By H. Levent Akin, Nancy M. Amato, Volkan Isler, A. Frank van der Stappen

This rigorously edited quantity is the end result of the 11th version of the Workshop on Algorithmic Foundations of Robotics (WAFR), that is the top-rated venue showcasing innovative learn in algorithmic robotics. The 11th WAFR, which was once held August 3-5, 2014 at Boğaziçi collage in Istanbul, Turkey persisted this practice. This quantity comprises prolonged types of the forty two papers offered at WAFR. those contributions spotlight the leading edge learn in classical robotics difficulties (e.g. manipulation, movement, course, multi-robot and kinodynamic planning), geometric and topological computation in robotics in addition novel purposes akin to informative course making plans, lively sensing and surgical making plans. This publication - wealthy by way of subject matters and authoritative individuals - is a different reference at the present advancements and new instructions within the box of algorithmic foundations.

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Extra info for Algorithmic Foundations of Robotics XI: Selected Contributions of the Eleventh International Workshop on the Algorithmic Foundations of Robotics

Example text

Namely, working with the 2-means divisive hierarchical clustering method [30], HC2-means , given a hierarchy ˚ (τ ) we construct a pair of vector fields, f y , f s(y) ∈ τ ∈BT J and an interior goal, y∈S FHC (τ ) with the following properties. The goal field, f y , has y as a point attractor and includes in its basin a neighborhood of a suitably well separated and compactly clustered “standard” exemplar, s (y) ∈ S (τ ). The global field, f s(y) has s (y) as a point attractor and includes in its basin a set Sz (τ ) ⊂ S (τ ) that excludes at most a zero measure subset of S (τ ).

Root B I −τ Ch (I, τ ) I 5 6 σ A ) ,A B) (τ , P r (I, τ ) C (γ interior node leaf node (τ , C) (σ ,A ) τ γ (γ, C) 1 2 3 4 7 8 9 10 11 12 13 A B C (σ, B) A C B Fig. 1 (Left) Hierarchical relations: parent—Pr (I, τ ), children—Ch (I, τ ), and local complement (sibling)—I −τ of cluster I of a rooted binary tree, τ ∈ BT[13] . An interior node is referred by its cluster, the list of leaves below it; for example, I = {4, 5, 6, 7}. info Navigation of Distinct Euclidean Particles via Hierarchical Clustering (a) 2 (c) 3 6 1 0 -4 3 -2 2 4 = 1 4 5 0 = 2 2 -2 23 6 0 x1 x2 = −1 (b) 6 3 5 2 4 −3 −3 2 3 1 3 2 3 1 2 = = = = −2 1 1 −2 −1 0 1 2 3 + + + + + 4 Fig.

That is to say, a pair of hierarchies, σ, τ ∈ BT J , is connected with an edge in EA if and only if their strata intersect, S (σ) ∩ S (τ ) = ∅. The adjacency graph is a central object of interest in this paper; however, as Fig. 2c anticipates, HC strata generally have complicated shapes, making it usually hard to compute the complete adjacency graph. Fortunately, the computational biology literature [16] offers an alternative notion of adjacency that turns out to be both feasible and nicely compatible with our needs, yielding a computationally effective, fully connected subgraph of the adjacency graph, A J , as follows.

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