Multi-channel Bayesian Adaptive Resonance Associate Memory for on-line topological map building

Exploration of unknown environments is a fundamental problem in autonomous robotics that deals with the complexity of autonomously traversing an unknown area while acquiring the most important information of the environment. In this work, a mobile robot exploration algorithm for indoor environments is proposed. It combines frontier-based concepts with behavior-based strategies in order to build a topological representation of the environment. Frontier-based approaches assume that, to gain the most information of an environment, the robot has to move to the regions on the boundary between open space and unexplored space. The novelty of this work is in the semantic frontier classification and frontier selection according to a cost–utility function. In addition, a probabilistic loop closure algorithm is proposed to solve cyclic situations. The system outputs a topological map of the free areas of the environment for further navigation. Finally, simulated and real-world experiments have been carried out, their results and the comparison to other state-of-the-art algorithms show the feasibility of the exploration algorithm proposed and the improvement that it offers with regards to execution time and travelled distance.

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Global Appearance Applied to Visual Map Building and Path Estimation Using Multiscale Analysis

Mathematical Problems in Engineering ◽

10.1155/2014/365417 ◽

2014 ◽

Vol 2014 ◽

pp. 1-23 ◽

Cited By ~ 1

Author(s):

Francisco Amorós ◽

Luis Payá ◽

Oscar Reinoso ◽

Walterio Mayol-Cuevas ◽

Andrew Calway

Keyword(s):

Pose Estimation ◽

Visual Information ◽

Multiscale Analysis ◽

Spatial Arrangement ◽

Field Of View ◽

Indoor Environments ◽

Map Building ◽

Topological Map ◽

Complete Field ◽

Estimation System

In this work we present a topological map building and localization system for mobile robots based on global appearance of visual information. We include a comparison and analysis of global-appearance techniques applied to wide-angle scenes in retrieval tasks. Next, we define multiscale analysis, which permits improving the association between images and extracting topological distances. Then, a topological map-building algorithm is proposed. At first, the algorithm has information only of some isolated positions of the navigation area in the form of nodes. Each node is composed of a collection of images that covers the complete field of view from a certain position. The algorithm solves the node retrieval and estimates their spatial arrangement. With these aims, it uses the visual information captured along some routes that cover the navigation area. As a result, the algorithm builds a graph that reflects the distribution and adjacency relations between nodes (map). After the map building, we also propose a route path estimation system. This algorithm takes advantage of the multiscale analysis. The accuracy in the pose estimation is not reduced to the nodes locations but also to intermediate positions between them. The algorithms have been tested using two different databases captured in real indoor environments under dynamic conditions.

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Reinforcement and Non-Reinforcement Machine Learning Classifiers for User Movement Prediction

Intelligent Technologies and Techniques for Pervasive Computing - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-4038-2.ch012 ◽

2013 ◽

pp. 218-237 ◽

Cited By ~ 1

Author(s):

Theodoros Anagnostopoulos

Keyword(s):

Machine Learning ◽

Classification Problem ◽

Location Prediction ◽

Context Aware ◽

Resonance Theory ◽

Self Organizing Maps ◽

Adaptive Resonance ◽

Proposed Model ◽

On Line ◽

Mobile Context

Mobile context-aware applications are required to sense and react to changing environment conditions. Such applications, usually, need to recognize, classify, and predict context in order to act efficiently, beforehand, for the benefit of the user. In this chapter, the authors propose a mobility prediction model, which deals with context representation and location prediction of moving users. Machine Learning (ML) techniques are used for trajectory classification. Spatial and temporal on-line clustering is adopted. They rely on Adaptive Resonance Theory (ART) for location prediction. Location prediction is treated as a context classification problem. The authors introduce a novel classifier that applies a Hausdorff-like distance over the extracted trajectories handling location prediction. Two learning methods (non-reinforcement and reinforcement learning) are presented and evaluated. They compare ART with Self-Organizing Maps (SOM), Offline kMeans, and Online kMeans algorithms. Their findings are very promising for the use of the proposed model in mobile context aware applications.

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