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This e-book comprises the complaints from the 2006 Workshop at the Algorithmic Foundations of Robotics. This biannual workshop is a hugely selective assembly of major researchers within the box of algorithmic matters concerning robotics. The 32 papers during this ebook span a large choice of themes: from primary movement making plans algorithms to purposes in drugs and biology, yet they've got in universal a starting place within the algorithmic difficulties of robot systems.
Read or Download Algorithmic Foundation of Robotics VII: Selected Contributions of the Seventh International Workshop on the Algorithmic Foundations of Robotics PDF
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Extra info for Algorithmic Foundation of Robotics VII: Selected Contributions of the Seventh International Workshop on the Algorithmic Foundations of Robotics
4, 9, 12, 16, 17, 18, 21, 22]), narrow passages remain a bottleneck for PRM planning. With few exceptions, most PRM planners use static sampling distributions based on a priori assumed geometric properties of the conﬁguration space or the workspace. Interestingly, the ﬁrst PRM planner , which consists of two sampling stages, uses dynamic sampling: the second stage exploits information gathered in the ﬁrst stage to update the sampling distribution and resample C. Recently, with the use of machine learning techniques in PRM planning [5, 13, 19], dynamic sampling has again gained popularity.
We use the chosen si to sample a new milestone m and assign to si a reward r that depends on the eﬀect of m on the roadmap R: • The milestone m reduces the number of connected components of R. In this case, m merges two or more connected components and improves its connectivity. We set r = 1. • The milestone m increases the number of connected components of R. In this case, m creates a new connected component and potentially improves the coverage of R. We also set r = 1. • Otherwise, r = 0. We then update the weight of si : wi (t + 1) = w i (t) exp ((r/pi )η/K) .
To adapt the ensemble distribution, we adjust the weights so that the component samplers with better performance have higher weights. See Algorithm 2 for an outline of the algorithm. In iteration t of Algorithm 2, we choose si with probability η w i (t) (1) + , pi = (1 − η) K−1 K i=0 wi (t) where w i (t) is the weight of si in iteration t and η ∈ (0, 1] is a small ﬁxed constant. We use the chosen si to sample a new milestone m and assign to si a reward r that depends on the eﬀect of m on the roadmap R: • The milestone m reduces the number of connected components of R.