A fundamental issue for detecting rare event or small probability event is to generate the description for the tail probability distribution of the sensed data. Tail quantile can effectively describe the tail probabil...
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ISBN:
(纸本)9783662469811;9783662469804
A fundamental issue for detecting rare event or small probability event is to generate the description for the tail probability distribution of the sensed data. Tail quantile can effectively describe the tail probability distribution. However, most of the existing works focus on the computation of approximate quantile summary, and they are inefficient in calculating tail quantile. This paper develops an algorithmbased on sampling technique for computing approximate tail quantile, such that the approximate result can satisfy the requirement of given precision. A more accurate estimator is given first. For given upper bound of relative error, this algorithm satisfies that the probability of the relative error between the exact tail quantile and the returned approximate result being larger than the specified upper bound is smaller than the given failure probability. Experiments are carried out to show the correctness and effectiveness of the proposed algorithms.
This paper proposes a path planning algorithm called guiding attraction based random tree (GART), which is built upon the famous sampling-basedalgorithm RRT* to generate a near optimal path in real time for unmanned ...
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ISBN:
(纸本)9781479939794
This paper proposes a path planning algorithm called guiding attraction based random tree (GART), which is built upon the famous sampling-basedalgorithm RRT* to generate a near optimal path in real time for unmanned aerial vehicle (UAV) navigation under uncertainty. The algorithm takes UAV heading dynamic constraint and 'obstacle safe attraction' into consideration, and uses a descriptive set method to describe the uncertainty caused by control and sensing error. The analysis shows that the computational complexity of GART is within a constant factor of RRT* and RRT. A number of detailed comparisons of the proposed algorithm with RRT* in 2D are given which verify the efficiency of our algorithm. Moreover, 3D simulation results demonstrate that GART find the near optimal path only after 2400 iterations, which means that GART outperformed RRT* by 833%.
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