RI: Small: Decision Making with Spatially and Temporally Uncertain Data

University Of Southern California, Los Angeles CA

Investigators

Abstract

This research investigates how robots can plan effectively in dynamic settings. The focus is on spatio-temporal ocean monitoring with autonomous underwater robots. Such monitoring and sensing allows scientists to gain a greater understanding of the planet and its environmental processes. For autonomous operation in the ocean, robots need to make decisions in spite of extensive uncertainty in ocean currents that change both spatially and temporally, and are often strong enough to alter the vehicle's motion significantly. The algorithms developed in this work are tested in the underwater domain with oceanographic phenomena of interest (e.g., algal blooms). Tests are performed on multiple datasets available at USC from prior field trials with underwater robots. Through the use of both robot testing and simulated testing, the methods developed in this work are validated across a wide range of applications and scales. When planning for a long-range and long-term environmental monitoring task, an accurate planner requires a forecast of ocean currents at the destination and intermediate way-points in order to determine corresponding actions. A drawback of state-of-the-art decision making methodologies lies in the fact that they rely on a known uncertainty description that is a "snapshot at a certain moment in time." In this research, the focus is on the design of new mechanisms that generalize state-of-the-art decision-making methodologies to overcome this limitation. The goal is the creation of a general methodology to compute control policies that consider not only a fully known (current and past) stochastic description, but also a (possibly uncertain) prediction of future dynamics. The stochastic transition dynamics are considered as a noisy or uncertain function that varies with some parameter such as the time. To solve this problem, a new and efficient value propagation mechanism is developed involving two processes that evolve in both spatial and temporal dimensions.