ANFIS-Based Inverse Kinematics and Forward Dynamics of 3 DOF Serial Manipulator

Deshmukh, Deepak; Pratihar, Dilip Kumar; Deb, Alok Kanti; Ray, Hena; Ghosh, Arghya

doi:10.1007/978-3-030-73050-5_15

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…However, it requires initial value selection and usually does not necessarily provide solution if the matrix is singular [17]. Soft computing method such as Artificial Neural Network [18], [19] and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) [20] provides options of solving inverse kinematic using optimization approach. However, this method requires longer process for training the model in the beginning [12].…”

Section: B Actuation Strategy: Inverse Kinematics and Trajectory Plan...mentioning

confidence: 99%

Performance Analysis of 4-DOF RPRR Robot Manipulator Actuation Strategy for Pick and Place Application in Healthcare Environment

Afrisal¹,

Setiyadi²,

Riyadi³

et al. 2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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Direct and indirect physical contact of humans and objects become the main medium of transmissible diseases such as COVID-19. Some strategies have been proposed to mitigate the risks of infections by minimizing physical contact, such as using robotics technology. Tele-robotics is one of the sub-fields in robotics that aims to implement physical surrogates for monitoring and controlling robots from remote distances, either autonomous, semi-autonomous, or manually guided. This paper discusses experimental research for evaluating the performance of a 4-DOF robot manipulator for pick and place tasks on small medical objects, such as test tubes in table-top scenarios. The robot manipulator is designed as an RPRR manipulator and is equipped with a gripper attached to its endeffector. Inverse kinematics and trajectory planning methods have been successfully implemented in real-time. The inverse kinematic method utilizes a pseudo-inverse Jacobian solver, and the trajectory generation utilizes a sigmoid function. The performance analysis results show that pick and place missions have been demonstrated with minimum tolerable position error, which is not more than 3.5 mm. The robot manipulator can satisfy high precision during repetitive experiments and maintain its accuracy in picking and placing standard test tubes from one rack to another within its working space. The smooth trajectories of the end-effector are achieved by implementing the sigmoid function. Thus, it satisfies the requirement for handling objects with minimum vibrations even during the actuation process with maximum speed.

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Section: B Actuation Strategy: Inverse Kinematics and Trajectory Plan...mentioning

confidence: 99%

Performance Analysis of 4-DOF RPRR Robot Manipulator Actuation Strategy for Pick and Place Application in Healthcare Environment

Afrisal¹,

Setiyadi²,

Riyadi³

et al. 2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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show abstract

“…However, to make the robot work in an intelligent system and to choose the best movement (forward and reverse), ANFIS (adaptive neuro-fuzzy inference system) was used. The ANFIS method was implemented to reduce the complexity of the analysis and to provide good results compared with mathematical modeling [46]. A simulation for the end-effector's positions for the 3DOF robot arm manipulation was completed using ANFIS for the accuracy inverse mapping by forwarding kinematics in 2D [47].…”

Section: The Robot Forward and Inverse Kinematics Using Anfismentioning

confidence: 99%

Intelligent Robotic Welding Based on a Computer Vision Technology Approach

et al. 2022

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Robots have become an essential part of modern industries in welding departments to increase the accuracy and rate of production. The intelligent detection of welding line edges to start the weld in a proper position is very important. This work introduces a new approach using image processing to detect welding lines by tracking the edges of plates according to the required speed by three degrees of a freedom robotic arm. The two different algorithms achieved in the developed approach are the edge detection and top-hat transformation. An adaptive neuro-fuzzy inference system ANFIS was used to choose the best forward and inverse kinematics of the robot. MIG welding at the end-effector was applied as a tool in this system, and the weld was completed according to the required working conditions and performance. The parts of the system work with compatible and consistent performances, with acceptable accuracy for tracking the line of the welding path.

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“…In addition to deterministic methods, stochastic approaches like Fuzzy control have been explored [49]- [51], but these often rely on expert knowledge for constructing membership functions, leading to varying configurations [52]. The Adaptive Neuro-Fuzzy Inference System (ANFIS) was developed to overcome this limitation [53], [54], employing inverse training to model dynamic systems with finite errors [55], [56]. The stochastic methods give a more nonlinear response than the deterministic methods.…”

Section: Introductionmentioning

confidence: 99%

Humanoid Walking Control Using LQR and ANFIS

Auzan,

Lelono,

Dharmawan

2023

Journal of Robotics and Control

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Humanoid robots possess remarkable mobility and adaptability for diverse environments. Nonetheless, accurate walking pattern tracking remains challenging, especially when employing the linear quadratic regulator (LQR) due to delays in high-mobility setpoint tracking. We propose a novel control approach to address this limitation by integrating an artificial neuro-fuzzy inference system (ANFIS) with the LQR to enhance pattern tracking. The research contributes to developing a control system that combines LQR and ANFIS to enable humanoid robots to follow various walking patterns with increased precision and efficiency and also the scheme to incorporate LQR and ANFIS. The study involves four experiments: step response, walking phase, static straight walking, and varied straight walking. Each test runs for 5 seconds with a 100-millisecond sampling rate, repeated five times, and employs the Integral Absolute Value (IAE) metric for evaluation. The LQR-ANFIS method exhibits superior performance, achieving a maximum overshoot of 0%, a rise time of 0.3 seconds, a settling time of 0.3 seconds, and a steady-state error of 0% in the step response experiment. The proposed control system also enables stable walking with step periods ranging from 0.15 to 4 seconds and step ranges of 0.05 to 0.03 meters. In conclusion, the integration of ANFIS with the LQR significantly enhances the mobility of humanoid robots, enabling them to navigate diverse environments and accurately track various walking patterns proficiently.

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ANFIS-Based Inverse Kinematics and Forward Dynamics of 3 DOF Serial Manipulator

Cited by 3 publications

References 19 publications

Performance Analysis of 4-DOF RPRR Robot Manipulator Actuation Strategy for Pick and Place Application in Healthcare Environment

Performance Analysis of 4-DOF RPRR Robot Manipulator Actuation Strategy for Pick and Place Application in Healthcare Environment

Intelligent Robotic Welding Based on a Computer Vision Technology Approach

Humanoid Walking Control Using LQR and ANFIS

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