Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Environment Learning for Indoor Mobile Robots: A Stochastic State Estimation Approach to Simultaneous Localization and Map Building (Springer Tracts in Advanced Robotics)
The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models
The Journal of Machine Learning Research
Onsager-Casimir antireciprocity relations for the hall gyrators analysis
WSEAS TRANSACTIONS on SYSTEMS
Markov approach of adaptive task assignment for robotic system in non-stationary environments
WSEAS TRANSACTIONS on SYSTEMS
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Flexible and adaptive behavior of mobile robots is characterized by context-awareness and the ability to reason using uncertain and imprecise information. A coherent behaviour of mobile robots placed multiple real-time design requirements on the controller, sensors, and actuators, at both hardware and software levels. In the context of use, perceptual-oriented capabilities more dynamically adapt to robot resources are a principle that drives mobile robotics to plans its sensor and effectors actions. The unknown, hidden variables in the mobile robotics can be model by the means of probabilistic inference that take into account incomplete and uncertain information. A sensing plan for mobile robots is enviable based on sensor nodes that consist of sharing small, inexpensive, and robustly inter-networked sensors. The paper investigates the methodology of network for deployment of combined proximity sensors as a localization method for robotic systems in structured and dynamic environments. Based on the property that Hall Effect sensors can detect the proximity of ferromagnetic objects a localization method for mobile robots in structured environment is studied. Using the deployment of combined proximity sensors as a localization method, the dynamic environments are explored by means of Bayesian belief network.