Research of smart real-time robot navigation system

Rahmani, Budi; Harjoko, Agus; Priyambodo, Tri Kuntoro; Aprilianto, Hugo

doi:10.1063/1.4940854

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…When using a stereo camera, the system will obtain the disparity map based on a pair of rectified images. After that, the system will calculate the approximate distance between objects detected by a calibrated camera [1,2]. A stereo camera-based navigation system implemented in [3,4].…”

Section: Introductionmentioning

confidence: 99%

A vision-based real-time obstacle avoidance’s rules utilising grid-edge-depth map

Rahmani¹,

Harjoko

Priyambodo

2020

IJEECS

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This paper presents a new rules-based of a real-time decision system for an autonomous wheeled robot with the holonomic-drive system. The robot uses decisions to avoid collisions with obstacles. The decision rules based on Grid-Edge-Depth Map. The Grid-Edge-Depth map represents the obstacle’s position and distance in the environment. The generation process of the Grid-Edge-Depth map presented in previous research. The decisions of the first scenario with no destination point are forward, stop, 90o right turn, and 90o left turn. The decisions of the second, and third scenarios with a destination point are forward, stop, 90o right turn, 90o left turn, 45o forward to the right, 45o forward to the left, slide to the right, and slide to the left. The proposal tested in a 5x3 metre living environment. Finally, the experiment resulted in 93.3% of navigation’s success for all the scenarios.

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Section: Introductionmentioning

confidence: 99%

A vision-based real-time obstacle avoidance’s rules utilising grid-edge-depth map

Rahmani¹,

Harjoko

Priyambodo

2020

IJEECS

Self Cite

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“…Navigation may define as the ability to move in any particular environment [1]. Other researchers define it as a science in guiding a robot to move to its environment [2]. As for the problems that accompany this process is the navigation can be defined in three questions, namely: "Where am I", "Where am I going", and "How do I get there".…”

Section: Introductionmentioning

confidence: 99%

Distance Estimation based on Color-Block: A Simple Big-O Analysis

Rahmani¹,

Aprilianto²,

Ismanto³

et al. 2017

IJECE

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This paper explains how the process of reading the data object detection results with a certain color. In this case, the object is an orange tennis ball. We use a Pixy CMUcam5 connecting to the Arduino Nano with microcontroller ATmega328-based. Then through the USB port, data from Arduino nano re-read and displayed. It's to ensure weather an orange object is detected or not. By this process, it will be exactly known how many blocks object detected, including the X and Y coordinates of the object. Finally, it will be explained the complexity of the algorithms used in the process of reading the results of the detection orange object.

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“…It can be a vision, sonar, or another kind of sensors. Vision-based mobile robot mostly equipped at least with one camera, and others equipped with dual or more camera at the same time [8]. Research has been conducted on building a map of static obstacles, for examples building an unknown outdoor environmental map as [9].…”

Section: Introductionmentioning

confidence: 99%

Grid-Edge-Depth Map Building Employing Sad With Sobel Edge Detector

Rahmani

Harjoko

Priyambodo

2017

International Journal on Smart Sensing and Intelligent Systems

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-This paper presents a new method of a map building which is suitable for a wheeled robot. The 2D map represents the obstacle's position and distance in the environment. The information of the obstacles obtained from a calibrated stereo camera. The stereo images size were 320x240 pixels. Hereafter the images were rectified and the disparity map was built using a Sum of Absolute Difference (SAD) algorithm. The depth map was calculated using disparity map, focal length, and baseline parameter values. In order to detect the obstacles, Sobel edge detection was implemented. The edge detection image was compared and substituted with the depth map which is resulting edge-depth map. The edge-depth map was divided into 25 grids (5 grids horizontal and 5 grids vertical). Finally, the minimum depth of detected obstacles for each grid was calculated. This process was resulting in a grid-edge-depth map (GED map). The proposal has been tested with a mobile robot in 5x3 meters living environment. Finally, experimental results are presented. The average error of feature points in the previous study was 5.40 cm, whereas in this study is 3.82 cm. There has been a decrease in the measurement gap of 29.26% from the previous study.

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Research of smart real-time robot navigation system

Cited by 3 publications

References 15 publications

A vision-based real-time obstacle avoidance’s rules utilising grid-edge-depth map

A vision-based real-time obstacle avoidance’s rules utilising grid-edge-depth map

Distance Estimation based on Color-Block: A Simple Big-O Analysis

Grid-Edge-Depth Map Building Employing Sad With Sobel Edge Detector

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