Swarm Robot: Self-localization vs Target/Source Localization

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Swarm Robot: Self-localization vs Target/Source Localization

By: Mad Helmi Bin Ab. Majid (PhD Student)

 

Localization is basically a process of identifying location or position of the robot itself or the target of interest with respect to a known landmark or reference. In swarm robotics research, localization is subcategorized as self-localization and object/source localization. Self-localization involves a process identifying a robot position in an unknown environment where inertial positioning system (such as GPS) fails to work. On the other hand, object or source localization is a process of identifying the position of a target or location of a source of some entities such as acoustic pinger, sound, chemical plume, color etc. The comparisons between the two types of localization for swarm robotic are summarized in the following Table 1.

 

Table 1: Comparison between self-localization and object/source localization

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Color Restoration for Underwater Object Detection and Tracking

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Color Restoration for Underwater Object Detection and Tracking

By: Mohd Faid Bin Yahya (PhD Student)

 

Underwater robots have been one of the most actively undergoing researches in the world as it is the pinnacle subject in the field of robotics and ocean engineering. Some of the applications which utilizes underwater robot are underwater monitoring, seabed mapping, and underwater structure inspections. The ability for the underwater robots to autonomously interact with the underwater environment is crucial for the success of aforementioned applications. One of the most important aspects which prove to be a very challenging issue is the usage of underwater vision. The reason being is due to the limited detection ranges and the blurry underwater visions. Being able to use vision when a robot is in underwater will open up new underwater application opportunities such as in sensing and close range detections.

In order to overcome the drawbacks of using vision in underwater, the underwater color restoration algorithm had been introduced in [1]. The algorithm has been conducted on an underwater robot. Also, the algorithm has been tested in the real time image processing scenarios. Figure 1 shows the underwater target objects where the algorithms had been tested to differentiate and tracked all of them at once.

 

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Figure 1. Underwater target objects.

 

Accordingly, Figure 2 below shows that the algorithm successfully differentiate as well as track between the cross,
circle, cone, and cylindrical shape objects.

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Figure 2. Target object tracking with labels 1,2,3, and 4 in the images denote cross, cone, sphere, and cylinder, respectively.

 

For future works, other forms of objects can be tested and further improvement can be made by implementing it into a real autonomous underwater vehicle.

 

Reference(s):

  1. L. Donghwa, K. Gonyup. K. Donghoon, M. Hyun, and C. Hyun-Taek. (2012). Vision-based object detection and tracking for autonomous navigation of underwater robots. Ocean Engineering48(2012)59-68.

 

Accelerometer ADXL 345 Reliability Test

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Accelerometer ADXL 345 Reliability Test

By: Song Yoong Siang (PhD Student)

 

The reliability of accelerometer ADXL 345 is tested in order to know whether it gives correct information of roll and pitch angle. Two tests are designed. One for testing roll angle measurement and the other for testing pitch angle measurement. For roll angle, a wire is fixed vertically at a frame equipped with accelerometer using masking tape as shown in Figure 1(a). This wire is acting as a reference point. Next, a protractor paper is placed in front of the wire as shown in Figure 1(b). The test is done by rotating the frame until the wire (reference point) points to 30°. The roll angle displayed by the accelerometer is recorded. The testing is repeated 7 times by increasing angle 30 degree clockwise per time. For pitch angle, the test is same as the test for roll angle, but the reference wire is fixed horizontally on the frame. Figure 1(c) and (d) show the position of reference wire and protractor paper for pitch angle reliability test.

 

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Figure 1: (a) A wire is fixed vertically at the frame using masking tape (b) Protractor paper is placed in front of the wire (c) A wire is fixed vertically at the frame using masking tape (d) Protractor paper is placed in front of the wire

 

                The reliability test of accelerometeron roll angle is shown in Table 1. Roll angle measured by the protractor paper is used as reference to calculate the error of roll angle measured by accelerometer. The accelerometershows 0° when the vehicle is straight. When the vehicle is inclined to right side, the roll angle is positive and vice versa. The roll angle measured by accelerometeris taken 20 times in order to observe its consistency. From Table 1, the biggest range of the three reading for difference roll angle is 3.64°. It is considered small and acceptable.

 

Table 1: Reliability test result of accelerometer on roll angle

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Figure 2 shows the individual chart of error of Accelerometer versus roll angle. The range of control limit of the chart is from -11.65° to 1.94°. The average value is -4.85°. The error of the Accelerometer is due to the improper position of the Accelerometer. As shown in Figure 2, when Accelerometer is installed into the circuit board, it is a bit inclined. This will affect the reading of the Accelerometer. Besides, the roll angle measured by the protractor paper is not precise. Since the size of vehicle is big, it is difficult to turn the vehicle to desired roll angle. Therefore, the error of roll angle measured by Accelerometer is considered acceptable.

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Figure 2: Individual chart of error of Accelerometer versus roll angle

         

The reliability test of accelerometeron pitch angle is shown in Table 2. Pitch angle measured by the protractor paper is used to compare the pitch angle measured by Accelerometer. The Accelerometer shows 0° when the vehicle is straight. When the vehicle is inclined upward, the pitch angle is positive and vice versa. The pitch angle measured by Accelerometer is taken 20 times in order to observe its consistency. From Table 2, the biggest range of the three readings for difference roll angle is 4.20°. It is considered small and acceptable.

 

Table 2: Reliability test result of Accelerometer on pitch angle

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Figure 3 shows the individual chart of error of Accelerometer versus pitch angle. The range of control limit of the chart is from -6.05° to 6.64°. The average value is 0.30°. Same as measuring for roll angle, the error of the Accelerometer is due to the improper position of itself. As shown in Figure 3, when Accelerometer is installed into the circuit board, it is a bit inclined. This will affect the reading of the Accelerometer. Besides, the pitch angle measured by the protractor paper is not precise. Since the size of vehicle is big, it is difficult to turn the vehicle to desired roll angle. Therefore, the error of pitch angle measured by Accelerometer is considered acceptable.

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Figure 3: Individual chart of error of Accelerometer versus pitch angle

Reference(s):

  1. (9 Jan). IMU digital combo board - 6 Degree of Freedom ITG3200/ADXL345 - SparkFun Electronic [Online]. Available: https://www.sparkfun.com/products/10121


 


CFD Tutorial Part 1: Introduction -ANYSY 15.0 for Beginner (Window Version)

 

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-ANYSY 15.0 for Beginner (Window Version)

By: Herdawatie Abdul Kadir (PhD Student)

 

In this tutorial, I will explain briefly the basic of CFD before we go to the ANSYS 15.0 (Fleuent tutorial). First you need to undertand what is CFD and ANSYS

What is CFD ?

CFD stands for Computational Fluid Dynamics used to solve and analyze impact of fluid flows on product using Numerical method. There are several disretization methods such as(1) Finite element method (2)  

Finite difference method (3) Spectral element method (4) Finite volume method (5) High-resolution discretization schemes.

There several CFD Package available such as :

1.OpenFOAM : http://www.openfoam.com

2.OpenFlower: http://sourceforge.net/projects/openflower

3.FLASH:http://flash.uchicago.edu

4.GADGET: http://www.mpa-garching.mpg.de/~volker/gadget

5.ZEUS-MP:

HYDRA: http://hydra.mcmaster.ca/hydra

COMSOL Multiphysics: http://www.comsol.com/products/multiphysics

CFDRC:: http://www.cfdrc.com

STAR-CD: http://www.cd-adapco.com

FLOW3D: http://www.flow3d.com

11.ANSYS CFX : Solver : Finite Element http://www.ansys.com/Products/Simulation+Technology/Fluid+Dynamics/Fluid+Dynamics+Products/ANSYS+CFX

12.ANSYS Fluent : Solver : Finite Volume  

http://www.ansys.com/Products/Simulation+Technology/Fluid+Dynamics/Fluid+Dynamics+Products/ANSYS+Fluent

13.ANSYS ICEM CFD : http://www.ansys.com/Products/Other+Products/ch.ANSYS+ICEM+CFD.it

14.ANSYS CFD-Flo : http://www.ansys.com/Products/Simulation+Technology/Fluid+Dynamics/Fluid+Dynamics+Products/ANSYS+CFD-Flo

 

Next, the selection of which CFD package to used depends on the type of problem you want to solve . We will focus only on ANSYS in this tutorial. ANSYS is a computational fluid dynamics (CFD) simulation software that allows prediction of fluid flows on product with confidence an accuracy throughout design and manufacturing as well as during end use.  There are two famous ANSYS bundle (a) ANSYS CFX   (b) ANSYS Fluent

“ANSYS CFX is a robust, flexible general-purpose computational fluid dynamics software package used to solve wide-ranging fluid flow problems of all levels of complexity within the Workbench environment. It offers a comprehensive range of physical models that can be applied to a broad range of industries and applications, with extensive capabilities for customization and automation”[2].

“ANSYS Fluent is a powerful and flexible general-purpose computational fluid dynamics software package used to model flow, turbulence, heat transfer, and reactions for industrial applications. The physical models allow accurate CFD analysis for a wide range of fluids problems - from airflow over an aircraft wing to combustion in a furnace. ANSYS Fluent is integrated into ANSYS Workbench”[2].

 

Different of ANSYS CFX and ANSYS Fluent ?

1) CFX more user friendly than Fluent

2) Fluent is the more famous than CFX

3) ANSYS FLUENT product offers several solution approaches

     -density-, segregated- and coupled-pressure-based methods.

4) ANSYS CFX software focuses on one approach to solve the governing equations of motion

     -coupled algebraic multigrid.

5) ANSYS CFX use Finite Element Slover and Fluent use Finite Solver

 

Table 1-1 Comparison between FLUENT and CFX[3]

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Reference(s):

  1. http://www.mallett.com/ansys-products/fluid-dynamics-solutions/
  2. ANSYS , Fluid Dynamics :   http://www.ansys.com/Products/Simulation+Technology/Fluid+Dynamics
  3. http://libback.uqu.edu.sa/hipres/indu/indu10725.pdf, pp. 9-11

 

 

 

 

Time Difference of Arrival (TDOA) using Cross-correlation

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Time Difference of Arrival (TDOA) using Cross-correlation

By: Mad Helmi Bin Ab. Majid (PhD Student)

 

Time difference of arrival (TDOA) is important for acoustic based localization. From TDOA it is possible to compute the location and direction of the sound source. Once TDOA is obtained, by incorporating multilateration operation position of the sound source can be estimated. In this article, we are going to discuss how cross-correlation could be utilized to obtain TDOA. Cross-correlation is a process of determining similarity between the two input signals. TDOA is determined by determine the time lag between the two signals. The overall process of estimating TDOA is illustrated in Figure 1 and example of the time lag is shown in Figure 2.

 

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Figure 1 Cross-correlation based TDOA estimation process

 

The cross-correlation of the two signals Siand Sj is represents by the following equation

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where[ ]* is a complex conjugate of a function. Following this definition, TDOA is computed as follows

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Figure 2 Maximum peak corresponding to time lag between two signals