水和空气中确定区域经济实体；然后，BlueROV(在项目过程中将成为自主的，并将被修改为携带船上的水质传感器)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。从更广泛的影响来看，云和本地资源之间的协作可以通过以下方式使任何CP受益：(I)将计算外包给云将允许资源受限的飞行器(就计算能力而言)满足任务期限，以及(Ii)使用云是有代价的，因此，为了在预算受限的情况下完成任务目标，只有当本地网络没有足够的计算资源来成功执行任务(突发)时，才应该将组成工作流的计算任务从本地网络迁移到云中。在外联方面，该项目将培养一批不同的、精通计算机的工程师，他们将能够解决自我管理的CPS问题。PIS将1)创建一门关于实时现场分布式计算的课程(针对计算机工程研究生和本科非工程专业)；2)开发教学模块，以纳入关键的高中活动；3)利用罗格斯大学现有的少数族裔学生外联计划和网络；4)整合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卓有成效的合作历史，以充分开发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.