Persistent Monitoring of Events With Stochastic Arrivals at Multiple Stations
Name
Karaman_Persistent monitoring.pdf
Size
686.3 KB
Format
Adobe PDF
Checksum (MD5)
aa3c96b4183fda82398a649876a61b77
Author(s)
Yu, Jingjin
Karaman, Sertac
Rus, Daniela L
Date Issued
March 2015
Journal
IEEE Transactions on Robotics
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Yu, Jingjin, Sertac Karaman, and Daniela Rus. “Persistent Monitoring of Events With Stochastic Arrivals at Multiple Stations.” IEEE Transactions on Robotics 31.3 (2015): 521–535.
Version
Author's final manuscript
Abstract
This paper introduces a new mobile sensor scheduling problem involving a single robot tasked to monitor several events of interest that are occurring at different locations (stations). Of particular interest is the monitoring of transient events of a stochastic nature, with applications ranging from natural phenomena (e.g., monitoring abnormal seismic activity around a volcano using a ground robot) to urban activities (e.g., monitoring early formations of traffic congestion using an aerial robot). Motivated by examples like these, this paper focuses on problems in which the precise occurrence times of the events are unknown apriori, but statistics for their interarrival times are available. In monitoring such events, the robot seeks to: (1) maximize the number of events observed and (2) minimize the delay between two consecutive observations of events occurring at the same location. This paper considers the case when a robot is tasked with optimizing the event observations in a balanced manner, following a cyclic patrolling route. To tackle this problem, first, assuming that the cyclic ordering of stations is known, we prove the existence and uniqueness of the optimal solution and show that the solution has desirable convergence rate and robustness. Our constructive proof also yields an efficient algorithm for computing the unique optimal solution with O(n) time complexity, in which n is the number of stations, with O(log n) time complexity for incrementally adding or removing stations. Except for the algorithm, our analysis remains valid when the cyclic order is unknown. We then provide a polynomial-time approximation scheme that computes for any ε > 0 a (1 + ε)-optimal solution for this more general, NP-hard problem.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/tro.2015.2409453
