Algorithmic Foundations of Robotics X: Proceedings of the by Kris Hauser (auth.), Emilio Frazzoli, Tomas Lozano-Perez,

By Kris Hauser (auth.), Emilio Frazzoli, Tomas Lozano-Perez, Nicholas Roy, Daniela Rus (eds.)

Algorithms are a primary section of robot structures. robotic algorithms approach inputs from sensors that supply noisy and partial facts, construct geometric and actual versions of the area, plan high-and low-level activities at diverse time horizons, and execute those activities on actuators with restricted precision. The layout and research of robotic algorithms bring up a distinct blend of questions from many elds, together with keep watch over concept, computational geometry and topology, geometrical and actual modeling, reasoning less than uncertainty, probabilistic algorithms, online game idea, and theoretical computing device science.

The Workshop on Algorithmic Foundations of Robotics (WAFR) is a single-track assembly of best researchers within the eld of robotic algorithms. given that its inception in 1994, WAFR has been held another 12 months, and has supplied one of many finest venues for the booklet of a few of the eld's most vital and lasting contributions.

This books comprises the court cases of the 10th WAFR, hung on June 13{15 2012 on the Massachusetts Institute of expertise. The 37 papers integrated during this publication conceal a huge variety of subject matters, from primary theoretical concerns in robotic movement making plans, keep an eye on, and belief, to novel applications.

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2 Type 2 criticalities: at critical angle (a), between (b), at critical angle (c) convolution [7] of θ (−M) and F. The convolution is the set of points, t, such that θ M + t has a local contact with F. It subdivides the plane into open regions: some regions comprise the free space and the others comprise the blocked space. Fig. 1a depicts an oval robot inside a dome-shaped room. The obstacle is the complement of the room. The convolution edges appear above. For t ∈ c ⊕ −a, denoted c − a, a + t contacts c, and similarly for b and d.

For the NAMO domain, this implies a displacement model for each object, which depends on the action a being executed, and the target obstacle o: δ x, δ y ∼ P(δ x, δ y|a, o) (5) In our case, however, we represent action models for a discrete set C of object categories. This allows us to incorporate observation uncertainty. Now instead of knowing the action model for an obstacle with certainty, the robot has a belief distribution P(c|o) over categories of action models. The actual displacement model is consequently the marginal over the categories: δ x, δ y ∼ ∑ P(δ x, δ y|a, c)P(c|o) (6) c P(c|o) can be used to encode any uncertainty the robot might have about the category of object it is manipulating.

This allows us to incorporate observation uncertainty. Now instead of knowing the action model for an obstacle with certainty, the robot has a belief distribution P(c|o) over categories of action models. The actual displacement model is consequently the marginal over the categories: δ x, δ y ∼ ∑ P(δ x, δ y|a, c)P(c|o) (6) c P(c|o) can be used to encode any uncertainty the robot might have about the category of object it is manipulating. For example, P(cut |o) could be the posterior probability of obstacle o being an unlocked table cut given some sensor data D: P(cut |D) ∝ P(D|cut )P(cut ).

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