August 2, 2006

Department of Electrical & Computer

Engineering’s

Robots can be very useful for completing as-signments which humans may find difficult or unpleasant. Small autonomous robots can be used to reach places not accessible to humans or larger robots, such as tiny holes in walls or through the rubble of a collapsed building. A small robot becomes much more useful when equipped with sensors which allow it to interact with its environment. A sensor-equipped robot can be used to locate gas leaks, create maps, or locate survivors. Small robots are not without limitations. They cannot carry the amount of hardware or travel over the same difficult terrain which their larger counterparts can.

Our goal was to build a small autonomous robot that can perceive its environment, react to un-foreseen circumstances and re-plan dynamically in order to achieve its mission. We addressed

the need for small, autonomous useful robots by designing a robot which navigates around obsta-cles while trying to reach an infrared transmitter.

Our solution, ‘Collision Avoidance Robotic Infra-red Tracker’ (C.A.R.I.T), is able to navigate around obstacles while trying to reach an infrared trans-mitter. It interacts with its environment through the use of sensors for input and motors for out-put. In order to reach the intended destination (infrared transmitter), a set of infrared detectors and an integrated digital compass is used to direct the robot to the transmitter. To avoid col-lisions with obstacles, a set of ultrasonic sensors bounces sound waves off stationary objects. An analog infrared photodiode produces a voltage that determines the distance to the destination. The microcontroller uses these signals to deter-

mine the appropriate motor control signals. The motors are controlled by a dual H-bridge circuit to determine motor direction (forward, reverse and speed). Finally, all decision-making by the mi-crocontroller is controlled by the programming algorithm.

Collision Avoidance Robotic Infrared Tracker (C.A.R.I.T.)Team C.A.R.I.T.:Jason Christensen, Mahmoud Azab, Todd Rosemurgy, Sumit Khatri

After looking at the current market, we realized that there are a lot of different sensors for Carbon Monoxide, Temperature, and Humidity but very few that integrate all of them, and even less that actually have a sensors network. Our smart home system is designed to integrate multiple sensors that can be placed at different nodes and communicate wirelessly. Such a smart home system can be used extensively throughout a home or a business, and can be custom designed to fit the customer’s need.

To demonstrate our sensors net-work, we have designed a base sta-tion, and two nodes. The nodes have a CO sensor (a temperature sensor is used for practical and demonstration purposes), and a humidity sensor. They also have an exhaust fan which increases speed with increasing levels of carbon monoxide. Also, 10 LEDs are placed on each node that light up one at a time with increasing levels of CO. The humidity and CO sensor send out a signal each to the PIC in the node that converts the frequency

and analog signals to digital values and sends them to the base station through the RF module.

The base station consists of an RF module, an LCD screen, a sound alarm, a phone dialer, and two buttons. One button controls the display on the LCD screen – decid-ing which node to display. The other button resets the entire sys-tem. Anytime that the base station receives a CO level of over 50%, the sound alarm is triggered. Once the alarm is triggered, the user has 30 seconds to turn off the alarm. If the alarm is not turned off within that time, then the phone dialer is activated which begins to place calls on pre-stored numbers and starts playing a pre-recorded mes-

sage. Once the system goes into phone dialer mode, the phone dialer alarm is triggered and the system freezes for 3 minutes to allow the phone dialer to place its calls.

The base station receives the values from the nodes approximately once every second, giving close to instantaneous readings for the users.

Team CODS: Vaibhav Sarihan, Syed Mujtaba Ali, Kamran Khan, Brian Butler

Wireless Home Monitoring

For anyone who has ever been to a rock concert, it becomes quite obvious that musicians carry slightly more equipment then just a guitar or drum set. This is especially true in the case of a guitarist. Guitarists carry a multitude of effects pedals and other devic-es to distort the sound of their guitars to that precise timbre that they are attempting to create for each and every song. They also must carry Amps and equipment simply so the sound may be heard.

The problems with this lie in the fact that they are carrying more equipment then is necessary and also spending much more money then may be necessary as well as each effect typically requires it’s own pedal and therefore it’s own purchase. The Guitar Group has designed a product that may be able to remedy that problem for the traveling musician. We have designed a DSP kit that is programmed to process audio signals from a guitar and add various effects (that can be chosen on the fly) and finally outputting the signal through the FM band so that the musician can simply use any local FM radio to output their riffs.

To achieve this we had to make use of a fast DSP in order to process the input signal without any notice-able delay between playing a string and hearing the modified output, we chose a TI TMS320C6713 DSK.

The design works as follows, first the signal is car-ried from the electric guitar through a typical ¼” audio cable to the travel kit. The signal then passes

Travel Guitar KitThe Guitar Group: Brandon Loudenburg, Chris Leclair, Edward King, John Green

through a switch where it is either carried to a Digital Tuner or to the DSP board itself. If the signal is sent to the DSP board it is then converted from the analog to the digital realm using an ADC converter with an AIC23 codec so that the DSP can process the information. The DSP runs algorithms based on the value of the dip switches

located on the board (each effect has been assigned a value and the 4 switches work in a binary fashion

to select the desired effect). The modified output is then sent through a DAC converter and carried to the input of the FM transmitter. The FM transmitter uses a Colpitt’s Oscillator to oscillate at a frequency of about 100 MHz (this can be adjusted so as to use a frequency that is not used by local radio stations). The analog audio is carried in the radio wave and can be heard on any commercial FM radio, thereby eliminating the need for an Amp.

Our team came up with the idea of imple-menting a system wherein household items could be activated using voice commands. There is a market for a system that would enable a consumer to control the activation of different appliances remotely. An impor-tant use of this could be by disabled people. A single system enabling them to control a wide range of appliances in the house with-out actually having to do it manually could be invaluable.

Our project is essentially comprised of two sat-ellites and one computer acting as a server. Each satellite contains a microphone, a FM transmitter, a RF receiver and two AC plug points leading to the devices to be controlled. The server consists of two FM receivers plugged into the audio input of the computer, a Java GUI and voice interpreter, and a RF transmit-ter attached to the computer via a serial port.

The microphones on the satellite pick up spoken com-mands and send them directly to the FM transmitter

The Home Automation Control System (HACS)Team HACS:Keenan I. Nichols, Jeremy Stack, Joe Hasty, Nikhil Pillai

placed inside the satellite. For the purpose of this project, we have set up the satellite FM transmitters to transmit at a frequency of 88.1 MHz and 88.7 MHz. The FM signals are picked up by two radios, each tuned to the necessary frequency. The signals are then sent directly to the computer where they are decoded and interpreted by the voice recognition software. The Java interface “listens” to the voice com-mands, checks the commands for validity and sends the appropriate signal to the RF transmitter telling it which of the 4 devices to switch on/off.

The RF transmitter is able to differentiate between multiple satellites, up to 255, thus al-lowing for over 500 controllable devices. Future advancements could include automatic timers while the home owner is on vacation and remote web-based configuration and activation. This enables a user to be able to turn off/on a light attached to the satellite by simply saying “sys-

tem lights” in the vicinity of any of the two micro-phones. The Java GUI also enables manual control of the devices as well as some gen-eral configuration options.

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As a group, our goal was to design a positioning system for use with robotic systems. Our robot can triangulate its position and navigate to a desired location. The purpose of designing such a robot is to prove that it is possible to have such a vehicle plot a course to a desired location autono-mously while starting anywhere inside a triangle of beacons (trilateration). Our experiment is to prove that an algorithm can be properly embed-ded in a robot, giving it the ability to navigate a

flat surface accordingly.Using an infrared and

ultrasonic signal scheme, we are able to imple-ment the trilateration algorithm. Our research initially implied that we could be able to execute this technique in a 2 dimensional environ-ment approximately 5 meters by 5 meters. This was accomplished by sending both an IR and ultrasonic signal to the robot from the beacons, each in rapid succession.

Positioning System for Robotic SystemsTeam Robo-Nav: Jeff Svatek, Timothy Graham, Nicholaus Kee, Alison Shanley

The IR signal is used as a timestamp and the difference in delay of the ultrasonic signal provides the distance informa-tion. The speed of light is constant, and the speed of sound can be assumed to be constant, though humidity and other factors cause slight variation. For our small distances, however, these varia-tions are negligible. The data will be processed initially with a PIC. This digital data is run through a digital analog con-verter using opto-isolators and a resistor

network. This analog information is then processed by a microcontroller located within the LEGO RCX brick, which translates the raw analog value into an integral distance. Using three distances (one from

each beacon), the microcontroller derives the robot’s coordinates rela-tive to a predeter-mined set of axes. This location infor-mation can be ap-plied to a myriad of algorithms to invoke robot movement in the proper direction.

The Principle of Trilateration Standing at B, you want to know your location relative to the reference points P1, P2, and P3. Measuring r1 narrows your position down to a circle. Next, measuring r2 narrows it down to two points, A and B. A third measurement, r3, gives your coordinates at B. A fourth measurement could also be made to reduce error.

SensingBeacons will transmit IR and ultrasonic signals. The robot receives these signals and sends the data to the microcontroller.

ComputationUsing the time stamps from the ultrasound, the microcontroller computes location using

the standard formula for trilateration.

ActuationThe robot interprets its relative location and

moves in the direction it is programmed to go.

Outside WorldThe robot can maneuver a flat 2D

surface up to 5 m without exceeding the range of the transmitting beacons.

As technology progresses, and computing power

still roughly following Moore’s law, the need for, and potential of artificial intelligence increases. In particular, robotics stand to gain immensely from these processing power increases as more complex algorithms can be run faster.

SART tried to develop on this idea of artificial intel-ligence with an autonomous robot. Having no previ-ous knowledge of its surroundings, the robot can navigate around obstacles to a beacon. SART decid-ed that the quickest, easiest, and most efficient way was to use a car-like vehicle. The Vex Robotics Starter Kit accomplished this several basic parts including a chassis, and two motors – one controlling each side of the “car”.

In order to be autonomous, the robot needed sensory input information to understand its sur-roundings. An obstacle detector and a directional sensor system were necessary. An ultrasonic sen-sor, the MaxSonar EZ1, was chosen as the obsta-

Smart Autonomous Robot TeamTeam SART:Morgan Hinchcliffe, Dan Maicach, Boone Staples, Steve Torku

cle detector due to its low power intake and high range sensing. The target beacon was designated as a 1 kHz pulse. SART decided to use a system of three microphones to follow this target. If the ultrasonic sensor fails, a bumper sensor was mount-ed on the front to help the robot get out of tough situations.

All the sensory information was processed using an FPGA made by National Instruments. The NI 7831R RIO board includes onboard DAC as well as multiple analog and digital inputs and outputs. The instincts of the robot’s “brain” were programmed using LabView which directly interfaced with the FPGA. It was programmed in such a way that the audio system would detect

the beacon, the robot would head in that direction, but be able to avoid obstacles and re-detect the target beacon to continue in that direction.

Small robots can per-form various duties that would be impossible for human beings or even hazardous to them. For example, small robots can be used to check for toxic gases in coal mines, check for volcanic activity, and check for coal mines etc. by having the respective sensors attached to them. Our goal in this project was to build such a small robot that was inexpensive, to help demonstrate how these machines could be used to perform the above mentioned tasks.

Our project is divided mainly into two parts. In the first part the robot will track an Infrared (IR) source placed ten feet away. In Part 2 the robot will try and track a Radiofrequency (RF) source placed the same distance away from it. For the IR part of the demonstration, we have an IR source posi-tioned 10 feet away from the starting point which emits IR light at 38 kHz. The robot has two IR sensors located on it which enable it to detect the transmitter. When the robot does not detect any IR light it is programmed to move in a large circle until either one of the detectors detect the IR. When one of the sensors detects the IR, the robot will posi-tion itself in that direction and move forward until it reaches the source. Once it reaches the source, we will demonstrate Part two of the project, where the robot will try and locate the RF source. The RF transmitter provides a strong and reliable signal at a frequency of

Project: IRFTeam Tech Gurus: Daniel Chunkapura, Gaurav Gupta, Anshul Gupta, Karan Garg

418 MHz. On the robot, there is a RF receiver which receives this signal. The receiver pro-vides signal strength in terms of analog voltage which is fed in the PIC. The robot moves to-wards the RF transmitter using this increasing sig-nal strength indication. Under ideal conditions this signal strength is directly proportional to

the distance but the RF receiver does pick up noise which may interfere with its functioning.

Similarly, a small robot could be attached with a seismographic sensor which would help it detect volcanic activity or a carbon monoxide sensor to de-tect toxic gases or one could even build a robot that sacrifices itself checking for land mines in a field.

As society evolves, new technologies are necessary to assist in even the smallest tasks. Automation is improving the way things are done, and shedding light on new possibilities. Our project, an autonomous robot car, is capable of doing just that. Spe-cifically, the robot is a mobile target tracker with the ability to avoid obstacles.

The robot car consists of several systems, as outlined in the block diagram below: its brain, the target and target locator, obstacle detection and avoidance, and motor cir-cuitry.

The brain of the robot, the most important component, is a NI PCI-7831R FPGA, donated by National Instruments. This powerful processor is capable of intricately detailed programming, and handles all of the robot’s decision making and calculations.

The target is a beacon that emits a 1 kHz audio signal, which is received by electret condenser microphones on the robot and run through an audio filter. The fil-tered signal is relayed to the FPGA, which rectifies it and trans-lates it into a dis-tance. The FPGA then delivers logic to the motors to turn the car in the direction of

Cognitive Autonomous RobotTeam Tiny Robot:Jonathan Miller, Andrew Hunter, and Chris Holcomb

the beacon. On its journey to the target, the robot will en-

counter obstacles. To avoid them, it utilizes two infrared sensors that shoot an analog signal out and measure the return signal as a voltage func-tion. Based on the data interpreted from each sensor, the FPGA will send corresponding signals to an H-bridge, which will relay commands to the motors.

While the plan looked good on paper, the implementation was not so smooth. We ran into a few problems, mainly dealing with the powering of the FPGA. After we resolved that with the intro-

duction of a motherboard, we discovered that the robot was pulling a lot of current. However, the rest of the project was integrated successfully.

In theory, our robot can be effective in a wide range of applications. It could be used to deliver objects to people that call out to it. Also, if the mo-tors are silenced and a camera is placed on the car, it could spy on its surroundings or act as a portable se-

curity camera. It could even participate in mili-tary warfare as a mobile bomb with the addition of an explosive. With some modifications, our autonomous robot has the potential for numer-ous innovations.

Autonomous Functioning RobotTeam WSAE:David Anderson, John Brinkley, Randy Doolittle, Noah Haewoong Yang

Our goal was to build a robot with autonomous func-tionality governed by control system theory, which will navigate a course of obstacles, find an

acoustic target and then stop within a fixed range of that target. Based on our desire to create a new paradigm for autonomous robot design, we are challenged to con-struct a more biological and simplistic robot, which is as power efficient, cheap and scalable as possible.

The solution we proposed is a simple self-governing machine using State Machine (Event Driven) Architec-ture a few basic directives to emulate certain search behaviors. We did our best to make a simple autono-mous machine that is scalable, modular, cost and power efficient. Our solution makes use of a FPGA to imple-ment a control structure and ultimately use ultrasonic and acoustic sensors to avoid obstacles, transverse a course and locate a target. This involves sensing, actua-tion, power distribution, brain subsystems, and a chassis.

Two MaxSonar-EZ1 ultrasonic sensors are being used to detect obstacles from 0 to 3 meters and alternate broadcasting every 50 milliseconds. If an obstacle is detected within 1 meter, the robot will veer left or right depending on the obstacle’s orientation to ultrasonic sensors. This control is done by a comparison between the voltage levels of the ultrasonic sensors, depend-ing upon which signal is lower (nearer objects return a lower signal) the algorithm will execute commands to turn in a direction to avoid the obstacle.

Target tracking is done with the use of two acoustic sensors (Kobitone electret unidirectional condenser microphones). These sensors are utilized by comparing the output voltage of each microphone and executing control in order to attempt to equalize the voltage level between the two microphones. This signal is measured from an amplified ac-tive band-pass filter, and then compared between the two microphones. The robot will adjust its course to achieve equal amplitude between the two sen-sors (thus facing the target). Once the robot gets to the target, the program has a threshold voltage to gauge the

distance from the target, the entire robot system will be terminated.

Two DC motor drives from Vex Robotics are used to control the movements of the robot for forward, back-ward and stopping capabil-ities. The FPGA controls the

two motors simultaneously by creating and varying a Pulse Width Modulation control signal, which drives the motors. The movement of the machine interacts with the environment through a tank-tread system, also pur-chased from Vex Robotics. The FPGA is programmed to react to the incoming/outgoing sensor data and utilizes the State Machine logic in order to send and receive necessary control signals.

Power is provided by two separate systems. The FPGA is powered by a bank of six Alkaline AA batteries pro-viding 3.3 and 5.0 V to power the PCI bus. The rest of robot relies off of +/- 8V and 2500mAh supplied by two separate six AA battery packs, that voltage is then used directly by the motors and amplifier circuits, regulated to 3.3V for the microphones and 5.0V for the ultrasonic sensors and the bumper sensor.

Our chassis is based on components obtained through the Vex Robotics Starter Kit.

This kit contains “erector set” style metal and gear com-ponents as well as motors. As the design of the chassis was not the focus of this project the use of the Vex kit was encouraged so that the group could focus on the sensor and actuation design and logic. However due to the amount of surface space necessitated by our project, it was required to fabricate many more risers and plat-forms in order to hold batteries, the PCI bus, as well as a secondary power system.

We used a National Instruments (NI) RIO FPGA (NI PCI-7831R) for our robot brain. This product contains onboard

ADC’s and DAC’s, which allows for easy input/output programming through Labview 8.0 FPGA and Real-Time mod-ules. Since all logic is operating in the hardware at loop rates of up to 40 MHz (25nS), it is very responsive. NI has do-nated this technology as a platform for us to use, we will utilize these on-board capabilities of the RIO unit.

BrainFPGA

I/O

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ADC

DAC

DAC

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Motor

Motor

Microphone

Microphone

Ultrasonic

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ADC

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Sonar

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Detailed Ultrasonic Sensor

Timer

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Algorithm

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Block Diagram 6/15/2006

TWSAE

Demonstration Day Winners

First Place-C.A.R.I.T.Jason Christensen, Mahmoud Azab, Todd Rosemurgy, Sumit Khatri

Second Place-Robo-NavJeff Svatek, Timothy Graham, Nicholaus Kee,

Alison Shanley

Third Place-Team CODSVaibhav Sarihan, Syed Mujtaba Ali, Kamran Khan,

Brian Butler