Collision-Free Mobile Robot navigation using Fuzzy Logic Approach

Dirik, Mahmut

doi:10.5120/ijca2018916085

Cited by 3 publications

(2 citation statements)

References 22 publications

(16 reference statements)

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“…Kim et al 6 have developed a kernel subspace learning algorithm for predicting pedestrian motion and subsequent control of an autonomous robot towards safe navigation. Dirik 7 has used fuzzy logic as a potential navigational technique for designing a safe navigational approach for mobile robots in indoor and outdoor environments. Keshmiri and Payandeh 8,9 designed a solution for multi-robot and multi-recharging station problem by enabling the robots to use their nearest recharging station rather than using a specific station and avoiding disturbance to other robots on the basis of regression technique.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Hybridization of Regression Technique with Genetic Algorithm for Navigation of Humanoids in Complex Environments

Kumar

Parhi

2019

Robotica

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SUMMARYIn the current investigation, a novel navigational controller has been designed and implemented for humanoids in cluttered environments. Here, regression analysis is hybridized with genetic algorithm (GA) for designing the controller. The obstacle distances collected in the form of sensor outputs are initially fed to the regression controller; and based on the previous training pattern data, an intermediate advancing angle (AA) is obtained as the first output. The intermediate AA obtained from the regression controller along with other inputs is again fed to the GA controller, which generates the final AA as the desired final output to avoid the obstacles present in a complex environment and reach the destination successfully. The working of the controller is tested on a V-REP simulation platform. In the current work, navigation of both single as well as multiple humanoids has been attempted. To avoid inter-collision among multiple humanoids during their navigation in a common platform, a Petri-Net model has been proposed. The simulation results are validated through a real-time experimental platform developed under laboratory conditions. The results obtained from both the simulation and experimental platforms are compared against each other and are found to be in good agreement with acceptable percentage of errors. Finally, the proposed controller is also compared with other existing navigational controller and an improvement in performance has been observed.

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Section: Introductionmentioning

confidence: 99%

Intelligent Hybridization of Regression Technique with Genetic Algorithm for Navigation of Humanoids in Complex Environments

Kumar

Parhi

2019

Robotica

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“…Nattharith and Güzel 10 proposed a vision-integrated fuzzy-based control strategy for navigation of a targetfollowing mobile robot. Dirik 11 discussed a fuzzy-logic-based navigational approach for hassle-free movement of a mobile robotic agent in an indoor arena. Shi et al 12 developed a grid navigation model based on fuzzy concepts and applied the same in simulation and experimental platforms.…”

Section: Introductionmentioning

confidence: 99%

Smart Navigation of Humanoid Robots Using DAYKUN-BIP Virtual Target Displacement and Petri-Net Strategy

Parhi¹,

Kumar²

2018

Robotica

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SummaryWith an ability to mimic the human behaviour and replace human efforts in proper platforms, humanoid robots have always acquired a special place among robotics practitioners. Being a complex method of analysis, navigation and path planning, humanoid robots still possess an interesting yet challenging area of investigation. In the current work, a novel navigational strategy has been proposed for smooth and hassle-free movement of single as well as multi-humanoid robots in complex environments. Here, the navigational plan is based on a virtual target displacement strategy which is activated when the robot is unable to find a safe path along the actual target line. After detection of a potential obstacle by the sensors of the robot, a number of virtual targets are generated around the actual target. Then, the most feasible path and point to move are calculated by assigning suitable weightage through several selected parameters to each target line and visualizing the safest path. The proposed approach is implemented on a V-REP simulation platform, and the simulation results are also validated against an experimental set-up prepared under test conditions. The validation of simulation results against experimental counterparts has revealed satisfactory agreement between them. To avoid possibility of any inter-collision during navigation of multi-humanoids under a common platform, a Petri-Net strategy has been integrated along with the proposed control strategy. Finally, the developed approach is also assessed against another existing navigational controller, and a significant performance improvement has been observed.

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Arithmetic mean-based decision-making algorithm for obstacle avoidance and multiple intruder detection

Arif¹,

Marzoughi²

2020

2020 Australian and New Zealand Control Conference (ANZCC)

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Collision-Free Mobile Robot navigation using Fuzzy Logic Approach

Cited by 3 publications

References 22 publications

Intelligent Hybridization of Regression Technique with Genetic Algorithm for Navigation of Humanoids in Complex Environments

Intelligent Hybridization of Regression Technique with Genetic Algorithm for Navigation of Humanoids in Complex Environments

Smart Navigation of Humanoid Robots Using DAYKUN-BIP Virtual Target Displacement and Petri-Net Strategy

Arithmetic mean-based decision-making algorithm for obstacle avoidance and multiple intruder detection

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