Neurogenetic algorithm applied to Route Planning for Autonomous Mobile Robots

Bruno, Diego Renan; Marranghello, Norian; Osório, Fernando Santos; Pereira, Aledir Silveira

doi:10.1109/ijcnn.2018.8489137

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…In this case, a 3D point map is generated through geometric projections and then converted to a 2D distance map. Neurogenetic algorithms have been tested in several studies [9], [10], [11] using the Gilbert-Johnson-Keerthi algorithm and the Participle Swarm Optimization algorithm. The first one is used to determine the distance between two convex sets.…”

Section: A Algorithmsmentioning

confidence: 99%

Multi-Path Routing Algorithm for Inclusive Mobility in an Urban Environment

Machado

Paiva

Pereira

2022

2022 IEEE International Smart Cities Conference (ISC2)

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The concept of smart city is frequently used nowadays to refer to the level of integration of cities with citizen services. Mobility and navigation are some examples. When considering disabled users, in a temporary or permanent way, the challenges to provide such services in an efficient way are even bigger. This paper is an extension of a previous work where a mobile application to recommend the most suitable route for citizens was implemented with both Dijkstra and A-Star algorithms. The presented work describes a multi-path approach considering the user might want to choose several destinations in the mobile application and obtain the most suitable route that goes through all of them. Results are presented using the Genetic and the Simulated Annealing Algorithms. Both were evaluated with 100 runs and 2, 4, 8 and 16 destinations. The average algorithm execution time, standard deviation, median value, maximum and minimum execution times were assessed. The Simulated Annealing Algorithm presented better results and always suggested the shorter, or equal, path when comparing to the Genetic Algorithm.

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Section: A Algorithmsmentioning

confidence: 99%

Multi-Path Routing Algorithm for Inclusive Mobility in an Urban Environment

Machado

Paiva

Pereira

2022

2022 IEEE International Smart Cities Conference (ISC2)

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“…There are several methods of Artificial Intelligent (AI) or modern control that are widely used in robotic by other researchers, such as Genetic Algorithm (GA) [1]- [3], Neural Network (NN) [4]- [6], and Fuzzy Logic [7]- [10]. The combination algorithm to control robot also proposed by several researchers [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

Obstacle Avoidance using Fuzzy Logic Controller on Wheeled Soccer Robot

Rinanto¹,

Marzuqi²,

Khumaidi³

et al. 2019

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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The purpose of this study is to apply Fuzzy Logic Controller on a wheeled soccer robot to avoid the collision with other robots in the field. The robot equipped by an omnidirectional camera as a vision sensor, a mini-PC for the image processing device, a microcontroller to handle I/O system, and three wheel's omnidirectional mover system. Omni-camera produces four inputvalues, namely: X coordinate ball position, Y coordinate ball position, distance and angle from obstacle to the point of interest in the camera frame. These inputs processed by a mini-PC and then forward to a microcontroller to calculate the output using Fuzzy Logic Controller. The output variables are the movement rate of the robot in the X, and Y coordinate. These outputs will be used by the kinematics controller to manage the speed of three Omniwheels driven by 24 volts DC motors. The experiment shows a good result with the percentage of the success of the robot catching the ball is around 70% and 80% in avoiding the obstacle. In time performance, the soccer robot with Fuzzy Logic Controller is superior by 4.67 seconds compared to the robot without this method.Obstacle Avoidance using Fuzzy Logic Controller on Wheeled Soccer Robot (Noorman Rinanto, et al) Rabib H. A et al [14]. The input data of this system comes from the omnidirectional camera. This camera catches the environment image, and then it is processed by a mini-PC that will produce coordinate of the object. Later, a microcontroller will compute the coordinate using Fuzzy Logic Algorithm. The output of this plant will be used to control the movement speed of the robot. RESEARCH METHODThis section categorized into several parts, including system design, omnidirectional camera, and fuzzy logic controller implementation.

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