Soft computing controller based path planning wheeled mobile robot

Suganya, K.; Arulmozhi, V.

doi:10.1109/icaca.2016.7887956

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…But in that article has not introduced any variable for direction concerning wheel slippage and skidsteering effects. The coordination between master and slave robot position and velocity employing bilateral tele-drive of WMRs with unlimited workspace, which is commonly considered Lee & Xu, 2006;Suganya & Arulmozhi, 2016;Tipsuwan & Chow, 2004). Indeed WMR is travelling on loose soil; disaster exploration and scientific expedition always require workspace mismatch and surface slippage at the time of actuated mobile robot position and desired angular velocity.…”

Section: Introductionmentioning

confidence: 99%

Simpsons 1/3 base fuzzy neural network with passive controller of wheeled mobile robot system

Suganya¹,

Arulmozhi²

2021

Arab Journal of Basic and Applied Sciences

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The paper investigates Passivity of Simpson 1/3 rule fuzzy neural network controller by teleoperation-based wheeled mobile robot navigation and wheel slippage. To render the obstacle avoidance for wheeled mobile robot and ensuring incident-free navigation particularly unlimited workspace and surface slippage of coordination for master robot position and slave robot velocity, a passivity controller mode is employed. Effective control strategies are formulated to achieve system stability in which soft-computing methodology is accessed to bypass robot wheel slippage and skidding. Soft-computing based fuzzy neural network system is framed through Simpson 1/3 rule for reducing master/slave robot position error on behalf of unavoidable and acceptable forces, which is confirmed by teleoperation system. We concluded mathematically, the system stability which has been shown via its passivity of the fuzzy neural network controller. The forces which can be handled by the human operator are approximately equal to the forces applied by the environmental force and sensor predictor of slave robot whichever comes under the unlimited workspace. Simulation results are verified with the effect of the proposed controller.

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Section: Introductionmentioning

confidence: 99%

Simpsons 1/3 base fuzzy neural network with passive controller of wheeled mobile robot system

Suganya¹,

Arulmozhi²

2021

Arab Journal of Basic and Applied Sciences

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Sliding mode control with soft computing based path planning wheeled mobile robot

Suganya¹,

Arulmozhi²

2017

2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS)

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Stability of tracking wheel mobile robot with teleoperation fuzzy neural network control system

Sumathi,

Ravi Kumar,

Anandhi

2023

Int. J. Mod. Phys. B

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The stability of the Tracking Wheel Mobile Robot with Teleoperation System and Path Following Method is discussed in this study. The path is to be tracked by the host computer which is the master robot. The response from the robot is captured on camera. As the slave robot approaches the target position, the camera captures the response robot’s position and as well as moving trajectory. The host computer receives all of the images, enabling mobile robot deviation recoveries. The slave robot can use teleoperation to follow the sensor based on the decisions made by the master robot. The Lyapunov function in the Fuzzy Neural Network (FNN) control structure assures the system’s stability and satisfactory performance. It supports a mobile robot’s ability to adhere to a reference trajectory without deviating from it. Finally, the outcome of the simulation demonstrates that our controller is capable of tracking different environmental conditions and maintaining stability.

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Soft computing controller based path planning wheeled mobile robot

Cited by 4 publications

References 9 publications

Simpsons 1/3 base fuzzy neural network with passive controller of wheeled mobile robot system

Simpsons 1/3 base fuzzy neural network with passive controller of wheeled mobile robot system

Sliding mode control with soft computing based path planning wheeled mobile robot

Stability of tracking wheel mobile robot with teleoperation fuzzy neural network control system

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