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IAES International Journal of Robotics and Automation (IJRA)
Published by Institute of Advanced Engineering and Science
ISSN : 20894856     EISSN : 27222586     DOI : -
Core Subject : Engineering,
Automotive Engineering Electrical & Electronics Engineering
Robots are becoming part of people's everyday social lives and will increasingly become so. In future years, robots may become caretaker assistants for the elderly, or academic tutors for our children, or medical assistants, day care assistants, or psychological counselors. Robots may become our co-workers in factories and offices, or maids in our homes. The IAES International Journal of Robotics and Automation (IJRA) is providing a platform to researchers, scientists, engineers and practitioners throughout the world to publish the latest achievement, future challenges and exciting applications of intelligent and autonomous robots. IJRA is aiming to push the frontier of robotics into a new dimension, in which motion and intelligence play equally important roles. Its scope includes (but not limited) to the following: automation control, automation engineering, autonomous robots, biotechnology and robotics, emergence of the thinking machine, forward kinematics, household robots and automation, inverse kinematics, Jacobian and singularities, methods for teaching robots, nanotechnology and robotics (nanobots), orientation matrices, robot controller, robot structure and workspace, robotic and automation software development, robotic exploration, robotic surgery, robotic surgical procedures, robotic welding, robotics applications, robotics programming, robotics technologies, robots society and ethics, software and hardware designing for robots, spatial transformations, trajectory generation, unmanned (robotic) vehicles, etc.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 8 Documents
 1 
Search results for , issue "Vol 10, No 2: June 2021" : 8 Documents clear
Parallel P-PD controller to achieve vibration and position control of a flexible beam Ammar N. Abbas; Muhammad Asad Irshad
IAES International Journal of Robotics and Automation (IJRA) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v10i2.pp149-160

Abstract

Robotic arms are considered as a cantilever beam fixed at one end and due to the length-to-weight ratio, it has a significant vibration-induced that needs to be controlled to achieve accurate position, speed control and to increase its efficiency. In this project, a discretized Timoshenko beam model is used to discuss the dynamics of the system. Further, to implement the control on the hardware an experimental setup is fabricated to observe the open-loop and closed-loop responses of the beam made of low-density polyethylene. An accelerometer as a feedback sensor is attached at one end of the flexible beam while another end is fixed at the moving cart having DC motor as an actuator. Simulink is used as the programming tool to perform all of the experimentation. Proportional-integral-derivative (PID) tuning is performed. Following that open-loop responses of the deflection of the beam parallel to the motion are observed with different input waveforms. By applying a proportional control scheme, another experiment is performed to demonstrate the disturbance rejection with an accelerometer as a feedback sensor, while ignoring position control. Finally, a PD and P based parallel control scheme is proposed to obtain simultaneously both position control and vibration reduction.
Missing data handling for machine learning models Karim H. Erian; Pedro H. Regalado; James M. Conrad
IAES International Journal of Robotics and Automation (IJRA) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v10i2.pp123-132

Abstract

This paper discusses a novel algorithm for solving a missing data problem in the machine learning pre-processing stage. A model built to help lenders evaluate home loans based on numerous factors by learning from available user data, is adopted in this paper as an example. If one of the factors is missing for a person in the dataset, the currently used methods delete the whole entry therefore reducing the size of the dataset and affecting the machine learning model accuracy. The novel algorithm aims to avoid losing entries for missing factors by breaking the dataset into multiple subsets, building a different machine learning model for each subset, then combining the models into one machine learning model. In this manner, the model makes use of all available data and only neglects the missing values. Overall, the new algorithm improved the prediction accuracy by 5% from 93% accuracy to 98% in the home loan example.
Wireless stepper motor control and optimization based on robust control theory Waleed Khalid Al-Azzawi
IAES International Journal of Robotics and Automation (IJRA) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v10i2.pp144-148

Abstract

Stepper motors are broadly utilized in actual systems, which are marked by non-linear parameters such as internal, external noises and uncertainties from wireless network. As well, a suitable controller is required when the problem is to track the target signal. In this paper, robust controller based on model reference are investigated to wireless control and optimize position and time in stepper motors. The core impression to build a robust controller is to use a model reference control system. Furthermore, simulations are implemented to control stepper motor position and time in two cases: first, when the wireless network without any delay and packet dropout. Second, uncertain equations when the wireless network with time delays and packet dropout. Simulation results demonstrate that proposed controller has achieved and enhanced the performance in tracking and robustness.
Design and development of an irrigation mobile robot Ahmed Hassan; Rao M. Asif; Ateeq Ur Rehman; Zuhaib Nishtar; Mohammed K. A. Kaabar; Khan Afsar
IAES International Journal of Robotics and Automation (IJRA) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v10i2.pp75-90

Abstract

Water plays a significant role among other existing natural resources. The daily demand for water supplies is increasingly on the rise as the population grows. To minimize the consumption of water in irrigation, several proposals were suggested. The currently existing system known as the automated irrigation system for effective water resource use with the prediction of the weather (AISWP) functions with a single farm that lacks the reliability in the precision of weather forecasting. So, a robot-based irrigation system has been proposed to improve the performance of the system. To minimize the water usage for crops, an automated irrigation system has been developed which irrigates the field in acres. An additional characteristic of the system has also been given for the soil pH measurement to allow the use of fertilizers accordingly. The solar-powered robot is managed wirelessly by a designated application. The robot is attached with various sensors and with a high-resolution camera that tests crop conditions and senses the soil state. The application has been created to provide information about the soil’s condition such as temperature level, humidity level, water level, and level of nutrients to the PC/Laptop with the real-time values via the GSM module.
Switched time delay control based on neural network for fault detection and compensation in robot Maincer Dihya; Mansour Moufid; Boudjedir Chemseddine; Bounabi Moussaab
IAES International Journal of Robotics and Automation (IJRA) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v10i2.pp91-103

Abstract

Fault detection in robotic manipulators is necessary for their monitoring and represents an effective support to use them as independent systems. This present study investigates an enhanced method for representation of the faultless system behavior in a robot manipulator based on a multi-layer perceptron (MLP) neural network learning model which produces the same behavior as the real dynamic manipulator. The study was based on generation of residue by contrasting the actual output of the manipulator with those of the neural network; Then, a time delay control (TDC) is applied to compensate the fault, in which a typical sliding mode command is used to delete the time delay estimate produced by the belated signal in order to obtain strong performances. The results of the simulations performed on a model of the SCARA arm manipulator, showed a good trajectory tracking and fast convergence speed in the presence of faults on the sensors. In addition, the command is completely model independent, for both TDC and MLP neural network, which represents a major advantage of the proposed command.
A discrete-time terminal sliding mode controller design for an autonomous underwater vehicle Nira Mawangi Sarif; Rafidah Ngadengon; Herdawatie Abdul Kadir; Mohd Hafiz A. Jalil; Khalid Abidi
IAES International Journal of Robotics and Automation (IJRA) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v10i2.pp104-113

Abstract

Autonomous underwater vehicle (AUV) are underwater robotic devices intended to explore hostiles territories in underwater domain. AUVs research gaining popularity among underwater research community because of its extensive applications and challenges to overcome unpredictable ocean behavior. The aim of this paper is to design discrete time terminal sliding mode control (DTSMC) reaching law-based employed to NPS AUV II purposely to improve the dynamic response of the closed loop system. This is accomplished by introducing a nonlinear component to sliding surface design in which the system state accelerated, and chattering effect is suppressed. The nonlinear component consist of fractional power is to ensure steeper slope of the sliding surface in the vicinity of the equilibrium point which lead to quicker convergence speed. Thus, the chattering effect in the control action suppressed as the convergence of the system state accelerated. The stability of the control system is proven by using Sarpturk analysis and the performance of the DTSMC is demonstrated through simulation study. The performance of DTSMC is benchmarked with DSMC and PID controller
Detecting African hoofed animals in aerial imagery using convolutional neural network Yunfei Fang; Shengzhi Du; Larbi Boubchir; Karim Djouani
IAES International Journal of Robotics and Automation (IJRA) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v10i2.pp133-143

Abstract

Small unmanned aerial vehicles applications had erupted in many fields including conservation management. Automatic object detection methods for such aerial imagery were in high demand to facilitate more efficient and economical wildlife management and research. This paper aimed to detect hoofed animals in aerial images taken from a quad-rotor in Southern Africa. Objects captured in this way were small both in absolute pixels and from an object-to-image ratio point of view, which were not perfectly suit for general purposed object detectors. We proposed a method based on the iconic Faster region-based convolutional neural networks (R-CNN) framework with atrous convolution layers in order to retain the spatial resolution of the feature map to detect small objects. A good choice of anchors was of prime importance in detecting small objects. The performance of the proposed Faster R-CNN with atrous convolutional filters in the backbone network was proven to be outstanding in our scenario by comparing to other object detection architectures.
Detection of duplicate and non-face images in the eRecruitment applications using machine learning techniques Manjunath K. E.; Yogeen S. Honnavar; Rakesh Pritmani; Sethuraman K.
IAES International Journal of Robotics and Automation (IJRA) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v10i2.pp114-122

Abstract

The objective of this work is to develop methodologies to detect, and report the noncompliant images with respect to indian space research organisation (ISRO) recruitment requirements. The recruitment software hosted at U. R. rao satellite centre (URSC) is responsible for handling recruitment activities of ISRO. Large number of online applications are received for each post advertised. In many cases, it is observed that the candidates are uploading either wrong or non-compliant images of the required documents. By non-compliant images, we mean images which do not have faces or there is not enough clarity in the faces present in the images uploaded. In this work, we attempt to address two specific problems namely: 1) To recognise image uploaded to recruitment portal contains a human face or not. This is addressed using a face detection algorithm. 2) To check whether images uploaded by two or more applications are same or not. This is achieved by using machine learning (ML) algorithms to generate similarity score between two images, and then identify the duplicate images. Screening of valid applications becomes very challenging as the verification of such images using a manual process is very time consuming and requires large human efforts. Hence, we propose novel ML techniques to determine duplicate and non-face images in the applications received by the recruitment portal.

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