Titlebook: Algorithmic Foundations of Robotics XI; Selected Contributio H. Levent Akin,Nancy M. Amato,A. Frank Stappen Book 2015 Springer Internationa

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Eigene Untersuchungen: Studie 3are particularly challenging to model, as their motion patterns are often uncertain and/or unknown .. This paper presents a novel changepoint detection and clustering algorithm that, when coupled with offline unsupervised learning of a Gaussian process mixture model (DPGP), enables quick detection o

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Einflussfaktoren und Lebenstil,ature transform and correspondence computations but also provide the motion information for both the environment and moving obstacles. This enables us to develop a new framework that is capable of simultaneous localization, scene mapping, and moving obstacle tracking. This method first extracts plan

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Christian Simhandl,Klaudia Mitterwachauernext sensing location using all information acquired so far. The goal is to minimize the robot’s travel cost required to identify a true hypothesis. Adaptive IPP is NP-hard. This paper presents . (RAId), a new polynomial-time approximation algorithm for adaptive IPP. We prove a polylogarithmic appro

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https://doi.org/10.1007/978-3-211-49297-0f the robots’ free space to change. This paper introduces a data structure, the Feasible Transition Graph (FTG), and algorithms that solve such complex motion planning problems. We define an equivalence relation over object configurations based on the robots’ free space connectivity. Within an equiv