Development of a robotic arm for dangerous object disposal

Oke, Alice; Afolabi, A. O.

doi:10.1109/csit.2014.6805994

Cited by 12 publications

(4 citation statements)

References 3 publications

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“…Robots are required to complete difficult human task, such as high accuracy task, high risk task, repetitive task or some tasks that require amount of energy [1]. Arm robot often called industrial robot (defined by ISO 8373) is widely used in the industry [2] especially in pick and place task [3].…”

Section: Introductionmentioning

confidence: 99%

Pattern recognition based movement control and gripping forces control system on arm robot model using LabVIEW

Ramadhan

Lilansa

Rifa'i

et al. 2022

J. Mechatron. Electr. Power Veh. Technol.

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Most arm robot has an inefficient operating time because it requires operator to input destination coordinates. Besides, main problem of arm robot is object’s vulnerability when it is manipulated by the robot. This research goals is to develop an arm robot control system which has ability to automatically detect object using image processing in order to reduce operating time. It is also able to control gripping force for eliminating damage to objects caused by robot gripper. This research is implemented in LabVIEW 2011 software to control arm robot model which can represent industrial scale robot. The software is designed with informative visualization to help user learn and understand robotic control concept deeply. The system can automatically detect object position based on pattern recognition method which has four steps: pre-processing process to initialize picture taken by camera, segmentation process for separating object from the background, classification process to determine characteristics of object, and position estimation process to estimate object position in the picture. The object’s position data are then calculated by using kinematic equation to control the robot’s motion. The results show that the system is able to detect object and move the robot automatically with accuracy rate in x-axis is 95.578 % and in y-axis is 92.878 %. The system also implements modified PI control method with FSR as input to control gripping force with maximum overshoot value 10 %. Arm robot model control system developed is successfully meet the expectation. The system control can be implemented to industrial scale arm robot with several modification because of kinematic similarity between model and industrial scale robot.

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Section: Introductionmentioning

confidence: 99%

Pattern recognition based movement control and gripping forces control system on arm robot model using LabVIEW

Ramadhan

Lilansa

Rifa'i

et al. 2022

J. Mechatron. Electr. Power Veh. Technol.

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“…This capability can be useful in some other tasks; however, there are few studies that are concerned with the detection of threat objects for EOD robots. Most of them focus on the specific part of the robot, like the robot arm [7,8,9], instead of its vision system, which is also a vital part of the robot design. The vision system serves as the guide of the robot operator in navigating the environment and in detecting unknown objects.…”

Section: Introductionmentioning

confidence: 99%

Threat object detection and analysis for explosive ordnance disposal robot

Galvez¹,

Dadios²

2022

Global J. Eng. Tech. Adv.

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Explosive Ordnance Disposal (EOD) robots are useful in military applications like the safe disposal of explosives. However, many of these robots do not have the capability to identify threat objects using their onboard vision system due to data unavailability for training an improvised explosive device (IED) detector. As a solution, this study used image processing and object detection algorithms to detect and analyze threat objects inside the baggage. A threat object detector was developed and composed of two separate modules such as baggage detection and IED detection and analysis modules. The experiments showed that baggage detection achieved 22.82% mean average precision (mAP) using Single Shot Detector (SSD) in the Microsoft Common Objects in Context (COCO) dataset, while IED detection achieved 77.59% mAP using Faster R-CNN in the X-ray dataset. The threat objects from the X-ray image were also analyzed using image processing techniques to get the dimension of the object and the distance from a reference object. Also, the baggage detection module was successfully deployed in Jetson TX2, which runs at a frame rate of 12 frames per second (FPS).

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“…Armed antiinfiltration robot based on peripheral interface controller using infrared range technique with a single disc system was investigated by Singh [3]. The development of robot arms for the disposal of dangerous objects was investigated by [4]. The design and implementation of unmanned, long-distance unmanned ground vehicles (UGVs) at low cost were examined by [5].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Line Follower Robot based Microcontroller ATMega32A

Latif

Widodo

Rahim

et al. 2020

jrc

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The development of technology in the field of robotics is very fast, but in the eastern regions of Indonesia, namely, the development of the development has not yet felt the impact. Especially in the Universitas Islam Sultan Agung learning media devices for microcontrollers are also not yet available. Therefore the author wants to pioneer by implementing the simplest robot design, the line follower robot, where the robot only goes along the lines. This study uses an experimental method, by conducting a research process based on sequences, namely: needs analysis, mechanical chart design, electronic part design, and control program design, manufacturing, and testing. The line follower robot based on the ATmega32A microcontroller has been tested, and the results show that the line follower robot can walk following the black line on the white floor and can display the situation on the LCD. But this line follower robot still has shortcomings in the line sensor sensitivity process depending on a certain speed. At speeds of 90-150RPM, the line follower robot can follow the path, while more than 150 rpm, the robot is not able to follow the path.

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Development of a robotic arm for dangerous object disposal

Cited by 12 publications

References 3 publications

Pattern recognition based movement control and gripping forces control system on arm robot model using LabVIEW

Pattern recognition based movement control and gripping forces control system on arm robot model using LabVIEW

Threat object detection and analysis for explosive ordnance disposal robot

Implementation of Line Follower Robot based Microcontroller ATMega32A

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