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

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

ISBN-10: 3642362788

ISBN-13: 9783642362781

ISBN-10: 3642362796

ISBN-13: 9783642362798

Algorithms are a basic portion of robot structures. robotic algorithms approach inputs from sensors that supply noisy and partial facts, construct geometric and actual types of the area, plan high-and low-level activities at varied time horizons, and execute those activities on actuators with constrained precision. The layout and research of robotic algorithms increase a special mix of questions from many elds, together with regulate concept, computational geometry and topology, geometrical and actual modeling, reasoning less than uncertainty, probabilistic algorithms, video game thought, and theoretical laptop science.

The Workshop on Algorithmic Foundations of Robotics (WAFR) is a single-track assembly of top researchers within the eld of robotic algorithms. for the reason that its inception in 1994, WAFR has been held some other yr, and has supplied one of many optimum venues for the e-book of a few of the eld's most vital and lasting contributions.

This books includes the lawsuits of the 10th WAFR, hung on June 13{15 2012 on the Massachusetts Institute of expertise. The 37 papers integrated during this e-book disguise a wide variety of subject matters, from basic theoretical matters in robotic movement making plans, keep watch over, and belief, to novel applications.

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MCTS in turn is limited by the branching factor and sparsity of rewards in the NAMO domain. In the next section we present a new algorithm that solves these shortcomings by combining the two approaches. Hierarchical Decision Theoretic Planning for NAMO 27 5 Algorithm In this section we outline how to construct an MDP for the NAMO problem, and how to solve it by combining the tools from MAX-Q and MCTS described above. Recall that a standard MDP has an action model associated with each state. 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.

Multi-step motion planning for free-climbing robots. In: Workshop on the Algorithmic Foundations of Robotics, Zeist, Netherlands (2004) 3. : A framework for push-grasping in clutter. In: Robotics: Science and Systems (2011) 4. : Fully dynamic algorithms for maintaining shortest paths trees. Journal of Algorithms 34(2), 251–281 (2000) 5. : Coming up with good excuses: What to do when no plan can be found. In: Int. Conf. on Automated Planning and Scheduling (2010) 6. : Path planning in expansive configuration spaces.

Our algorithm (Algorithm 1), therefore takes the following input: 1. The set O of obstacles present in the workspace 2. Distributions P(ci |o j ) representing the probability of obstacle o j belonging to category ci 3. Motion models indicating 2D object displacements, P(δ x, δ y|a1ll , c1 ) . . P(δ x, δ y|akll , cm ), indexed by action and object category 4. Cgoal The robot’s goal configuration in the workspace It outputs: 1. A high-level policy π0 indicating which obstacle to move for each free space 2.

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Algorithmic Foundations of Robotics X: Proceedings of the Tenth Workshop on the Algorithmic Foundations of Robotics by Kris Hauser (auth.), Emilio Frazzoli, Tomas Lozano-Perez, Nicholas Roy, Daniela Rus (eds.)

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