We detail our ongoing work in Flint, Michigan to detect pipes made of lead and other hazardous metals. After elevated levels of lead were detected in residents' drinking water, followed by an increase in blood lead levels in area children, the state and federal governments directed over $125 million to replace water service lines, the pipes connecting each home to the water system. In the absence of accurate records, and with the high cost of determining buried pipe materials, we put forth a number of predictive and procedural tools to aid in the search and removal of lead infrastructure. Alongside these statistical and machine learning approaches, we describe our interactions with government officials in recommending homes for both inspection and replacement, with a focus on the statistical model that adapts to incoming information. Finally, in light of discussions about increased spending on infrastructure development by the federal government, we explore how our approach generalizes beyond Flint to other municipalities nationwide.

Online approximation of the optimal station-keeping strategy for a marine craft subject to an irrotational current is considered. An approximate policy that minimizes a user-defined cost function over an infinite time horizon is obtained using an actor-critic-identifier-based adaptive dynamic programming technique. The hydrodynamic drift dynamics are assumed to be unknown; therefore, a concurrent learning-based system identifier is developed to identify the unknown model parameters. The identified model is used to implement an adaptive model-based reinforcement learning technique to estimate the unknown value function. The developed policy guarantees uniformly ultimately bounded convergence of the vehicle to the desired station and uniformly ultimately bounded convergence of the approximated policies to the optimal polices without the requirement of persistence of excitation. The developed strategy is validated using an autonomous underwater vehicle, where the three degrees-of-freedom in the horizontal plane are regulated. The experiments are conducted in a second-magnitude spring located in central Florida.

When the residents of Flint learned that lead had contaminated their water system, the local government made water-testing kits available to them free of charge. The city government published the results of these tests, creating a valuable dataset that is key to understanding the causes and extent of the lead contamination event in Flint. This is the nation's largest dataset on lead in a municipal water system.

This study focuses on the development of a nonlinear control design for a fully-actuated autonomous underwater vehicle (AUV) using a continuous robust integral of the sign of the error control structure to compensate for system uncertainties and sufficiently smooth bounded exogenous disturbances. A Lyapunov stability analysis is included to prove semiglobal asymptotic tracking. The resulting controller is experimentally validated on an AUV developed at the University of Florida in both controlled and open-water environments.

Sensor fusion and sensor integration is becoming an increasingly popular approach in dealing with complex sensor systems in autonomous mobile robots (AMR). However, the procedure for the sensor integration and sensor fusion is a non-trivial process. This paper presents a scenario based approach to sensor fusion based on the autonomous evolution of sensory and actuator driver layers through environmental constraints (AEDEC) [T.A Choi, 2002]. Using the scenario based approach, the programmer's work of creating a sensory driver will be eliminated by having the AMR learn the driver on its own. In the process of creating each scenario, sensor fusion is automatically implemented. If sensors change or even if the sensor configuration changes, the driver can be updated by having the AMR relearn the driver over again. Due to the tabular structure of the scenario based sensory drivers, malfunctioning sensors can not only be detected, but the driver can automatically adapt to the malfunctioning sensor in real-time. Furthermore, different AMR's trained using AEDEC architecture will have similar interpretations of its environment. This is guaranteed by having the AMR learn the driver in the same highly structured training environment. The behavioral coding is simplified by eliminating any reference to hardware dependent parameters. Finally, the level of abstraction and the consistency of the highly structured environment allows for coding portability.

This paper demonstrates fundamental problems with dexterity measures found throughout the robotics literature and offers a methodology for correcting those problems. Measures of robot dexterity derived from eigenvalues, eigenvectors, similarity transformations, singular-value decompositions and the Moore-Penrose inverse of the manipulator Jacobian do not have invariant physical meaning. The paper presents manipulability ellipsoids and manipulability screw-subspaces for both redundant and nonredundant manipulators.>