Real time indoor intelligent navigation system inside hypermarkets

Khalifa, Ismahene Hadj; Kamel, Abdelkader El; Barfety, Bernard

doi:10.3182/20100712-3-fr-2020.00076

Cited by 9 publications

(2 citation statements)

References 2 publications

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“…However, the model ran on a limited CAD system and doesn't offer the possibility of training considering multiple goal structures. Moreover, there are studies that, although considering human factors in the design of their indoor navigation system, such as disabled or elderly and blind people (Khalifa, El Kamel and Barfety, 2010;Blattner, Vasilev and Harriehausen-Mühlbauer, 2015;Tsirmpas et al, 2015), they do not address emergency cases. Atila et al (Atila et al, 2018) studied the combination of different human factors extensively; however they used building surveying devices for the building case study which can be more expensive and cumbersome than initiating a computer-based design model which can also benefit and be updated from the simulation feedback resulting in an optimised design.…”

Section: Previous Researchmentioning

confidence: 99%

The synergy of non-manifold topology and reinforcement learning for fire egress

Jabi¹,

Chatzivasileiadi²,

Wardhana³

et al. 2019

Proceedings of the 37th International Conference on Education and Research in Computer Aided Architectural Design in Europe (eC

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This paper illustrates the synergy of non-manifold topology (NMT) and a branch of artificial intelligence and machine learning (ML) called reinforcement learning (RL) in the context of evaluating fire egress in the early design stages. One of the important tasks in building design is to provide a reliable system for the evacuation of the users in emergency situations. Therefore, one of the motivations of this research is to provide a framework for architects and engineers to better design buildings at the conceptual design stage, regarding the necessary provisions in emergency situations. This paper presents two experiments using different state models within a simplified game-like environment for fire egress with each experiment investigating using one vs. three fire exits. The experiments provide a proof-of-concept of the effectiveness of integrating RL, graphs, and non-manifold topology within a visual data flow programming environment. The results indicate that artificial intelligence, machine learning, and RL show promise in simulating dynamic situations as in fire evacuations without the need for advanced and time-consuming simulations.

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Section: Previous Researchmentioning

confidence: 99%

The synergy of non-manifold topology and reinforcement learning for fire egress

Jabi¹,

Chatzivasileiadi²,

Wardhana³

et al. 2019

Proceedings of the 37th International Conference on Education and Research in Computer Aided Architectural Design in Europe (eC

View full text Add to dashboard Cite

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“…Even though a number of research works has taken individual features of evacuees into account, those features are not sufficient for a proper navigation. While Khalifa et al (2010) proposed an indoor navigation system to guide handicapped people in hypermarkets [9], Tsirmpas et al (2015) designed an elderly and blind people-oriented indoor navigation system [10]. Blattner et al (2015) developed an indoor navigation system, which uses a Bluetooth signal transmitter, to assist the handicapped people in a building [11].…”

Section: Related Workmentioning

confidence: 99%

SmartEscape: A Mobile Smart Individual Fire Evacuation System Based on 3D Spatial Model

Atila

Ortakcı

Özacar

et al. 2018

IJGI

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We propose SmartEscape, a real-time, dynamic, intelligent and user-specific evacuation system with a mobile interface for emergency cases such as fire. Unlike past work, we explore dynamically changing conditions and calculate a personal route for an evacuee by considering his/her individual features. SmartEscape, which is fast, low-cost, low resource-consuming and mobile supported, collects various environmental sensory data and takes evacuees' individual features into account, uses an artificial neural network (ANN) to calculate personal usage risk of each link in the building, eliminates the risky ones, and calculates an optimum escape route under existing circumstances. Then, our system guides the evacuee to the exit through the calculated route with vocal and visual instructions on the smartphone. While the position of the evacuee is detected by RFID (Radio-Frequency Identification) technology, the changing environmental conditions are measured by the various sensors in the building. Our ANN (Artificial Neural Network) predicts dynamically changing risk states of all links according to changing environmental conditions. Results show that SmartEscape, with its 98.1% accuracy for predicting risk levels of links for each individual evacuee in a building, is capable of evacuating a great number of people simultaneously, through the shortest and the safest route.

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