Monte Carlo algorithm for trajectory optimization based on Markovian readings

This paper describes an efficient algorithm to find a smooth trajectory joining two points A and B with minimum length constrained to avoid fixed subsets. The basic assumption is that the locations of the obstacles are measured several times through a mechanism that corrects the sensors at each read...

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Bibliographic Details
Published in:Computational optimization and applications 2012, Vol.51 (1), p.305-321
Main Authors: Dias, Ronaldo, Garcia, Nancy L., Zambom, Adriano Z.
Format: Article
Language:English
Subjects:
Algorithms
Autonomous vehicles
Avoidance
Confidence
Convex and Discrete Geometry
Ellipses
Joining
Management Science
Mathematics
Mathematics and Statistics
Monte Carlo methods
Nonparametric statistics
Operations Research
Operations Research/Decision Theory
Optimization
Random variables
Sensors
Statistics
Studies
Trajectories
Unmanned aerial vehicles
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Summary:This paper describes an efficient algorithm to find a smooth trajectory joining two points A and B with minimum length constrained to avoid fixed subsets. The basic assumption is that the locations of the obstacles are measured several times through a mechanism that corrects the sensors at each reading using the previous observation. The proposed algorithm is based on the penalized nonparametric method previously introduced that uses confidence ellipses as a fattening of the avoidance set. In this paper we obtain consistent estimates of the best trajectory using Monte Carlo construction of the confidence ellipse.
ISSN:0926-6003
1573-2894
DOI:10.1007/s10589-010-9337-3