NeTS: Small: Co-Optimization of Sensing, Communications and Navigation of a Robotic Network under Resource Constraints

University Of California-Santa Barbara, Santa Barbara CA

Investigators

Abstract

Robotic networks can have a tremendous impact in many different areas such as disaster relief, emergency response, and national security. The recent disasters such as Hurricane Sandy of 2012 or Japan's earthquake of 2011 remind us of the crucial role that unmanned autonomous networks can play as part of our society. The goal of this project is to introduce a new multi-disciplinary design paradigm for the successful operation of mobile robotic networks through the co-optimization of sensing, communications and navigation. In the robotics/control community, most existing work does not deal with realistic communication issues (such as shadowing and multipath fading) and ideal links/disk models are assumed for predicting connectivity. On the other hand, the communication and networking communities are not typically concerned with path planning and navigation. In a robotic network, path planning not only affects sensing quality but also impacts connectivity maintenance. This multi-disciplinary nature makes designing robust decision-making strategies for a successful task accomplishment in robotic networks considerably challenging and an open problem. Furthermore, a separate optimization of the given sensing, communications and navigation resources may not suffice for a successful operation under resource constraints. In this research effort, the focus is on the impact of limited energy (both motion and communications), time, and bandwidth resources and on laying the foundation of the corresponding optimum sensing, communication and navigation co-design policies, which includes trajectory, sensing, connectivity, motion speed/power, and communication transmission rate/power optimization. In this approach, realistic probabilistic connectivity metrics are properly co-optimized with sensing and navigation goals such that each robot chooses a trajectory that allows it to maximize its information gathering while maintaining the needed connectivity. This framework answers fundamental questions such as when to invest in motion and when to invest in communications. The project also addresses task feasibility and the fundamental limits of information generation, gathering and exchange, which can provide key insights for resource planning before deployment. Overall, this new co-optimization foundation enables the successful operation of robotic networks under limited resources and can thus have a tremendous impact on our society. This project also has a significant educational impact on minority and under-represented students.