scholarly journals Global localization and topological map-learning for robot navigation

2002 ◽  
Keyword(s):  
Robot Navigation ◽  
Global Localization ◽  
Topological Map ◽  
Map Learning

Perception-action map learning in controlled multiscroll systems applied to robot navigation

2008 ◽  
Vol 18 (4) ◽  
pp. 043119 ◽  
Author(s):  
Paolo Arena ◽  
Sebastiano De Fiore ◽  
Luigi Fortuna ◽  
Luca Patané
Keyword(s):  
Robot Navigation ◽  
Map Learning ◽  
Perception Action

A Semantic Map for Indoor Robot Navigation Based on Predicate Logic

2020 ◽  
Vol 11 (1) ◽  
pp. 1-21
Author(s):  
Hengsheng Wang ◽  
Jin Ren
Keyword(s):  
Natural Language ◽  
Indoor Environment ◽  
Robot Navigation ◽  
Predicate Logic ◽  
Constraint Satisfaction Problem ◽  
Real Data ◽  
Main Concern ◽  
Building Structure ◽  
Topological Map ◽  
Semantic Map

Interacting with mobile robots through natural language is the main concern of this article, which focuses on the semantic meaning of concepts used in natural language instructions to navigate robots indoors. Assuming the building structure is the prior knowledge of the robot and the robot has the ability of navigating itself locally to avoid collision with the environment, the building structure is represented with predicate logic on SWI-Prolog as the database of the indoor environment, which is called semantic map in this paper, in which the basic predicate clauses are based on two kinds of entities, namely ‘area' and ‘node.' The area names (in natural language convention) of indoor environment are organized with an ontology and are defined in the semantic map which includes the geometric information of areas and connection relationships between areas. With the semantic map database, functions for robot navigation, like a topological map, path planning, and self-localization, are realized through reasoning by properly designed predicates based on constraint satisfaction problem (CSP). An example building is given to show the idea proposed in this article, the real data of which was used to establish the semantic map, and the predicates for navigation functions worked well on SWI-Prolog.

scholarly journals Cooperative Exploration by Multi-Robots without Global Localization

10.5772/5682 ◽  
2007 ◽  
Vol 4 (3) ◽  
pp. 36 ◽  
Author(s):  
Shi Chao-xia ◽  
Hong Bing-rong ◽  
Wang Yan-qing
Keyword(s):  
Reference Frame ◽  
Mobile Robotics ◽  
Fundamental Problem ◽  
Global Localization ◽  
Scale Invariant ◽  
Topological Map ◽  
Invariant Features ◽  
Unknown Environments ◽  
Efficient Exploration ◽  
Cooperative Exploration

Efficient exploration of unknown environments is a fundamental problem in mobile robotics. We propose a novel topological map whose nodes are represented with the range finder's free beams together with the visual scale-invariant features. The topological map enables teams of robots to efficiently explore environments from different, unknown locations without knowing their initial poses, relative poses and global poses in a certain world reference frame. The experiments of map merging and coordinated exploration demonstrate the proposed map is not only easy for merging, but also convenient for robust and efficient explorations in unknown environments.

scholarly journals Intelligent Robot Navigation using View Sequences and a Sparse Distributed Memory

2010 ◽  
Vol 1 (4) ◽  
Author(s):  
Mateus Mendes ◽  
A. Paulo Coimbra ◽  
Manuel M. Crisóstomo
Keyword(s):  
Associative Memory ◽  
Incomplete Data ◽  
Distributed Memory ◽  
Robot Navigation ◽  
High Dimensional ◽  
Intelligent Robot ◽  
Ability To Work ◽  
Topological Map ◽  
Sparse Distributed Memory

AbstractDifferent approaches have been tried to navigate robots, including those based on visual memories. The Sparse Distributed Memory (SDM) is a kind of associative memory based on the properties of high dimensional binary spaces. It exhibits characteristics such as tolerance to noise and incomplete data, ability to work with sequences and the possibility of one-shot learning. Those characteristics make it appealing to use for robot navigation. The approach presented in this work was to navigate a robot using sequences of visual memories stored into a SDM. The robot makes intelligent decisions, such as selecting only relevant images to store during path learning, adjusting memory parameters to the level of noise and inferring new paths from learnt trajectories. The method of encoding the information may influence the tolerance of the SDM to noise and saturation. The present paper reports novel results of the limits of the model under different typical navigation problems. An algorithm to build a topological map of the environment based on the visual memories is also described.

scholarly journals A CNN-Based System for Mobile Robot Navigation in Indoor Environments via Visual Localization with a Small Dataset

2021 ◽  
Vol 12 (3) ◽  
pp. 134
Author(s):  
Farzin Foroughi ◽  
Zonghai Chen ◽  
Jikai Wang
Keyword(s):  
Deep Learning ◽  
Mobile Robot ◽  
Loss Function ◽  
Input Data ◽  
Robot Navigation ◽  
Mobile Robot Navigation ◽  
Visual Localization ◽  
Generalization Capability ◽  
Feature Maps ◽  
Topological Map

Deep learning has made great advances in the field of image processing, which allows automotive devices to be more widely used in humans’ daily lives than ever before. Nowadays, the mobile robot navigation system is among the hottest topics that researchers are trying to develop by adopting deep learning methods. In this paper, we present a system that allows the mobile robot to localize and navigate autonomously in the accessible areas of an indoor environment. The proposed system exploits the Convolutional Neural Network (CNN) model’s advantage to extract data feature maps for image classification and visual localization, which attempts to precisely determine the location region of the mobile robot focusing on the topological maps of the real environment. The system attempts to precisely determine the location region of the mobile robot by integrating the CNN model and topological map of the robot workspace. A dataset with small numbers of images is acquired from the MYNT EYE camera. Furthermore, we introduce a new loss function to tackle the bounded generalization capability of the CNN model in small datasets. The proposed loss function not only considers the probability of the input data when it is allocated to its true class but also considers the probability of allocating the input data to other classes rather than its actual class. We investigate the capability of the proposed system by evaluating the empirical studies based on provided datasets. The results illustrate that the proposed system outperforms other state-of-the-art techniques in terms of accuracy and generalization capability.

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