Proceedings ArticleDOI
Acting under uncertainty: discrete Bayesian models for mobile-robot navigation
Anthony R. Cassandra,Leslie Pack Kaelbling,J.A. Kurien +2 more
- Vol. 2, pp 963-972
TLDR
The optimal solution to the problem of how actions should be chosen is presented, formulated as a partially observable Markov decision process, which goes on to explore a variety of heuristic control strategies.Abstract:
Discrete Bayesian models have been used to model uncertainty for mobile-robot navigation, but the question of how actions should be chosen remains largely unexplored. This paper presents the optimal solution to the problem, formulated as a partially observable Markov decision process. Since solving for the optimal control policy is intractable, in general, it goes on to explore a variety of heuristic control strategies. The control strategies are compared experimentally, both in simulation and in runs on a robot.read more
Citations
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Sequential Monte Carlo methods in practice
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Planning Algorithms: Introductory Material
TL;DR: This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms, into planning under differential constraints that arise when automating the motions of virtually any mechanical system.
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Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
Alejandro Barredo Arrieta,Natalia Díaz-Rodríguez,Javier Del Ser,Javier Del Ser,Adrien Bennetot,Adrien Bennetot,Siham Tabik,Alberto Barbado,Salvador García,Sergio Gil-Lopez,Daniel Molina,Richard Benjamins,Raja Chatila,Francisco Herrera +13 more
TL;DR: In this paper, a taxonomy of recent contributions related to explainability of different machine learning models, including those aimed at explaining Deep Learning methods, is presented, and a second dedicated taxonomy is built and examined in detail.
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Robust Monte Carlo localization for mobile robots
TL;DR: A more robust algorithm is developed called MixtureMCL, which integrates two complimentary ways of generating samples in the estimation of Monte Carlo Localization algorithms, and is applied to mobile robots equipped with range finders.
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Principles of Robot Motion: Theory, Algorithms, and Implementations
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TL;DR: In this paper, the mathematical underpinnings of robot motion are discussed and a text that makes the low-level details of implementation to high-level algorithmic concepts is presented.
References
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Proceedings ArticleDOI
High resolution maps from wide angle sonar
Hans P. Moravec,Alberto Elfes +1 more
TL;DR: The use of multiple wide-angle sonar range measurements to map the surroundings of an autonomous mobile robot deals effectively with clutter, and can be used for motion planning and for extended landmark recognition.
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The Optimal Control of Partially Observable Markov Processes over a Finite Horizon
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