In this paper, we introduce publicly available human-robot teaming datasets captured during the summer 2018 season using our Aquaticus testbed. Our Aquaticus testbed is designed to examine the interactions between human-human and human-robot teammates while situated in the marine environment in their own vehicles. In particular, we assess these interactions while humans and fully autonomous robots play a competitive game of capture the flag on the water. Our testbed is unique in that the humans are situated in the field with their fully autonomous robot teammates in vehicles that have similar dynamics. Having a competition on the water reduces the safety concerns and cost of performing similar experiments in the air or on the ground. By having the competitions on the water, we create a complex, dynamic, and partially observable view of the world for participants while in their motorized kayak. The main modality for teammate interaction is audio to better simulate the experience of real-world tactical situations - ie fighter pilots talking to each other over radios. We have released our complete datasets publicly so that we can enable researchers throughout the HRI community that do not have access to such a testbed and may have expertise other than our own to leverage our datasets to perform their own analysis and contribute to the HRI community.

Project Aquaticus is a human-robot teaming competition on the water involving autonomous surface vehicles and human operated motorized kayaks. Teams composed of both humans and robots share the same physical environment to play capture the flag. In this paper, we present results from seven competitions of our half-court (one participant versus one robot) game. We found that participants indicated more trust in more aggressive behaviors from robots.

This paper is about the autonomous control of an autonomous underwater vehicle (AUV), and the particular considerations required to allow proper control while towing a 100-meter vector sensor array. Mission related objectives are tempered by the need to consider the effect of a sequence of maneuvers on the motion of the towed array which is thought not to tolerate sharp bends or twists in sensitive material. We describe and motivate an architecture for autonomy structured on the behavior-based control model augmented with a novel approach for performing behavior coordination using multi-objective optimization. We provide detailed in-field experimental results from recent exercises with two 21-inch AUVs in Monterey Bay California.

This paper describes an investigation into the adaptive control of autonomous mobile sensor platforms for providing oceanographic sampling. Mobile sensor platforms provide an ability to rapidly sample oceanographic data of interest for real-time input into ocean environmental models with the goal of reducing the modeling uncertainty by introducing selected sampled data. The major objective of this paper is to describe the autonomy architecture developed to support adaptive sampling. This architecture consists of an open-source distributed autonomy architecture and an approach to behavior-based control of autonomous vehicles using multiple objective functions that allows reactive control in complex environments with multiple constraints. Experimental results are provided for an adaptive ocean thermal gradient tracking application performed by an autonomous surface craft in Monterey Bay. These results highlight not only the suitability of autonomous sensor platforms for providing adaptive sampling of the ocean environment but, also, the suitability of our behavior-based autonomy approach and distributed autonomy architecture for providing a simple, flexible, and scalable method for autonomous sensor platform control. The paper concludes with an overview of future adaptive sampling experiments planned with autonomous underwater sensor platforms using the same methodology.

This paper describes an investigation into the control of autonomous mobile sensor platforms in a marine sensor network used to provide monitoring of transitory phenomenon over a wide area. A distributed network of small, inexpensive vehicles with heterogeneous sensors allows us to build a robust monitoring network capable of real-time response to rapidly changing sensor data. The major objective of this paper is to describe a framework for adaptive and cooperative control of the autonomous sensor platforms in such a network. This framework has two major components, a sensor that provides high-level state information to a behavior-based autonomous vehicle control system and a new approach to behavior-based control of autonomous vehicles using multiple objective functions that allow reactive control in complex environments with multiple constraints. Experimental results are presented for a 2-D target tracking application using a network of autonomous surface craft in which one platform with a simulated bearing sensor tracks a moving target and relays the target state information to a second vehicle that is moving in a classification mode. From these results, it is readily seen that there is the potential for potent synergy from the cooperation of multiple sensor platforms which can each view an event of interest from a different vantage point

The ocean, is fundamentally important to many areas of modern society and thus improved knowledge of the ocean is essential. Ocean scientists have made remarkable progress in observation technology, modeling and assimilation in physical oceanography, acoustics, and biology. To some extent, such advances have been confined to each discipline. Therefore a great demand has arisen for a modern distributed computing and networking infrastructure within which we bring together advanced modeling, observation tools and field estimation methods. The paper describes a knowledge network of distributed heterogeneous data and software resources for multidisciplinary ocean research.

Matched field and ocean acoustic tomography concern the estimation of parameters for models of ocean environments using acoustics. Both require full field representations for the observed signals since waveguide effects are important. The authors present Cramer-Rao lower bounds for the attainable accuracy of both methods. These bounds are expressed in terms of the Green's function for the propagation between source and receivers.