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

CPS：中：在资源受限和不确定的环境中实现网络机器人的实时动态控制和适应

• 批准号: 1739315
• 负责人: Dario Pompili
• 金额: $ 99.99万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1739315&HistoricalAwards=false
• 关键词: CPS; Medium; Enabling; Real; time

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.

不同物理变量的河流，湖泊和水库中的近实时水质监测对于防止被污染的水到达平民并部署及时的解决方案或至少要发出早期警告以防止对人类和水生生物的损害至关重要。为了迅速做出最佳决策并“关闭循环”，有必要实时收集，汇总和处理水数据。因此，该项目的目的是设计一个网络物理系统（CPS），其中无人机（例如Rutgium Mutiium Naviator，一种混合​​无人驾驶的空气/水下汽车（HUA/UV））以及自主的水下机器人（例如修改后的Bluerovs）可以（i）conture（i）conture（i）conture（i）conture（i），并根据自己的兴趣（i）进行测量（i），并及时估计，并及其及其; （ii）然后，通过协作信息融合和集成，将这些测量/原始数据的原位转换执行到有价值的信息中，最后将这些测量数据转换为知识。为了实现上述目标，该项目将需要解决任何CPS中传感器的数据处理中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，同时将提高与传统感应系统相比的可扩展性，可靠性和及时性。在任务1中将处理实现本地资源和云资源之间动态合作的挑战，在该任务1中，新型的自适应采样解决方案也将开发ROI的采样成本（就时间或能源消耗而言）。在任务2中，由于计算模型对输入数据和资源的可用性的不可预测的行为，新颖的解决方案将旨在处理本地资源中的模型不确定性。在任务3中，该项目旨在开发生物摄像头，即“实验室机器人”，该项目使用现场测量并与云资源进行通信以实时取得水质的结果；此外，将设计新的解决方案，以优化Naviator当前的混合空气/水多电动平台/推进系统，以便它能够与生物摄像头进行携带和执行测试，同时也增加其耐力。最后，在任务4中，将执行在新泽西州Raritan河上进行的集成现场测试，以验证算法以及分析其可扩展性（从经济和可行性的角度来看）和置信度/准确性性能。具体而言，导航人员将通过多模式操作（即在水和空气中）识别ROI； 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计算能力）为了满足任务截止日期，（ii）使用云的价格是一定的，因此，为了在预算限制内完成任务目标，仅当前者没有足够的计算资源即可成功执行任务（出现爆炸）时，构成工作流程的计算任务才应从本地网络迁移到云。在宣传方面，该项目将开发一条多样的和计算机识字工程师的管道，他们将能够解决自我管理的CPS问题。 PIS 1）在实时的原位分布式计算（用于研究生计算机工程和本科生非工程专业）上创建课程； 2）开发教学模块，以纳入关键的高中活动； 3）利用现有的少数民族学生推广计划和罗格斯的网络； 4）结合交流计划和团队教学方法； 5）利用分布式教育技术，并应用于机器人技术和网络。我们的电气/计算机和机械工程团队具有理论和系统级别的技能，跨学科的专业知识，以及可验证的富有成果的合作历史，以充分利用该项目的研究和教育潜力。

项目成果

UW-MARL: Multi-Agent Reinforcement Learning for Underwater Adaptive Sampling using Autonomous Vehicles

UW-MARL：使用自动驾驶车辆进行水下自适应采样的多智能体强化学习

• DOI: 10.1145/3366486.3366533
• 发表时间: 2019
• 期刊: ACM International Conference on Underwater Networks and Systems (WUWNet
• 作者: Rahmati, Mehdi;Nadeem, Mohammad;Sadhu, Vidyasagar;Pompili, Dario
• 通讯作者: Pompili, Dario

Real-time Image Enhancement for Vision-based Autonomous Underwater Vehicle Navigation in Murky Waters

• DOI: 10.1145/3366486.3366523
• 发表时间: 2019-10
• 期刊: Proceedings of the 14th International Conference on Underwater Networks & Systems
• 作者: Wenjie Chen;M. Rahmati;Vidyasagar Sadhu;D. Pompili

Compressed Underwater Acoustic Communications for Dynamic Interaction with Underwater Vehicles

用于与水下航行器动态交互的压缩水下声学通信

• DOI: 10.1145/3366486.3366488
• 发表时间: 2019
• 期刊: ACM International Conference on Underwater Networks and Systems (WUWNet
• 作者: Rahmati, Mehdi;Arjula, Archana;Pompili, Dario
• 通讯作者: Pompili, Dario