A Semantic Map for Indoor Robot Navigation Based on Predicate Logic

2020 ◽  
Vol 11 (1) ◽  
pp. 1-21
Author(s):  
Hengsheng Wang ◽  
Jin Ren

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.

2012 ◽  
Vol 3 (2) ◽  
Author(s):  
Daniel N. Cassenti ◽  
Troy D. Kelley ◽  
Rosemarie E. Yagoda ◽  
Eric Avery

AbstractThe capability to use voice commands to control robots offer an intriguing possibility to increase the efficiency with which robotic operators may give commands. This study consists of two experiments that investigate how robot operators prefer to speak to robots in a search-and-find task and to evaluate which mode of speaking generates the greatest performance. Experiment 1 revealed that operators used selective exocentric references when available to direct a confederate acting as a robot. Experiment 2 revealed that with the same exocentric references operators showed improved performance while directing an actual robot as compared with egocentric-only commands. Experiment 2 also revealed that performing a dual task was less detrimental to performance when using exocentric commands as compared to egocentric commands. Suggestions for improvements to a robot control system that follow from these results include developing recognition of structural properties of an indoor environment and improving map incorporation.


2018 ◽  
Vol 24 (2) ◽  
pp. 986-989
Author(s):  
A. A Dahalan ◽  
A Saudi ◽  
J Sulaiman ◽  
W. R. W Din

Author(s):  
Tunahan Altintop ◽  
Ronald R. Yager ◽  
Diyar Akay ◽  
Fatih Emre Boran ◽  
Muhammet Ünal

It is now well recognized that knowledge extracted from rich healthcare data play a vital role for delivery, management and planning of healthcare services. So far, however, there is not much study done on the domain of operational and financial healthcare data since, up to now, a great deal of works are dedicated to clinical/medical healthcare data for the purposes of diagnosis and treatment of diseases. In this paper, an attempt is made, by applying fuzzy linguistic summarization, for the first time to discover knowledge from operational and financial healthcare data. Fuzzy linguistic summarization, in its simplest term, provides natural language based summaries from a dataset in a human consistent way along with a degree of truth attached to each summary. While basically valuable, its benefit can be increased by only generating summaries with a degree of truth above than an indicated threshold value. A genetic algorithm is developed within this context in order to eliminate less promising and useless linguistic summaries. We assess the proposed approach experimentally on a real data and evaluate the generated summaries to gain actionable insights from them.


Author(s):  
Wenjun Zhang ◽  
Qiao Zhang ◽  
Kai Sun ◽  
Sheng Guo

A novel Laser-SLAM algorithm is presented for real indoor environment mobile mapping. SLAM algorithm can be divided into two classes, Bayes filter-based and graph optimization-based. The former is often difficult to guarantee consistency and accuracy in largescale environment mapping because of the accumulative error during incremental mapping. Graph optimization-based SLAM method often assume predetermined landmarks, which is difficult to be got in unknown environment mapping. And there most likely has large difference between the optimize result and the real data, because the constraints are too few. This paper designed a kind of sub-map method, which could map more accurately without predetermined landmarks and avoid the already-drawn map impact on agent’s location. The tree structure of sub-map can be indexed quickly and reduce the amount of memory consuming when mapping. The algorithm combined Bayes-based and graph optimization-based SLAM algorithm. It created virtual landmarks automatically by associating data of sub-maps for graph optimization. Then graph optimization guaranteed consistency and accuracy in large-scale environment mapping and improved the reasonability and reliability of the optimize results. Experimental results are presented with a laser sensor (UTM 30LX) in official buildings and shopping centres, which prove that the proposed algorithm can obtain 2D maps within 10cm precision in indoor environment range from several hundreds to 12000 square meter.


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