Intelligent Maze Solving Robot Based on Image Processing and Graph Theory Algorithms

Aqel, Mohammad O. A.; Issa, Ahmed; Khdair, Mohammed; ElHabbash, Majde; AbuBaker, Mohammed; Massoud, Mohammed A. M.

doi:10.1109/icpet.2017.15

Cited by 17 publications

(10 citation statements)

References 12 publications

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“…Recent advances in image processing and artificial intelligence has been utilized to build smarter robots in the maze. For example, authors in [13] have used an intelligent maze solving robot that can determine its shortest path on a line maze based on image processing and artificial intelligence algorithms. The image of the line maze is captured by a camera and sent to the computer to be analyzed and processed by a program and based on graph theory algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…The best shortest path is instructed to the robot to reach its desired destination point through Bluetooth. The authors in [13] have claimed that the solution works faster than the traditional methods which push the robot to move through the maze cell by cell in order to find its destination point. The research in [14] has solved the maze by upgrading the line maze solving algorithm (an algorithm used to solve a maze made of lines to be traced by a mobile robot) by using the curved and zigzag turns.…”

Section: Related Workmentioning

confidence: 99%

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Optimized Path Planning in Reinforcement Learning by Backtracking

Kiadi

Tan

Villar

2019

CTCSA

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This paper focuses on finding the shortest path in a controlled indoor environment, called "smart lab". This study proposes a faster pathfinding model based on optimizing the decision-making process and fitting the hyper-parameters. At the same time, the study includes a comparison of performing the path planning on the robot through fog computing. The solution components are designed around the four main subcomponents of the reinforcement learning systems [1]: policy, reward signal, value function, and

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Optimized Path Planning in Reinforcement Learning by Backtracking

Kiadi

Tan

Villar

2019

CTCSA

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“…Generally, there are two kinds of maze traversing classifications: 1) a model-based algorithm, whereby the objects are known beforehand; and 2) a sensory information-based or learning algorithm, which recognizes entirely unknown objects. The sensory information-based robot learns its environment by evaluating the data provided by the sensors [8]. There are many kinds of sensors that use in the design of robots such as tactile sensors, visual sensors, and laser sensors.…”

Section: Introductionmentioning

confidence: 99%

“…Maze-solving is the act of discovering away in the maze. Some maze-solving approaches have no prior knowledge of the maze; conversely, in other approaches, an individual can see the complete maze instantly [8], [11].…”

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confidence: 99%

Path discovering in maze area using mobile robot

et al. 2022

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Robotic maze pathfinding problems deal with detecting the correct route from the start point to the end-point in a virtual maze environment consisting of walls. Automated robot mobility is a significant feature, which enables a mobile robot to traverse a maze independently, from one position to another, without human intervention. There is a myriad of autonomous industrial mobile robot applications, including the transportation of goods and parts, domestic cleaning, indoor security surveillance, airport baggage couriering, and a plethora of other applications to traverse dangerous locations. This paper proposes a pathfinding mobile robot in a virtual maze based on a combination of a simplified left-hand algorithm and a line-following control algorithm. The mobile robot works in any maze to determine a route from the initial starting point to the end-point. The approach outlined in this paper uses a left-hand algorithm to solve the maze problem and a line-follower control algorithm to enable the robot to move in a straight line through the virtual maze. The algorithm used is less complicated and prevents the robot from falling into infinity loops compared to the traditional wall-follower algorithm.

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“…La mayoría de los trabajos existentes se encuentran relacionados con robots resolviendo laberintos, tal es el caso de una investigación de la Escuela Colombiana Julio Garavito, que construyó un móvil capaz de salir de un laberinto según indicaciones enviadas por un computador, posterior al procesamiento de la imagen capturada por una cámara [8]. O un agente inteligente que usa procesamiento de imágenes y teoría de grafos y logra crear un solucionador de laberintos [9]. Sin embargo, también existen iniciativas en el área de software con aplicaciones lúdicas para niños, por ejemplo, Josué Osorio Flores creó un sistema llamado MAZE3D, un juego programado con el fin de crear aleatoriamente escenarios de mapas tridimensionales.…”

Section: Introductionunclassified

Maze Companion: aplicativo inteligencia artificial que ayuda a solucionar laberintos

Saint-Priest

Domínguez-Acevedo²,

Márquez-Hernández³

2019

rev.ing.univ.Medellin

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La capacidad de concentración de un niño depende de su edad y lo normal es que a medida que crece, aumenta este tiempo de atención; sin embargo, es importante trabajar esta capacidad desde edades tempranas. Los clásicos juegos de mesa como ajedrez, laberintos, triqui, rompecabezas, etc., requieren cierta cantidad de procesos mentales para ser jugados y son ideales para mantener la atención del niño durante varios minutos. El presente proyecto es una propuesta innovadora a través del procesamiento de imágenes y algoritmos de búsqueda por anchura, resuelve laberintos físicos creados por los jugadores. La efectividad obtenida fue de un 72 %, se recalca que la calidad del sensor y el ángulo en el cual se toma la fotografía influyen enormemente.

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Intelligent Maze Solving Robot Based on Image Processing and Graph Theory Algorithms

Cited by 17 publications

References 12 publications

Optimized Path Planning in Reinforcement Learning by Backtracking

Optimized Path Planning in Reinforcement Learning by Backtracking

Path discovering in maze area using mobile robot

Maze Companion: aplicativo inteligencia artificial que ayuda a solucionar laberintos

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