Oregon Robotics Tournament and Outreach Program III NXT

Oregon Robotics Tournament and Outreach Program III. NXT Advanced Robotics Techniques 2013 Opening doors to the worlds of science and technology for Oregon’s youth

Instructor Contacts Ken Cone ken_cone@ous. edu (503) 725 -2918 Terry Hamm terry. hamm@gmail. com (503) 720 -5157 Dale Jordan Dale_A_Jordan@msn. com Jim Ryan james. r. ryan@intel. com 971 -215 -6087 Roger Swanson swanson@hevanet. com 503 -297 -1824

ORTOP Project Administrator Cathy Swider Cathy_Swider@ous. edu (503) 725 -2920

Goals n n Audience: Rookie and experienced coaches and mentors Build confidence using more advanced navigation techniques Gain experience with 3 motor robot and missions You should have already attended NXT workshops I & II and/or coached a team in a tournament

Agenda n Robot Design n n Navigation n Going straight Turning accurately Sensors n n n Building strong structures Gears Motors Light sensor and calibration Distance sensor Mission Planning & The Buoy Mission

Engineering Design Process Define the Problem Research the Problem Redesign Develop Possible Solutions Communicate Test and Evaluate Choose the Best Solution Create a Prototype

Building Strong Structures n n Parts are pinned together using pins & beams Here are three cross bracing examples n Note: 3 -4 -5 Pythagorean theorem Ref: Minnesota High Tech Kids n http: //www. hightechkids. org/forteams/coaches-library 2 inch drop test

Gears n Spur Gear (show example) n n Instructor holds beam Class person turns axles and feels torque on other axle Tactile feedback = learning about gears and torque Gear Ratio and Speed n n n Turn the large gear one complete turn How may revolutions does the small gear turn? Is gear ratio the same as the ratio of gear teeth?

Gears n Gears & Pulleys n Worm Gears (show example) n n Crown & Bevel Gears n n Turn 90 degrees Spur Gears n n One way transfer of power Change direction & change torque Note gears used on workshop robot fork lift

Robot Design n Three motor base n n n Level, square Screen location Attachments n n Soda can pusher Fork

Navigation n n Often times the robot does not go where you want it to go on a consistent basis. Why might that be? In this segment we’ll explore basic robot move and turn accuracy

Navigation n Let’s do a simple “Going Straight” lab test to understand how the robot moves: n n n n Tape down 2 pieces of paper on the table, 2 feet apart Draw a starting line on one paper Create a simple program to move 2 feet then stop Run speeds at 20, 50, 90 Use a “soda can pusher” or similar attachment on the front of the robot Run test 5 -10 times Mark a dot where front “can pusher” arm stops Begin test now

Navigation n Going Straight test: n n n Run test 5 -10 times, record results Draw box around landing points What does the box tell us? Goal: To understand how the robot moves – its accuracies and inaccuracies We can then compensate for inaccuracies with other techniques n Examples: use a wide fork for the buoy mission, or a wide soda can pusher

Navigation n Theory - The S curve inaccuracy: n n n A Move block rotates both wheels to the programmed position using built-in rotation sensors as feedback If one wheel slows down, the other wheel slows down, causing the robot to move in a lazy S curve Did you observe this in your going straight lab tests?

Navigation n n Compensation for navigational errors – “reset to zero” Use mechanical means n n Angled corners (back into a corner) Wall follower (wheel) Back against a wall Use sensors n Light & touch to “reset to zero” and re-establish the exact robot location

Navigation n Variables that affect going straight: n n n n Starting box position Speed Battery charge Tire size Motor friction, gear backlash (Google these terms) NXT software tries to keep both wheels moving at same rate What is distinct about the last two variables?

Navigation n Turning Accuracy n n Let’s review two types of turns Spin Turn n n Position Move block slider all the way to the LEFT or RIGHT A spin turn uses 2 wheels, one forward, one backward - robot “spins” around center point between the wheels

Navigation n One Wheel Turn n n Use Move block, turn one motor off One wheel moves, one is stationary – robot turns around the stationary wheel Which turn is more accurate? Where could we use the spin turn?

Navigation n Extra Credit n n n Program a move-turn sequence Use “spin” turns and “one-wheel” turns Place a pencil mark where robot stops Run program 5 -10 times and collect data Variables n n n Left-right error Turn angle error Any more variables?

Navigation n Review: n n n Use navigation and “reset to zero” to know where robot is at all times Understand variables and how they influence robot movement. We learned left-right moves are less accurate than front-back moves. Long dead reckoning moves are ok, if robot/attachments can compensate for move inaccuracies, e. g. n n n Width of can pusher, width of buoy pickup fork Backing into wall or corner to re-align robot Go for simple & sufficient solution

Sensors n n Now that we know how to move and turn with some precision, lets take a look at sensors Sensors we can use in FLL: n n Touch, light, rotation, distance Teams sometimes give up on sensors because they seem complex and don’t seem to work in a predictable manner Most teams feel comfortable with the built-in rotation sensors in the motors to determine the number of rotations/degrees In this segment we’ll explore the light sensor in more detail to help us navigate on the playing field

Light Sensor Variables n Let’s look at Light Sensor-to-mat distance & record some data: n n n Mount Light Sensor on the robot about 1/8” from the mat Record Black, Green, & White values using the NXT View function Move the Light Sensor to 3/8” from the mat, record values How does the distance affect Light Sensor values? As the robot moves, it bounces. Which distance do you think would work best, and why? Black 1/8” from mat 3/8” from mat Green White

Light Sensor Variables n Now let’s record some more data: n n n Use your hands to cover the Light Sensor to simulate a dark room Record Black, Green, & White values What does this tell us about Light Sensor performance in various room lighting conditions? Black Light Sensor uncovered Light Sensor covered Green White

Uncalibrated Light Sensor Values n Data from 7 light sensors - uncalibrated, room light on 80 Uncal room light on What trigger point would we use to stop on black? On green or 70 black? 60 Uncal value n 50 Black 40 Green 30 White 20 10 0 0 1 2 3 4 Sensor number 5 6 7 8

Calibrated Light Sensor Values 120 n n Calibration: A process to compensate for varying lighting conditions Data from two light sensors – uncalibrated (left), calibrated (right) 100 2 4 Light Sensor 1 Light Sensor 2 n 80 3 60 1 2 40 3 1 20 0 2 With calibrated values: BLACK n Values are GREEN spread apart WHITE n Lightest 4 values are “close to” 100 n Darkest 4 values are “close to” 0

Calibrating Light Sensors n Using the internal calibration program n n n Connect NXT brick to your computer On the upper left corner, click “Tools” and “Calibrate Sensors” To Calibrate, do the following: n n Select the light sensor and the port the sensor is connected to. Then click the Calibrate button. This will download a small program to the NXT and run it automatically. On the NXT’s screen you will see text that reads “Min Value: ”. Point the light sensor towards a material or spot that represents what the light sensor should measure as dark. Press the orange Enter button on the NXT. Next you will see text that reads “Max Value: ”. Point the light sensor towards a material or spot that represents the brightest location the sensor will encounter during the program. Press the orange Enter button again. Calibration is complete. Caution: After Calibration, read light sensor values from the Light Sensor program block to determine trigger points. Do not use NXT View function, as View gives incorrect values for calibrated light sensors.

Light Sensor Trigger Point n Typically set the Trigger Point half way between the two light intensities you want to recognize n n White = 63 Black = 34 Trigger point (programmed) = (( white – black) / 2) + black Trigger point example: ((63 -34) / 2) + 34 = 49

Summary of Light Sensor Variables n Light sensor readings vary depending on: n n Distance of sensor from the mat Ambient light in the room The particular light sensor you are using Whether the robot’s light sensors have been calibrated or not

Value of Calibrating Light Sensors n n Spreads lightest and darkest values far apart If distinguishing between only two light intensities, will help adjust for differing ambient lighting Cannot use NXT View function to read light values to determine trigger points Can you write a program that will read the light sensor values from the robot?

Sensors -- Distance n Homework assignment: n n n Add a “Wait for Distance” block Program robot to stop 4 inches from a wall Shape and texture of target object can impact performance This completes the four sensors: Rotation, Touch, Light, and Distance Questions on Sensors?

The Buoy Mission n One last programming exercise to bring several things together: n n n Use a Mission Planning Sheet to plan moves, turns, and attachments for your attack on the mission Use three motors and multiple sensors Program your robot and win the points s

The Buoy Mission Engineering Problem: n n n n Move the buoy from its current position, Place it in the box at the end of the mission board, Park the robot on the blue finish line The box has a fence in front, so to score points you must lift the buoy over the fence and into the box. (Extra credit points for detecting the fence using the distance sensor. ) First, write a mission plan (moves, turns & attachments), then write a program to execute your plan. Experiment with a calibrated light sensor

Buoy Mission End here s Start here Place buoy here

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