Algorithmic Foundation of Robotics VII: Selected by Erion Plaku, Lydia E. Kavraki (auth.), Srinivas Akella,

By Erion Plaku, Lydia E. Kavraki (auth.), Srinivas Akella, Nancy M. Amato, Wesley H. Huang, Bud Mishra (eds.)

Algorithms are a primary portion of robot platforms: they keep watch over or cause approximately movement and notion within the actual global. They obtain enter from noisy sensors, ponder geometric and actual constraints, and function at the global via vague actuators. The layout and research of robotic algorithms consequently increases a different blend of questions on top of things concept, computational and differential geometry, and machine science.

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.

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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

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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 [16], 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.

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