Project Abstracts

Semester A, 2006-2007



Alex Shlizer, Barak Drezner and Gilad Schwartz

Environment: Hardware: ABB 571 Controller; Software: WizCon – graphics, CodeSys – ladder diagram programming.

The goal of the project is to design a Tic-Tac-Toe game using Human-Machine-Interface (HMI) supplied by WizCon, and to program the ABB 571 controller for operating the game.

The outputs of the graphical side are sent to the ABB controller and then - to the ER7 robot controller.

The Tic-Tac-Toe interface is built using the WizCon software. The game management was created by programming the ladder diagram flow in the CoDeSys environment.

The interface runs on two client computers; the server side, which manages the game, runs on the ABB controller.

The communication between the HMI interfaces and the ABB conducts via Internet by TCP\IP Protocol. The outputs of the ABB controller are connected to the inputs of the ER7 robot controller, which according to outputs starts the corresponding ACL scripts.



Semester A, 2006-2007


Maayan Dayan and Hen Drishpoon

Environment: Hardware: Robots ER7; Software: vision - ViewFlex, robots motion - ACL.

The project’s goal is to present the abilities of a robot’s communication skills using the “Viewflex” as a vision application.

The chosen assignment is to present a game that resembles the game “Black-Jack”. The two robots play against one another by throwing a dice a few times, summing the results and compare between them. A sum which is greater that 11, automatically disqualifies the player; the robot with the higher result (if it is lower than or equal to 11) wins.

The project includes two ACL programs for both robots, which run in parallel. One of the robots is the “main” robot. This robot will get from the other robot its dice throwing result and will compare it to his own result. The “main” robot will also announce the result of the game by “clapping hands” according to the result accepted.

The execution of the project includes:

  • Teaching the robots the possible dice models with “Pattern Matching” technique;
  • Preparing ACL scripts that will identify these models
  • Implementation of the robots communication system


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Last Updated on Thursday, 21 July 2011 10:22



Tel-Aviv University                                                 CIM Laboratory

Semester A, 2006-2007



Ron Reinich and Ron Shkedi

Environment: Hardware: Robot ER7, Controller ABB 571; Software: CodeSys – ladder diagram programming, Intellitec - ACL programming.

The purpose of this project was to control an ER7 robot remotely using an ABB-AC571 controller. The implementation came through a game of Tic-Tac-Toe. The game is played over the internet and the moves are being sent through it to the ABB controller, which sends its outputs to the robot's controller. The program which instructs the robots is downloaded to the robot's controller and the robot receives commands to which space it should move and what shape to draw.

During the project we learned about controllers, how to connect them, and how to deal with the too few input and output entries that the ABB controller has.

Since the board contains nine different spaces and the ABB controller only has eight output exits, we had to figure a way to save on outputs. We divided the board into three columns and three rows, and in order to get to a certain space, the use of two outputs was necessary (one column output and one row output). Another output was used to define the shape  that needed to be drawn (X or circle).

The product of the project is a game played by two players on different computers. After each turn, the robot receives inputs from the players through the controllers and then moves to the right spot and draws the right shape. After one of the players has won, a light flashes for a few seconds.


robot er7



Last Updated on Thursday, 21 July 2011 10:24

Automated Parking Vehicle

Yuval Rotshtein and Tal Bloch

Environment: Robo-Lego


the robolego-car will drive straight until it identifies a free parking space. It will only recognize spaces that fit the car size, and it will drive a bit ahead and make a smooth reverse parking. If the robot touches another car that park behind him, it will stop and drive straight a bit and stop.



Imri Rozenboim, Roi Raz and Hadas Shranghaim

Environment: RoboLego, RCX Controller, NQC – programming.

The project's goal is to guid the “RoboLego”, a “LEGO” robot, throughout any given maze that maintains the rules of straight lanes and only 90 degrees turns.

The challenge was to guide the Robot using a simple navigation algorithm of “keeping the right” while it gets its orientation by interpreting the signals of three light sensors.

The maze lanes are black strips placed on a white board. Two light sensors placed on the front of the robot maintain the robot's course and prevent it from diverting from the maze black lanes. A third sensor, placed on its right side, detects right turns and guides the robot to turn accordingly.

The robot stops when it sees “the light”, a strong LED attached to the maze exit.




Chen Estrugo, Michal Shor and Tal Toibin

Environment: RCX RoboLego, programming - NQC.

The goal of the project is to model the basic concepts of quantum mechanics and to simulate the quantum robot versus the classical one using LEGO® MINDSTORMS™ Robots System and NQC programming language.

In each case (the quantum and classical), the robot simulates an object, for which two of its characteristics, say color and weight, are being tested. In order to simulate the color detection the touch sensor is used, and for the simulation of the weight detection the light sensor is used. Additional light sensor is used for the robot’s trajectory control.

Quantum Lego Robot [1]

During its course, the robot encounters the detectors, which represent color and weight detectors, and turn right or left according to the recognised characteristic.

The direction of the robot’s movement is calculated as follows. In the classical scenario, during the robot’s movement, for each newly met detector a number 0 or 1 is generated randomly, and is stored as a corresponding inner characteristic of the robot. Thus, if the robot meets, for example, the color detector and according to the previous color detector its inner state is 0 (or 1), then the state still 0 (or 1). As opposed to the case where the inner state, which corresponds to the color, is undefined, then it is chosen randomly as 0 or 1. In the quantum scenario, the vector of the robot’s state is calculated for each detector independently using the corresponding detector’s matrix. The probability of turning to the right or to the left is calculated as a square of the obtained amplitude.

Quantum Lego Robot [2]


Iris Binhas and Roy Frenkiel

Environment: RoboLego, NXT Controller, Lego Mindstroms NXT- Programming.

The goal of the project is to compare between RCX controller and NXT controller, first by a comparison of attributes, and second by a demonstration of how the robot operates as radar.

The comparison part of the project was done by internet research that showed the vast superiority of the NXT controller over the RCX controller.

The demonstration of the robot’s operation as a radar was done by implementing a program called JennToo. This program utilizes an Ultrasonic sensor with the capability of measuring a distance from the sensor up to 240 cm.

In the first part of the program, the robot makes a 360 degree turn during which the robot constantly takes measurements of current distance and angle- using a built in rotation sensor. The second part of the program transforms the readings into coordinates and plots them on the screen.

The program also enables the change of the radar’s resolution using the controller’s arrow keys.


 2007_B_JennToo_Radar_NXT_Lego[2]                   2007_B_JennToo_Radar_NXT_Lego[3]


Elazar Heim and Nadav Weiss

Environment: RoboLego, RCX Controller, NQC – programming.

The goal of the project is: Using LEGO components, light and temperature sensors and RCX 1.0 controller to build a robot capable of scanning a warehouse for hot objects and disposing of any such found.

The robot is programmed to move on a black track on the warehouse floor, thus enabling him to perform his task in any given warehouse. When arriving at a junction, the robot turns left and enters a cell. In every cell, the robot will perform heat (Radioactive radiation) measurement. If the robot found a hot object, it will seize it with its gripper and will evacuate it to the disposal area.

The Robot checks all cells in the warehouse clockwise until it either finds a hot object or it finishes examining all the cells in the warehouse. If no hot object was found, the robot returns to its base point.

The robot has two motors for manoeuvring, and one motor for gripping the objects. The robot uses two light sensors to navigate and heat sensor for detection of hot objects.

The task scenario is the following. The robot starts its task by measuring the temperature at the base point. From there it drives to the first junction, and starts examining the cells on the left side of the warehouse. The robot turns left, enter the cell and measures the temperature inside the cell. If a hot object is found the robot seizes it with the gripper and evacuates it to the base point. If there is no hot object in the cell, the robot reverses to the junction, turn right and continues to the next cell.

If no hot object is found on the left side, the robot examines the cells on the right side. If no hot object was found in the entire warehouse, the robot returns to the base point and waits.



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