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

Overview: 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, e.g., to withdraw water from a treatment plant in the case of an emergency (caused by an accident or a terrorist attack) or at least to issue early warnings so to save damage to human and aquatic life. To make optimal decisions and “close the loop” promptly, it is necessary to collect, aggregate, and process real-time water data. Our cross-disciplinary engineering team has expertise and proof of success in the study, design, and development of Technology for Cyber-Physical System (CPS) (II.A.2) and Engineering of CPS (II.A.3). Our vision is to design a CPS where drones such as the multi-medium Naviator, a Hybrid Unmanned Air/Underwater Vehicle (HUA/UV), and underwater robots can be steered to the region of interest to take measurements/collect biosamples and enable their in-situ transformation into valuable information and, finally, into knowledge through information fusion and integration. Towards this goal, fundamental problems need to be solved: in-situ processing of data from sensors in any CPS is, in fact, susceptible to three types of uncertainties arising from multiple sources (i.e., resource, model, and data). Our approach will provide greater autonomy and cooperation in CPSs and at the same time ensure greater scalability, reliability, and timeliness in comparison to traditional sensing systems.

Key words: water-quality monitoring; multi-medium (air/underwater) robots; mobile cloud computing.

Intellectual Merit: The challenges to achieve dynamic collaboration between local and cloud resources will be handled in Task 1. We will also develop novel adaptive-sampling solutions that minimize the sampling cost (in terms of time or energy expenditure) of a region of interest. In Task 2, we will provide novel solutions to handle model uncertainties in the local resource pool due to the unpredictable behavior of computational models to input data and local resources’ availability. In Task 3, we will develop a biosampler, “lab-on-robot”, that uses in-situ measurements and communicates with the cloud resources to give results in real time on the quality of water in various water bodies. We will develop new solutions to optimize the Naviator’s current hybrid air/water multirotor platform/propulsion system in order for it to be able to carry and perform testing with the biosampler while also increasing its endurance. Finally, in Task 4, we will concentrate on transitioning to field experiments so to validate our algorithms as well as to analyze their scalability (from an economical and feasibility perspective) and confidence/accuracy performance.

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 can 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).

We will also develop a pipeline of diverse and computer literate engineers who will be able to solve self-management CPS problems. We 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.