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» Randomized model predictive control for robot navigation
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ESANN
2001
15 years 4 months ago
A divide-and-conquer learning architecture for predicting unknown motion
Time varying environments or model selection problems lead to crucial dilemmas in identification and control science. In this paper, we propose a modular prediction scheme consisti...
Patrice Wira, Jean-Philippe Urban, Julien Gresser
AROBOTS
2008
158views more  AROBOTS 2008»
15 years 3 months ago
Learning traversability models for autonomous mobile vehicles
Autonomous mobile robots need to adapt their behavior to the terrain over which they drive, and to predict the traversability of the terrain so that they can effectively plan thei...
Michael Shneier, Tommy Chang, Tsai Hong, William P...
ICRA
2009
IEEE
126views Robotics» more  ICRA 2009»
15 years 1 months ago
Time-bounded lattice for efficient planning in dynamic environments
For vehicles navigating initially unknown cluttered environments, current state-of-the-art planning algorithms are able to plan and re-plan dynamically-feasible paths efficiently a...
Aleksandr Kushleyev, Maxim Likhachev
ICRA
2009
IEEE
170views Robotics» more  ICRA 2009»
15 years 10 months ago
Path diversity is only part of the problem
— The goal of motion planning is to find a feasible path that connects two positions and is free from collision with obstacles. Path sets are a robust approach to this problem i...
Ross A. Knepper, Matthew T. Mason
CORR
2004
Springer
218views Education» more  CORR 2004»
15 years 3 months ago
A Dynamic Clustering-Based Markov Model for Web Usage Mining
Markov models have been widely utilized for modelling user web navigation behaviour. In this work we propose a dynamic clustering-based method to increase a Markov model's ac...
José Borges, Mark Levene