Mon, Apr 30, 2018 @ 02:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
PhD Candidate: Stephanie Kemna
Committee: Gaurav Sukhatme (chair), Nora Ayanian, David Caron
Title: Multi-Robot Strategies for Adaptive Sampling with Autonomous Underwater Vehicles
Time & place: Monday April 30th, 2pm, RTH406
Biologists and oceanographers are sampling lakes and oceans worldwide, to obtain data on the natural phenomena they are interested in. For example, measuring algae abundance to understand and explain potentially harmful algal blooms. Typical methods of sampling are (a) taking physical water samples and sensor measurements from boats, (b) deploying sensor packages off of buoys, docks or other static man-made structures, and more recently (c) running pre-programmed missions with aquatic robots. The use of robot teams could significantly improve cost- and time-efficiency of lake and ocean sampling, allowing persistent and efficient mapping of the water column in finer resolution. Additionally, these systems may be able to intelligently gather data without needing a lot of prior information. We envision a scenario where one or two groups of biologists or oceanographers come together for monitoring a lake, bringing their autonomous vehicles with biological sensors.
Our focus is on improving sampling efficiency, and environmental modeling performance, through the addition of (decentralized) coordination approaches for multi-robot sampling systems. In this presentation, I will discuss adaptive informative sampling techniques for single- and multi-robot deployments. Adaptive informative sampling means that the robots adapt their trajectory online, based on sampled data, while incorporating information-theoretic metrics to seek out the most informative sampling locations. Through simulation studies we have shown the benefits that can be obtained from employing adaptive informative sampling approaches. We include field results to show the feasibility of running adaptive informative sampling on board an autonomous underwater vehicle (AUV).
For the multi-robot case, we show the benefits that can be obtained from adding data sharing between vehicles, and we explore the trade-off of surface based (Wi-Fi) communications versus underwater (acoustic) communications. In terms of coordinating multiple vehicles, I will first discuss an explicit coordination approach, based on dynamic estimation of Voronoi partitions, which shows potential for improving modeling performance in the early stages of model creation. I then discuss a method we developed for how to best start adaptive sampling runs when no prior data is available. Finally, I will discuss the use of implicit coordination through asynchronous surfacing with a surface-based data hub. We showed that performance across surfacing strategies was similar, though some turned out to be less consistent in performance, and some methods showed potential for greatly reducing the number of surfacing events needed.
Overall, I have developed several methods for adaptive informative sampling with AUVs, focusing on multi-robot coordination and field constraints. The results of my studies show the benefits and potential of incorporating data sharing and coordination strategies into adaptive sampling routines for multi-robot systems.
Audiences: Everyone Is Invited
Contact: Lizsl De Leon