Index Catalog // ScholarsArchive@OSU

Creator:: Hollinger, Geoffrey A. and Sukhatme, Gaurav S.
Abstract:: We propose three sampling-based motion planning algorithms for generating informative mobile robot trajectories. The goal is to find a trajectory that maximizes an information quality metric (e.g. variance reduction, information gain, or mutual information) and also falls within a pre-specified budget constraint (e.g. fuel, energy, or time). Prior algorithms have...
Resource Type:: Article
Full Text:: Information Gathering Algorithms Geoffrey A. Hollinger∗ and Gaurav S. Sukhatme† August 12, 2014 Abstract

Creator:: Hollinger, Geoffrey A., Yerramalli, Srinivas, Singh, Sanjiv, Mitra, Urbashi, and Sukhatme, Gaurav S.
Abstract:: This paper presents novel data fusion methods that enable teams of vehicles to perform target search tasks without guaranteed communication. Techniques are introduced for merging estimates of a target’s position from vehicles that regain contact after long periods of time, and a fully distributed team planning algorithm is proposed that...
Resource Type:: Article
Full Text:: , Fellow, IEEE, and Gaurav S. Sukhatme, Fellow, IEEE Abstract—This paper presents novel data fusion

Creator:: Hollinger, Geoffrey A., Pereira, Arvind A., Binney, Jonathan, Somers, Thane, and Sukhatme, Gaurav S.
Abstract:: Operating autonomous underwater vehicles (AUVs) near shore is challenging—heavy shipping traffic and other hazards threaten AUV safety at the surface, and strong ocean currents impede navigation when underwater. Predictive models of ocean currents have been shown to improve navigation accuracy, but these forecasts are typically noisy, making it challenging to...
Resource Type:: Article
Full Text:: , OR 97331 somersth@oregonstate.edu Gaurav S. Sukhatme§ Department of Computer Science Viterbi

Creator:: Chung, Jen Jen, Smith, Andrew J., Skeele, Ryan, and Hollinger, Geoffrey A.
Abstract:: In this paper, we introduce a novel algorithm for incorporating uncertainty into lookahead planning. Our algorithm searches through connected graphs with uncertain edge costs represented by known probability distributions. As a robot moves through the graph, the true edge costs of adjacent edges are revealed to the planner prior to...
Resource Type:: Article

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