## Potential Field Path Planning Simulation

Instructions
Left mouse click on the map to drop the green ball
Move the obstacle using the <g> key
<w> increase sphere of influence of an obstacle
<s> decrease sphere of influence of an obstacle

Potential Field Obstacle Avoidance
Robots need to be able to avoid obstacles and one such method is Potential Fields. Potential Fields Obstacle Avoidance is an adaptation of the movement of charged particles into the field of robotics where obstacles generate a ‘rejecting’ field and the goal generates an ‘attractive’ field. When these two fields are added together you get a field, indicating the robot’s motion at that specific position on the map.

Mobile robots generally need to move from a START position (0) to a GOAL position (1) in order to accomplish tasks.

The simulator makes use of the Quad Tree method to create a distance graph and then this distance graph is used by the A* (A-star) path finding algorithm to find the best path.

##### Instructions

Click on the map to create an obstacle (Pink square = obstacle, Green square = no obstacle)
Use the ‘g’ key to change the GOAL position
Use the ‘s’ key to change the START position

##### Note:

The map’s grid can be expanded to accommodate a larger map. For this simulation, however, it is fixed to an 8×8 array.

Although the Quad Tree method is applied using a grid, the robot can move freely around inside each grid and is not bound by the center point of any quad.

The numbers next to each Node ID is the values used by the A-star algorithm to determine the best path. The tutorial will follow soon explaining all the detail.

## Particle Filter Localization Simulation

Let us assume the following: You want a robot that will fetch something from somewhere or should take something to someplace. In order for this mobile robot to know if it reached the goal, or to plan a path to the goal, it needs to know where it is.

This ‘knowing where it is’ is known as localization. One method used for localization is Particle Filters (Monte Carlo Localization) and this simulation implements particle filter localization.

The simulation consists of RED particles, a GREEN robot and BLUE landmarks (add landmarks by LEFT clicking on the map). By moving the robot around using the w,a,d keys you will see how the particles cluster around the robot, essentially showing where the robot is. (NOTE: If you do not add any landmarks the robot will not be able to localize.)

## Fitting a Linear Line to Data Points

A line of best fit is a line that is the best approximation of a given set of data. This line can be linear, exponential, logarithmic, polynomial, a power average or a moving average and will depend on the type of data you have and the purpose of the approximated line. Your dataset can even be in 3 dimensions.

I’m only going to look at 2D data (ordered pairs) and the Least Square Method to find a straight line ($y=mx+c$) that best fit the given data points.

## HB-25 LabVIEW VI

A couple of years ago I bought a Made In USA robot platform from Parallax Inc. which has been standing around doing nothing. The time is NOW, to get it up and running.

The platform includes 2 x HB-25 motor controllers which I want to interface to LabVIEW.

This VI maps an input which range form -100 to 100 to an input which will work with the HB-25 in mode 1.

## Linear Mapping of Input Range to Output Range

Whenever you mix sensor input values and electronics, you always get a situation where the data read from the sensor needs to be interpreted or changed or scaled to be used by your control software.

Mapping or scaling a linear range of input values to a linear range of output values is as easy as: $y=mx+c$

## Bio-metric Attendance

Attendance is part of any learning environment. If you do not attend the lectures or practical sessions, your chances of failure increases.

For years I took attendance either by making a class list available for the students to sign or by calling out each student’s name. But this was only part of the process! The other part involved entering the attendance onto either a spreadsheet or web based system in order to gather some statistics. For small classes this might not be an issue, but for large classes… It becomes a mess!!

Since I love automation, I decided to design a bio-metric attendance solution using a fingerprint scanner, an Arduino Mega and an ITDB3.2 inch TFT LCD.

## Voltage Conversion Hack

Have you ever wanted to convert 3.3V to 5V? Well I had to recently come up with a hack since I did not have the correct driver logic chips.

Here’s how…

## CSE Vision

As it is with most institutions, employees have office numbers situated in some or other passage. In order to see someone specific, you go to the administrator/secretary to find out if said person is around or you walk aimlessly up and down the passage hoping to find someone that can at least point you in the right direction.

This is frustrating… for EVERYBODY!

CSE Vision intends to solve this problem.

# So I broke my touch screen …

I’ve been busy with a new project where I use the 3.2″ TFT Touch screen from ITEAD Studio. While testing the application I noticed that the back-light’s brightness change every time I push a button on the screen. AHAA!! – Bad connection! So I started pressing on the edges of the screen, to find out where this is coming from…

SNAP!!!

The screen broke…

1 2