Indoor Navigation Using A* Algorithm

Kasim, Shahreen; Loh, Yin Xia; Wahid, Norfaradilla; Fudzee, Mohd Farhan Md; Mahdin, Hairulnizam; Ramli, Azizul Azhar; Suparjoh, Suriawati; Salamat, Mohamad Aizi

doi:10.1007/978-3-319-51281-5_60

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…From here, a performance analysis of the OAN algorithm is done. At the time of writing, there have been multiple international efforts to provide meaningful benchmarks for various UAV OAN algorithms [34,35,36]. However, not one of them has been agreed upon to suitably benchmark UAV OAN algorithms.…”

Section: Resultsmentioning

confidence: 99%

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COAA* — An Optimized Obstacle Avoidance and Navigational Algorithm for UAVs Operating in Partially Observable 2D Environments

2021

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Obstacle avoidance and navigation (OAN) algorithms typically employ offline or online methods. The former is fast but requires knowledge of a global map, while the latter is usually more computationally heavy in explicit solution methods, or is lacking in configurability in the form of artificial intelligence (AI) enabled agents. In order for OAN algorithms to be brought to mass produced robots, more specifically for multirotor unmanned aerial vehicles (UAVs), the computational requirement of these algorithms must be brought low enough such that its computation can be done entirely onboard a companion computer, while being flexible enough to function without a prior map, as is the case of most real life scenarios. In this paper, a highly configurable algorithm, dubbed Closest Obstacle Avoidance and A* (COAA*), that is lightweight enough to run on the companion computer of the UAV is proposed. This algorithm frees up from the conventional drawbacks of offline and online OAN algorithms, while having guaranteed convergence to a global minimum. The algorithms have been successfully implemented on the Heavy Lift Experimental (HLX) UAV of the Autonomous Robots Research Cluster in Taylor’s University, and the simulated results match the real results sufficiently to show that the algorithm has potential for widespread implementation.

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Section: Resultsmentioning

confidence: 99%

“…Therefore, we employ a modified A* algorithm which includes a herein called 'danger' cost. Interested readers are encouraged to explore the its formulation [34]. This cost denotes the proximity of connected paths to the obstacles around it.…”

Section: A* Search Integrationmentioning

confidence: 99%

COAA* — An Optimized Obstacle Avoidance and Navigational Algorithm for UAVs Operating in Partially Observable 2D Environments

2021

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“…A* algorithm uses a statistical function to determine a better path, while Dijkstra's algorithm considers all feasible routes (Kasim et al, 2016). EBS-A* algorithm reduced the number of important nodes by 91.89 %, the number of right-angle turns by 100 %, and the efficiency of path planning by 278 %.…”

Section: Discussion and Analysis Of Shortest-path Algorithmsmentioning

confidence: 99%

A Review: Current Trend of Immersive Technologies for Indoor Navigation and the Algorithms

Sariman,

Othman,

Mat Akir

et al. 2024

JST

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The term “indoor navigation system” pertains to a technological or practical approach that facilitates the navigation and orientation of individuals within indoor settings, such as museums, airports, shopping malls, or buildings. Over several years, significant advancements have been made in indoor navigation. Numerous studies have been conducted on the issue. However, a fair evaluation and comparison of indoor navigation algorithms have not been discussed further. This paper presents a comprehensive review of collective algorithms developed for indoor navigation. The in-depth analysis of these articles concentrates on both advantages and disadvantages, as well as the different types of algorithms used in each article. A systematic literature review (SLR) methodology guided our article-finding, vetting, and grading processes. Finally, we narrowed the pool down to 75 articles using SLR. We organized them into several groups according to their topics. In these quick analyses, we pull out the most important concepts, article types, rating criteria, and the positives and negatives of each piece. Based on the findings of this review, we can conclude that an efficient solution for indoor navigation that uses the capabilities of embedded data and technological advances in immersive technologies can be achieved by training the shortest path algorithm with a deep learning algorithm to enhance the indoor navigation system.

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“…Comparativa de Google maps indoor y nuestra propuesta: NavIndoor. El siguiente trabajo de investigación presenta el desarrollo de un sistema de navegación en interiores usando la tecnología GPS [13], el cual fue desarrollado por la empresa Google, este sistema de navegación está incorporado a la aplicación móvil de Google maps que ya es actualmente utilizada por la mayoría de la gente para buscar lugares o nos ayude a llegar a un destino. Esta aplicación funciona con solo acercarse al edificio, una vez dado el primer paso se muestra un mapa del edificio donde se puede observar aquellos lugares de interés, navegar a través del edificio.…”

Section: Trabajos Relacionadosunclassified

Navegación en interiores utilizando la tecnología beacons

Rebollar¹,

López-García²,

Estrada-Esquivel³

et al. 2019

RCS

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Resumen. El internet de las cosas (IoT por sus siglas en inglés) está impulsando la generación de edificios inteligentes. Estos edificios tienen como requisito principal la navegación de interiores. Sin embargo, la tecnología GPS, la cual es utilizada para llevar a cabo el posicionamiento, no es posible utilizarla dentro de un edificio debido a que las señales satelitales no viajan bien a través de materiales sólidos. De esta manera, se han propuesto sensores, dispositivos Bluetooth, o RFID que se utilizan en los edificios inteligentes. En este artículo presentamos una propuesta de navegación de interiores, la cual utiliza la tecnología beacons y los teléfonos inteligentes. Nuestra aplicación de software obtiene información del contexto y genera la mejor ruta para llegar al destino dentro de un edificio inteligente. Se utilizó el algoritmo Dijkstra para procesar toda la información. De ahí que nuestra propuesta tiene como objetivo combinar diferentes tecnologías, y adaptar algoritmos desarrollados a la navegación de interiores.Palabras clave: navegación de interiores, tecnología beacons, edificio inteligente, internet de las cosas.

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Indoor Navigation Using A* Algorithm

Cited by 7 publications

References 5 publications

COAA* — An Optimized Obstacle Avoidance and Navigational Algorithm for UAVs Operating in Partially Observable 2D Environments

COAA* — An Optimized Obstacle Avoidance and Navigational Algorithm for UAVs Operating in Partially Observable 2D Environments

A Review: Current Trend of Immersive Technologies for Indoor Navigation and the Algorithms

Navegación en interiores utilizando la tecnología beacons

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