PeRL STUDIES AUTONOMOUS NAVIGATION & MAPPING FOR MOBILE ROBOTS IN A PRIORI UNKNOWN ENVIRONMENTS.

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Risk aversion in belief-space planning under measurement acquisition uncertainty

Summary


Stephen M. Chaves, Jeffrey M. Walls, Enric Galceran and Ryan M. Eustice, Risk aversion in belief-space planning under measurement acquisition uncertainty. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 2079-2086, Hamburg, Germany, September 2015.

Abstract

This paper reports on a Gaussian belief-space planning formulation for mobile robots that includes random measurement acquisition variables that model whether or not each measurement is actually acquired. We show that maintaining the stochasticity of these variables in the planning formulation leads to a random belief covariance matrix, allowing us to consider the risk associated with the acquisition in the objective function. Inspired by modern portfolio theory and utility optimization, we design objective functions that are risk-averse, and show that risk-averse planning leads to decisions made by the robot that are desirable when operating under uncertainty. We show the benefit of this approach using simulations of a planar robot traversing an uncertain environment and of an underwater robot searching for loop-closure actions while performing visual SLAM.

Bibtex entry

@INPROCEEDINGS { schaves-2015b,
    AUTHOR = { Stephen M. Chaves and Jeffrey M. Walls and Enric Galceran and Ryan M. Eustice },
    TITLE = { Risk aversion in belief-space planning under measurement acquisition uncertainty },
    BOOKTITLE = { Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems },
    YEAR = { 2015 },
    MONTH = { September },
    ADDRESS = { Hamburg, Germany },
    PAGES = { 2079--2086 },
}