A fuzzified systematic adjustment of the robotic Darwinian PSO

Couceiro, Micael S.; Machado, J. A. Tenreiro; Rocha, Rui P.; Ferreira, Nuno M. F.

doi:10.1016/j.robot.2012.09.021

Cited by 40 publications

(16 citation statements)

References 29 publications

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“…To overcome the complex problems, FDPSO is adopted that provides a high immunity to sub-optimal (local) solutions by using Rastrigin function [39], or even time-variant functions [41].…”

Section: Fractional-order Darwinian Particle Swarm Optimizationmentioning

confidence: 99%

Multi-Level Thresholding with Fractional-Order Darwinian PSO and Tsallis Function

Pugalenthi¹,

Oliver²

2019

IJITEE

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A novel optimal multi-level thresholding is proposed using gray scale images for Fractional-order Darwinian Particle Swarm Optimization (FDPSO) and Tsallis function. The maximization of Tsallis entropy is chosen as the Objective Function (OF) which monitors FDPSO’s exploration until the search converges to an optimal solution. The proposed method is tested on six standard test images and compared with heuristic methods, such as Bat Algorithm (BA) and Firefly Algorithm (FA). The robustness of the proposed thresholding procedure was tested and validated on the considered image data set with Poisson Noise (PN) and Gaussian Noise (GN). The results obtained with this study verify that, FDPSO offers better image quality measures when compared with BA and FA algorithms. Wilcoxon’s test was performed by Mean Structural Similarity Index (MSSIM), and the results prove that image segmentation is clear even in noisy dataset based on the statistical significance of the FDPSO with respect to BA and FA.

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“…To overcome the complex problems, FDPSO is adopted that provides a high immunity to sub-optimal (local) solutions by using Rastrigin function [39], or even time-variant functions [41].…”

Section: Fractional-order Darwinian Particle Swarm Optimizationmentioning

confidence: 99%

Multi-Level Thresholding with Fractional-Order Darwinian PSO and Tsallis Function

Pugalenthi¹,

Oliver²

2019

IJITEE

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“…Other useful simulators running on ROS platforms that can be easily found available on the web are: the Simple Two Dimensional Robot (STDR) Simulator [24], under the GNU General Public License v3.0 is a flexible and scalable 2D multi-robot simulator, ideal for swarm robotics and multi-robot systems; the Virtual Robot Experimentation Platform (V-REP) [25], with free and commercial versions available, is particularly used in industry (Robotic Arms and Manipulators); the Webots Simulator [26] a commercial simulator which has a development environment to model, program and simulate mobile robots; among others [27][28][29][30][31]. Table 8 also shows the learning objectives and outcomes of the use of simulation tools on ROS.…”

Section: Computer Aided Design -Learningmentioning

confidence: 99%

Intensive summer course in robotics – Robotcraft

Ferreira

Araújo²,

Couceiro³

et al. 2018

ACI

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This paper describes a two-month summer intensive course designed to introduce participants with a hands-on technical craft on robotics and to acquire experience in the low-level details of embedded systems. Attendants started this course with a brief introduction to robotics; learned to draw, design and create a personalized 3D structure for their mobile robotic platform and developed skills in embedded systems. They were familiarize with the practices used in robotics, learning to connect all sensors and actuator, developing a typical application on differential kinematic using Arduino, exploring ROS features under Raspberry Pi environment and Arduino – Raspberry Pi communication. Different paradigms and some real applications and programming were addressed on the topic of Artificial Intelligence. Throughout the course, participants were introduced to programming languages (including Python and C++), advanced programming concepts such as ROS, basic API development, system concepts such as I2C and UART serial interfaces, PWM motor control and sensor fusion to improve robotic navigation and localization. This paper describes not just the concept, layout and methodology used on RobotCraft 2017 but also presents the participants knowledge background and their overall opinions, leading to focus on lessons learned and suggestions for future editions.

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“…4. In order to overcome the limitations of the deterministic approach described in the previous section, we developed an adaptive fuzzy logic system to handle the sensing information arising from the sonars, LRF, and alcohol sensor, by following a similar approach to our previous work in another domain [29]. This was the base for the development of the SmokeNav v2 layer.…”

Section: Smokenav V2 : Fuzzified Modelmentioning

confidence: 99%

A Sensor Fusion Layer to Cope with Reduced Visibility in SLAM

Santos

Couceiro

Portugal

et al. 2015

J Intell Robot Syst

Self Cite

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Mapping and navigating with mobile robots in scenarios with reduced visibility, e.g. due to smoke, dust, or fog, is still a big challenge nowadays. In spite of the tremendous advance on Simultaneous Localization and Mapping (SLAM) techniques for the past decade, most of current algorithms fail in those environments because they usually rely on optical sensors providing dense range data, e.g. laser range finders, stereo vision, LIDARs, RGB-D, etc., whose measurement process is highly disturbed by particles of smoke, dust, or steam. This article addresses the problem of performing SLAM under reduced visibility conditions by proposing a sensor fusion layer which takes advantage from complementary characteristics between a laser range finder (LRF) and an array of sonars. This sensor fusion layer is ultimately used with a state-of-the-art SLAM technique to be resilient in scenarios where visibility cannot be assumed at all times. Special attention is given to mapping using commercial off-the-shelf (COTS) sensors, namely arrays of sonars which, being usually available in robotic platforms, raise technical issues that were investigated in the course of this work. Two sensor fusion methods, a heuristic method and a fuzzy logic-based method, are presented and discussed, corresponding to different stages of the research work conducted. The experimental validation of both methods with two different mobile robot platforms in smoky indoor scenarios showed that they provide a robust solution, using only COTS sensors, for adequately coping with reduced visibility in the SLAM process, thus decreasing significantly its impact in the mapping and localization results obtained.

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A fuzzified systematic adjustment of the robotic Darwinian PSO

Cited by 40 publications

References 29 publications

Multi-Level Thresholding with Fractional-Order Darwinian PSO and Tsallis Function

Multi-Level Thresholding with Fractional-Order Darwinian PSO and Tsallis Function

Intensive summer course in robotics – Robotcraft

A Sensor Fusion Layer to Cope with Reduced Visibility in SLAM

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