Mobile Robot Navigation Using Fuzzy Logic in Static Environments

Singh, Ngangbam Herojit; Thongam, Khelchandra

doi:10.1016/j.procs.2017.12.004

Cited by 55 publications

(25 citation statements)

References 10 publications

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“…Penelitian tersebut didukung dengan penelitian ini Fuzzy logic dapat digunakan untuk solusi design yaitu melakukan navigasi lokal, Navigasi global, perencanaan jalur serta kontrol kecepatan robot [10]. Pada penelitian lain dibahas tentang tantangan dibidang robotika permasalahan yang disebutkan adalah penghindaran rintangan dan penghematan energi dan dari hasil penelitian tersebut bahwa masalah pada navigasi Mobile robot dapat diatasi dengan menggunakan sistem inferensi logika Fuzzy [11]. Mobile Robot dapat dikontrol dengan menggunakan jaringan Komunikasi Mobile GSM hal ini dapat berguna apabila sesuati kondisi tidak dapat dilakukan oleh manusia, sehingga Mobile Robot dapat membantu hal tersebut [12].…”

Section: Jurnal Sistem Informasi Dan Telematika (Telekomunikasi Multunclassified

Otomatisasi Navigasi penghindar Obstacle pada Mobile Robot dengan Metode Fuzzy Sugeno dan Mikrokontroler Arduino

Endra

Aprilinda

Cucus

et al. 2020

Explore. jurnal. sistem. inf. dan. telematika

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Section: Jurnal Sistem Informasi Dan Telematika (Telekomunikasi Multunclassified

Otomatisasi Navigasi penghindar Obstacle pada Mobile Robot dengan Metode Fuzzy Sugeno dan Mikrokontroler Arduino

Endra

Aprilinda

Cucus

et al. 2020

Explore. jurnal. sistem. inf. dan. telematika

View full text Add to dashboard Cite

“…In this section, to verify the superiority of the presented algorithm for a single mobile robot and 20 × 20 grid-based environment maps, the path planning results under the presented hybrid artificial fish swarm algorithm and the traditional fish swarm algorithm are shown in Figure 3. For 20 × 20 and 40 × 40 grid-based environment maps, the path planning results based on the adaptive artificial fish swarm algorithm (AAFA) [21], fuzzy logic (FL) [22], the improved genetic algorithm (IGA) [23] and our method are shown in Table 1. As can be seen in Figure 3 and Table 1, optimization performance and iteration time can be improved by using the presented method.…”

Section: Simulation Experimentsmentioning

confidence: 99%

Cooperative Path Planning for Multiple Mobile Robots via HAFSA and an Expansion Logic Strategy

Huang

Jiang

et al. 2019

Applied Sciences

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The cooperative path planning problem of multiple mobile robots in an unknown indoor environment is considered in this article. We presented a novel obstacle avoidance and real-time navigation algorithm. The proposed approach consisted of global path planning and local path planning via HAFSA (hybrid artificial fish swarm algorithm) and an expansion logic strategy. Meanwhile, a kind of scoring function was developed, which shortened the time of local path planning and improved the decision-making ability of the path planning algorithm. Finally, using STDR (simple two dimensional robot simulator) and RVIZ (robot operating system visualizer), a multiple mobile robot simulation platform was designed to verify the presented real-time navigation algorithm. Simulation experiments were performed to validate the effectiveness of the proposed path planning method for multiple mobile robots.

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“…Singh and Thongam used MATLAB R2014a Mamdani fuzzy system and presented the navigation of mobile robot in static environment. They provided the sensor information of obstacle distances and robot rotation angle as input to the fuzzy system, and calculated the angular velocities of wheeled robot as output . Rao et al presented grey wolf searching technique for navigation of autonomous mobile robots in various environments with less time, energy, and collision‐free characteristic .…”

Section: Introductionmentioning

confidence: 99%

Implementation of grey wolf optimization controller for multiple humanoid navigation

Muni

Parhi

Kumar

2020

Computer Animation & Virtual

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In this paper, grey wolf optimization controller (GWOC) is considered as a multiobjective technique for multiple humanoid navigations. Upon activation of GWOC, the humanoids mimic the group hunting behavior of grey wolves and navigate toward the target in a collision-free manner in presence of both static and dynamic hurdles. The wolves in the pack will either diverge for searching prey or converge together for attacking the prey following the best search agent (Leader Alpha). GWOC has the ability to keep the humanoid free from being trapped in local minima whereas it facilitates it to head toward global minima. GWOC provides better results as compared to other intelligent techniques because of its five characteristics that include safe boundary, protection, following, hunting, and caring. Both simulation and experimental navigation in laboratory conditions for single as well as for multiple humanoid NAOs have been carried out. From the results of simulation and experimental data, it is confirmed that GWOC provides global minima for humanoid robots in complex environments with different shaped obstacles. A Petri-net controller is considered while navigating multiple humanoids, as during multiple humanoid navigations, one humanoid robot acts as a dynamic obstacle to other humanoids. K E Y W O R D SGWOC, NAO humanoid, navigation, petri-net model INTRODUCTIONLot of research works have been performed for path navigation of mobile robots, biped robots, and industrial robots by using both classical and artificial intelligent techniques. But there is still some improvement needed to avoid the trapping of robots in local minima. Today, the main challenging task for researchers is the path navigation of humanoids having multidegrees of freedom by using computational methods. Though several researchers are working on it, still it is in virtue to obtain the best intelligent method for effective and efficient navigation. For this purpose, grey wolf optimization controller (GWOC) is considered. The humanoid NAO considered for this research work is developed by ALDEBARAN robotics France, which is having 25 degrees of freedom. 1 There are sensors for detecting the obstacles, cameras to take the images of hurdles, motors, actuators, and batteries to perform the predefined tasks. The obstacle distances and shape of the obstacles are detected by inbuilt ultrasonic sensors and camera mount on the head. The programmed GWOC activates when NAO finds any obstacle in its path while heading toward target. Several researchers have worked toward the navigation of different kinds of robots using many intelligent techniques as path navigation of robots plays a Comp Anim Virtual Worlds. 2020;31:e1919. wileyonlinelibrary.com/journal/cav

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Mobile Robot Navigation Using Fuzzy Logic in Static Environments

Cited by 55 publications

References 10 publications

Otomatisasi Navigasi penghindar Obstacle pada Mobile Robot dengan Metode Fuzzy Sugeno dan Mikrokontroler Arduino

Otomatisasi Navigasi penghindar Obstacle pada Mobile Robot dengan Metode Fuzzy Sugeno dan Mikrokontroler Arduino

Cooperative Path Planning for Multiple Mobile Robots via HAFSA and an Expansion Logic Strategy

Implementation of grey wolf optimization controller for multiple humanoid navigation

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