Monte Carlo algorithm for trajectory optimization based on Markovian readings
نویسندگان
چکیده
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.
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عنوان ژورنال:
- Comp. Opt. and Appl.
دوره 51 شماره
صفحات -
تاریخ انتشار 2012