Desain Fuzzy Logic Controller Untuk Pengendali Pergerakan Mobile Manipulator

Technology is developing more and more to facilitate human life. Technology enables automation in all areas of life, and robots are among the most frequently used machines in automation. Robots can help with human work in all fields, including agriculture. A mobile robot manipulator is a combination of a robot arm and a mobile robot so that this type of robot can combine the capabilities of the two robots. This paper discusses the design of a robot manipulator to be used in agriculture to replace farmers in the harvesting of agricultural products, such as tomatoes. This paper presents a mechanical, electrical design and uses the Fuzzy Logic Controller as artificial intelligence. The feasibility of the proposed method is demonstrated by simulation in Mobotsim.

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Artificial Intelligent Fuzzy Logic Controller Applied on 6DOF Robot Arm Using LabVIEW and FPGA

European Journal of Engineering Research and Science ◽

10.24018/ejers.2018.3.5.661 ◽

2018 ◽

Vol 3 (5) ◽

pp. 1

Author(s):

Ahlam Najm A-Amir ◽

Hanan A.R. Akkar

Keyword(s):

Fuzzy Logic ◽

Fuzzy Logic Controller ◽

Fuzzy Controller ◽

Robot Manipulator ◽

Robot Arm ◽

Serial Link ◽

Artificial Intelligent ◽

Field Programmable ◽

Labview Fpga

In this work an efficient Artificial Intelligent Robotic Fuzzy Logic Controller (AIRFC) system have been constructed to control the robot arm. A serial link Robot manipulator with 6 Degree of Freedom (DOF) from DFROBOT of code ROB0036 is used as a case study. A fuzzy logic type1 controller is implemented on LabVIEW to control each joint of the robot arm for nonlinearity measurements and a fuzzy logic type2 controller is applied which is more suitable for uncertainty. The hardware design is implemented and finally downloaded using the Field Programmable Gate Array (FPGA) kit named PCI-7833R from National Instrument. By using the LabVIEW FPGA MODEL the target board can be detected for software implementation of the controllers’ systems. The work shows that in case of type2 fuzzy logic the rise time is less than that of type1 fuzzy logic for the shoulder, wrist roll and the gripper angles and it is higher for base, elbow and wrist pitch angles. The settling time is the same in elbow and wrist pitch angles and for the type2 fuzzy controller it is less for other angles.

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Autonomous Visual Servoing for Alternately Working Arm Robots

Kinetik Game Technology Information System Computer Network Computing Electronics and Control ◽

10.22219/kinetik.v6i3.1285 ◽

2021 ◽

Author(s):

Tresna Dewi ◽

Rusdianasari Rusdianasari ◽

RD Kusumanto ◽

Siproni Siproni ◽

Fradina Septiarini ◽

...

Keyword(s):

Image Processing ◽

Fuzzy Logic ◽

Industry 4.0 ◽

Visual Servoing ◽

Fuzzy Logic Controller ◽

Human Life ◽

Experimental Results ◽

Agricultural Products ◽

Pick And Place ◽

Time Required

Robots have infiltrated many aspects of human life up to this point, and with the term Industry 4.0, robots have even become the primary workforce in various factories. This condition necessitates that the robots collaborate without clashing. This paper discusses the application of two arm robot manipulators working alternately in sorting agricultural products. The proposed method employs simple image processing to detect the object and becomes the input to the system to control the robots. The effectiveness of the proposed method is enhanced by the application of a Fuzzy Logic Controller to smoothen robots’ joints motions. The average time required by the robot to finish their task from detecting to returning to standby position is 11.76 s for green tomatoes and 12.86 s for red tomatoes. The experimental results show that the proposed method is effective in controlling two robots to pick and place agricultural products using visual servoing.

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STAND-UP MOTION SUPPORT BY A MOBILE MANIPULATOR SYSTEM WITH HIGH SPEED TACTILE SENSORS

International Journal of Information Acquisition ◽

10.1142/s0219878911002458 ◽

2011 ◽

Vol 08 (03) ◽

pp. 181-195

Author(s):

ZHAOXIAN XIE ◽

HISASHI YAMAGUCHI ◽

MASAHITO TSUKANO ◽

AIGUO MING ◽

MAKOTO SHIMOJO

Keyword(s):

Fuzzy Logic ◽

Feedback Control ◽

Experimental Verification ◽

High Speed ◽

Center Of Pressure ◽

Tactile Sensor ◽

Mobile Manipulator ◽

Tactile Sensing ◽

Robot Arm ◽

Tactile Sensors

As one of the home services by a mobile manipulator system, we are aiming at the realization of the stand-up motion support for elderly people. This work is charaterized by the use of real-time feedback control based on the information from high speed tactile sensors for detecting the contact force as well as its center of pressure between the assisted human and the robot arm. First, this paper introduces the design of the tactile sensor as well as initial experimental results to show the feasibility of the proposed system. Moreover, several fundamental tactile sensing-based motion controllers necessary for the stand-up motion support and their experimental verification are presented. Finally, an assist trajectory generation method for the stand-up motion support by integrating fuzzy logic with tactile sensing is proposed and demonstrated experimentally.

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Hybrid Neural-Fuzzy Controller for Motorized Robot Arm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.403-408.5068 ◽

2011 ◽

Vol 403-408 ◽

pp. 5068-5075

Author(s):

Fatma Zada ◽

Shawket K. Guirguis ◽

Walied M. Sead

Keyword(s):

Fuzzy Logic ◽

Design Methodology ◽

Fuzzy Logic Controller ◽

Fuzzy Controller ◽

System Response ◽

Robot Arm ◽

Neural Controller ◽

Hybrid Controller ◽

Neural Fuzzy ◽

Transient And Steady State

In this study, a design methodology is introduced that blends the neural and fuzzy logic controllers in an intelligent way developing a new intelligent hybrid controller. In this design methodology, the fuzzy logic controller works in parallel with the neural controller and adjusting the output of the neural controller. The performance of our proposed controller is demonstrated on a motorized robot arm with disturbances. The simulation results shows that the new hybrid neural -fuzzy controller provides better system response in terms of transient and steady-state performance when compared to neural or fuzzy logic controller applications. The development and implementation of the proposed controller is done using the MATLAB/Simulink toolbox to illustrate the efficiency of the proposed method.

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Fuzzy Logic Controller for Obstacle Avoidance of Mobile Robot

International Journal of Nonlinear Sciences and Numerical Simulation ◽

10.1515/ijnsns-2018-0038 ◽

2019 ◽

Vol 20 (1) ◽

pp. 51-62

Author(s):

Rajmeet Singh ◽

Tarun Kumar Bera

Keyword(s):

Fuzzy Logic ◽

Mobile Robot ◽

Dynamic Model ◽

Obstacle Avoidance ◽

Fuzzy Logic Controller ◽

Fuzzy Controller ◽

Bond Graph ◽

Membership Functions ◽

Current Position ◽

Dc Motors

AbstractThis work describes design and implementation of a navigation and obstacle avoidance controller using fuzzy logic for four-wheel mobile robot. The main contribution of this paper can be summarized in the fact that single fuzzy logic controller can be used for navigation as well as obstacle avoidance (static, dynamic and both) for dynamic model of four-wheel mobile robot. The bond graph is used to develop the dynamic model of mobile robot and then it is converted into SIMULINK block by using ‘S-function’ directly from SYMBOLS Shakti bond graph software library. The four-wheel mobile robot used in this work is equipped with DC motors, three ultrasonic sensors to measure the distance from the obstacles and optical encoders to provide the current position and speed. The three input membership functions (distance from target, angle and distance from obstacles) and two output membership functions (left wheel voltage and right wheel voltage) are considered in fuzzy logic controller. One hundred and sixty-two sets of rules are considered for motion control of the mobile robot. The different case studies are considered and are simulated using MATLAB-SIMULINK software platform to evaluate the performance of the controller. Simulation results show the performances of the navigation and obstacle avoidance fuzzy controller in terms of minimum travelled path for various cases.

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Mobile Robot Wall-Following Control Using Fuzzy Logic Controller with Improved Differential Search and Reinforcement Learning

Mathematics ◽

10.3390/math8081254 ◽

2020 ◽

Vol 8 (8) ◽

pp. 1254 ◽

Cited By ~ 1

Author(s):

Cheng-Hung Chen ◽

Shiou-Yun Jeng ◽

Cheng-Jian Lin

Keyword(s):

Fuzzy Logic ◽

Reinforcement Learning ◽

Mobile Robot ◽

Fuzzy Logic Controller ◽

Search Algorithm ◽

Experimental Results ◽

Average Error ◽

Stopover Site ◽

Accumulated Reward ◽

Wall Following

In this study, a fuzzy logic controller with the reinforcement improved differential search algorithm (FLC_R-IDS) is proposed for solving a mobile robot wall-following control problem. This study uses the reward and punishment mechanisms of reinforcement learning to train the mobile robot wall-following control. The proposed improved differential search algorithm uses parameter adaptation to adjust the control parameters. To improve the exploration of the algorithm, a change in the number of superorganisms is required as it involves a stopover site. This study uses reinforcement learning to guide the behavior of the robot. When the mobile robot satisfies three reward conditions, it gets reward +1. The accumulated reward value is used to evaluate the controller and to replace the next controller training. Experimental results show that, compared with the traditional differential search algorithm and the chaos differential search algorithm, the average error value of the proposed FLC_R-IDS in the three experimental environments is reduced by 12.44%, 22.54% and 25.98%, respectively. Final, the experimental results also show that the real mobile robot using the proposed method can effectively implement the wall-following control.

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