### Rapidly Exploring Random Cycles: Persistent Estimation of Spatiotemporal Fields With Multiple Sensing Robots

@article{lan_rapidly_2016,
title = {Rapidly {Exploring} {Random} {Cycles}: {Persistent} {Estimation} of {Spatiotemporal} {Fields} {With} {Multiple} {Sensing} {Robots}},
volume = {32},
issn = {1552-3098, 1941-0468},
shorttitle = {Rapidly {Exploring} {Random} {Cycles}},
url = {http://ieeexplore.ieee.org/document/7570171/},
abstract = {This paper considers the problem of planning trajectories for both single and multiple sensing robots to best estimate a spatio-temporal ﬁeld in a dynamic environment. The robots use a Kalman ﬁlter to maintain an estimate of the ﬁeld value, and to compute the error covariance matrix of the estimate. Two new sampling-based path planning algorithms (RRC and RRC*) are proposed to ﬁnd periodic trajectories for the sensing robots that minimize the largest eigenvalue of the error covariance matrix over an inﬁnite horizon. The algorithms are proven to ﬁnd the minimum inﬁnite horizon cost cycle in a random graph, which grows by successively adding random points. The algorithms leverage recently developed methods for periodic Riccati recursions to efﬁciently compute the inﬁnite horizon cost of the cycles, and they use the monotonicity property of the Riccati recursion to efﬁciently compare the costs of different cycles without explicitly computing their costs. The algorithms are demonstrated in a study using National Oceanic and Atmospheric Administration (NOAA) data to plan sensing trajectories in the Caribbean Sea. Our algorithms signiﬁcantly outperform random, greedy, and receding horizon approaches in this environment.},
language = {en},
number = {5},
urldate = {2020-09-15},
journal = {IEEE Transactions on Robotics},
author = {Lan, Xiaodong and Schwager, Mac},
month = oct,
year = {2016},
keywords = {filtering\_and\_estimation},
pages = {1230--1244},
month_numeric = {10}
}