scholarly journals Autonomous Visual Servoing for Alternately Working Arm Robots

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.

scholarly journals Desain Fuzzy Logic Controller Untuk Pengendali Pergerakan Mobile Manipulator

2020 ◽  
Vol 1 (01) ◽  
pp. 12-18
Author(s):  
Putri Repina Kesuma ◽  
Tresna Dewi ◽  
RD Kusumanto ◽  
Pola Risma ◽  
Yurni Oktarina
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Mobile Robot ◽  
Fuzzy Logic Controller ◽  
Robot Manipulator ◽  
Human Life ◽  
Agricultural Products ◽  
Mobile Manipulator ◽  
Robot Arm

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.

scholarly journals Inverse kinematic analysis of 4 DOF pick and place arm robot manipulator using fuzzy logic controller

2020 ◽  
Vol 10 (2) ◽  
pp. 1376
Author(s):  
Tresna Dewi ◽  
Siti Nurmaini ◽  
Pola Risma ◽  
Yurni Oktarina ◽  
Muhammad Roriz
Keyword(s):  
Fuzzy Logic ◽  
Inverse Kinematics ◽  
Fuzzy Logic Controller ◽  
Robot Manipulator ◽  
Inverse Kinematic ◽  
Experimental Results ◽  
Position Errors ◽  
Motion System ◽  
Pick And Place ◽  
The Right

The arm robot manipulator is suitable for substituting humans working in tomato plantation to ensure tomatoes are handled efficiently. The best design for this robot is four links with robust flexibility in x, y, and z-coordinates axis. Inverse kinematics and fuzzy logic controller (FLC) application are for precise and smooth motion. Inverse kinematics designs the most efficient position and motion of the arm robot by adjusting mechanical parameters. The FLC utilizes data input from the sensors to set the right position and motion of the end-effector. The predicted parameters are compared with experimental results to show the effectiveness of the proposed design and method. The position errors (in x, y, and z-axis) are 0.1%, 0.1%, and 0.04%. The rotation errors of each robot links (θ1, θ2, and θ3) are 0%, 0.7% and 0.3%. The FLC provides the suitable angle of the servo motor (θ4) responsible in gripper motion, and the experimental results correspond to FLC’s rules-based as the input to the gripper motion system. This setup is essential to avoid excessive force or miss-placed position that can damage tomatoes. The arm robot manipulator discussed in this study is a pick and place robot to move the harvested tomatoes to a packing system.

Digital Image Processing for Detecting and Classifying Plant Diseases

2017 ◽  
Vol 2 (11) ◽  
pp. 1-7
Author(s):  
Izay A. ◽  
Onyejegbu L. N.
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Programming Language ◽  
Design Methodology ◽  
Fuzzy Logic Controller ◽  
Object Oriented ◽  
Plant Diseases ◽  
Analysis And Design ◽  
Crop Diseases ◽  
Growing Population

Agriculture is the backbone of human sustenance in this world. With growing population, there is need for increased productivity in agriculture to be able to meet the demands. Diseases can occur on any part of a plant, but in this paper only the symptoms in the fruits of a plant is considered using segmentation algorithm and edge/ sizing detectors. We also looked at image processing using fuzzy logic controller. The system was designed using object oriented analysis and design methodology. It was implemented using MySQL for the database, and PHP programming language. This system will be of great benefit to farmers and will encourage them in investing their resources since crop diseases can be detected and eliminated early.

scholarly journals Object Detection Robot Using Fuzzy Logic Controller Through Image Processing

2021 ◽  
Vol 1737 (1) ◽  
pp. 012045
Author(s):  
M Khairudin ◽  
S Yatmono ◽  
AC Nugraha ◽  
M Ikhsani ◽  
A Shah ◽  
...  
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Object Detection ◽  
Fuzzy Logic Controller

An Advanced Fuzzy Logic Based Traffic Controller

2014 ◽  
Vol 5 (1) ◽  
pp. 31-40
Author(s):  
Bilal Ahmed Khan ◽  
Nai Shyan Lai
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Traffic Management ◽  
Fuzzy Logic Controller ◽  
Current Model ◽  
Traffic Signals ◽  
Processing Technique ◽  
Urban Traffic ◽  
Image Processing Technique ◽  
Traffic Light

Traffic light plays an important role in the urban traffic management. Therefore, it is necessary to improve the traffic controller for effective traffic management and better traffic flow leading to greener environment. In this paper, an advanced and intelligent traffic light controller is proposed, utilising the fuzzy logic technology and image processing technique. A fuzzy logic control has been implemented to provide the attribute of intelligence to the system. For real-time image acquisition, the process is further linked to the fuzzy logic controller which generates a unique output for each input pattern. Here image processing and fuzzy logic tool boxes of MATLAB are used where the final output is sent to Peripheral Interface Controller (PIC) microcontroller to drive the traffic signals in the desired manner. The results obtained show an improvement of 44% in the overall outcome of traffic management as compared to the conventional traffic controller, marking great feasibility and practicality of the current model.

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

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1254 ◽  
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.

scholarly journals Direct Torque Control of Doubly Star Induction Motor Using Fuzzy Logic Speed Controller

2018 ◽  
Vol 7 (1) ◽  
pp. 42
Author(s):  
Lallouani Hellali ◽  
Saad Belhamdi
Keyword(s):  
Fuzzy Logic ◽  
Induction Motor ◽  
Fuzzy Logic Controller ◽  
Direct Torque Control ◽  
Pi Controller ◽  
General Purpose ◽  
Torque Control ◽  
Speed Controller ◽  
Wide Range ◽  
Time Required

<p>This paper presents the simulation of the control of doubly star induction<br />motor using Direct Torque Control (DTC) based on Proportional and Integral<br />controller (PI) and Fuzzy Logic Controller (FLC). In addition, the work<br />describes a model of doubly star induction motor in α-β reference frame<br />theory and its computer simulation in MATLAB/SIMULINK®.The structure<br />of the DTC has several advantages such as the short sampling time required<br />by the TC schemes makes them suited to a very fast flux and torque<br />controlled drives as well as the simplicity of the control algorithm.the<br />general- purpose induction drives in very wide range using DTC because it is<br />the excellent solution. The performances of the DTC with a PI controller and<br />FLC are tested under differents speeds command values and load torque.</p>

scholarly journals Implementation of Fuzzy Logic Controller for Wall Following and Obstacle Avoiding Robot

2019 ◽  
Vol 4 (1) ◽  
pp. 9-21
Author(s):  
Aryuanto Soetedjo ◽  
M. Ibrahim Ashari ◽  
Cosnas Eric Septian
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Fuzzy Logic Controller ◽  
Low Cost ◽  
Experimental Results ◽  
Raspberry Pi ◽  
Ultrasonic Sensors ◽  
Obstacle Avoiding ◽  
Wall Following

This paper presents the development of wall following and obstacle avoiding robot using a Fuzzy Logic Controller. The ultrasonic sensors are employed to measure the distances between robot and the wall, and between the robot and the obstacle. A low cost Raspberry Pi camera is employed to measure the left/right distance between the robot and the obstacle. The Fuzzy Logic Controller is employed to steer the mobile robot to follow the wall and avoid the obstacle according to the multi sensor inputs. The outputs of Fuzzy Logic Controller are the speeds of left motor and right motor. The experimental results show that the developed mobile robot could be controlled properly to follow the different wall positions and avoid the different obstacle positions with the high successful rate of 83.33%.

scholarly journals PID and Fuzzy Logic Controller Design for Balancing Robot Stabilization

2018 ◽  
pp. 1-10
Keyword(s):  
Fuzzy Logic ◽  
Tilt Angle ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Position Control ◽  
Controller Design ◽  
Point Of View ◽  
Relative Advantage ◽  
Balancing Robot ◽  
Time Required

This paper addresses the problem of position control and stabilization for the two wheeled balancing robot. A mathematical model is derived based on the robot’s position and tilt angle and a fuzzy logic control is proposed for the balancing robot control. The fuzzy logic controller performance is compared with a conventional PID controller to show the difference between them. Both controllers were tested on the balancing robot in simulation using MATLAB software and the results were put together for a comparative point of view. The simulations shows a relative advantage for the fuzzy logic controller over the conventional PID controller especially in reducing the time required for stabilization which takes about 2 seconds and almost without overshoot while in the PID case the robot will have about 10% overshoot in position and about 20 degrees in tilt angle.

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