Critterbot

1. Critterbot

2. Physics 30 Outcomes This lesson will address the following outcomes from: Physics 20 Program-Of-Study: 20-A1.5k: explain, quantitatively, two dimensional motion in a horizontal or vertical plane, using vector components. Physics 30 Program-Of-Study: 30-A1.2k: explain, quantitatively, the concepts of impulse and change in momentum, using Newton’s laws of motion. 30-B3.1k: describe magnetic interactions in terms of forces and fields. 30-C1.2k: compare and contrast the constituents of the electromagnetic spectrum on the basis of frequency and wavelength. 30-C1.6k: describe, quantitatively, the phenomena of reflection and refraction, including total internal reflection. 30-C1.9k: describe, qualitatively, diffraction, interference and polarization. 30-C1.2sts: explain that scientific knowledge may lead to the development of new technologies, and new technologies may lead to or facilitate scientific discovery (ST4) [ICT F2-4.4].

3. Introduction: Critterbot Do you have a pet? Have you ever tried to train your pet to sit? To fetch? To lie down? Each time it learns a new trick or does something good you would probably give them a treat to indicate they did a ‘good job’!

4. Critterbot What if your pet wasn’t a typical toilet bowl drinking, peeing/pooping all over your house pet… …instead it is a robotic ‘critterbot’!

5. Critterbot Just like a pet, you’ll need to train your critterbot… BUT unlike a robot, critterbot will learn new tricks on its own.

6. Critterbot Critterbot is a learning robot that uses a subjective representation of knowledge gained through experience in sensorimotor robotics (using electronic sensors) to connect low-level experiences and high-level knowledge to create a life-long learner.

7. Critterbot Imagine a baby crawling up to a wall, the baby has a couple of options: (a) turn to the right or left (b) stay at the wall (c) back away from the wall Eventually the baby will learn to do something after they get to the wall, but how do they learn what to do?

8. Critterbot Critterbot attempts to learn these seemingly natural actions through reinforcement learning. When critterbot is in front of the wall it may learn to back away from the wall in which case the researcher will give it positive reinforcements (through codes), or a ‘yummy treat’ for the good work. Critterbot will eventually learn that when it backs away from the wall it is doing something good and continue to back away from walls each rolls up to a wall.

9. Critterbot – Reinforcement Learning For more information regarding reinforcement learning, please refer to the AICML lesson ‘Reinforcement Learning’ (created for Science 8). Link to that PowerPoint file on your computer or follow the link to the file on the CMASTE.ca website, below. Reinforcement Learning by a Critterbot

10. Critterbot – Machine Learning 1232746809.240 0 0 0 0 0 0 0 0 0 -32 -32 976 16 2 2 2 2 2 18 2 63 2 10 308 172 120 120 1232746809.250 0 0 0 0 0 0 0 0 0 -32 -32 992 4 2 3 3 3 5 17 4 64 3 19 308 168 124 120 1232746809.260 0 0 0 0 0 0 0 0 0 -32 -32 992 8 3 2 4 5 3 17 2 65 2 19 308 172 124 120 1232746809.270 0 0 0 0 0 0 0 0 0 -32 -32 992 4 2 2 2 2 2 17 2 66 5 21 312 172 120 120 1232746809.280 0 0 0 0 0 0 0 0 0 -32 -32 992 12 3 2 2 2 2 22 3 65 2 17 304 172 124 120 1232746809.290 0 0 0 0 0 0 0 0 0 -32 -32 976 0 2 2 2 2 2 21 3 66 3 18 312 168 120 124 1232746809.300 0 0 0 0 0 0 0 0 0 -32 -32 976 12 3 2 2 2 2 21 2 49 2 18 308 168 124 120 1232746809.310 0 0 0 0 0 0 0 0 0 -32 -32 992 4 3 3 2 2 2 21 2 49 4 19 308 172 124 120 1232746809.320 0 0 0 0 0 0 0 0 0 -32 -32 992 12 2 2 3 3 5 24 2 48 2 18 308 172 120 120 1232746809.330 0 0 0 0 0 0 0 0 0 -32 -32 992 8 2 2 2 2 2 23 2 49 2 17 308 172 120 120 1232746809.340 0 0 0 0 0 0 0 0 0 -32 -32 992 12 3 3 3 6 4 25 2 58 3 19 312 168 120 124 1232746809.350 0 0 0 0 0 0 0 0 0 -32 -32 992 4 2 2 2 2 2 25 4 58 3 16 304 172 124 The Critterbot Project is an initiative of the Reinforcement Learning and Artificial Intelligence (RLAI) lab at the University of Alberta. In that lab they build and teach critterbot to be an ‘ideal’ pet! This learning aspect of critterbot is attributed to Machine Learning, where computer codes are programmed to review the data collected and learn what to do with the data all on its own. These are truly ‘thinking robots’!!! BUT, how does critterbot sense walls and treats? Critterbot has a variety of sensors on it to detect a variety of objects and rays like ‘treats’

11. Critterbot • Next we will discuss the variety of sensors in detail: • Light Sensor (visible light) • Infrared Sensor (detect IR near visible spectrum) • Infrared Sensor (distance detector) • Infrared Sensor (heat detector) • Gyroscope Sensor (balance detector) • Accelerometer (acceleration detector) • Sound Sensor (detects sound waves) • Bump Sensor (detect how something feels) • Magnetic Field Sensor • Battery Level Sensor • Motor Velocity Sensor • Motor Current Sensor • Motor Temperature Sensor

12. Critterbot – Light Sensor Light Sensor Is used to detect the visible range of the Electromagnetic Spectrum (400nm – 700nm). Not only does it detect the color of the light, it can also detect the brightness of the light source. Higher values on the sensor indicate brighter light sources.

13. Critterbot – Infrared Sensor Infrared Sensor Is used to detect the infrared range of the Electromagnetic Spectrum (750nm – 100 μm), however this sensor is used to detect the IR range close to the visible range (close to 750nm). It’s primary uses are for finding beacons, make specific landmarks like LED (light-emitting diode) light to direct critterbot. Researchers can use these lights for the charging station, unique symbol for “here is a place to charge”.

14. Critterbot – Infrared Sensor Infrared Sensor Uses a pulse of Electromagnetic Radiation to detect the distance critterbot is to a specific object. There are 2 types of these infrared sensors: LIDAR – uses radar to time the distance SHARP IR Sensor – uses trigulation of pulse to calculate distance

15. Critterbot – Infrared Sensor (LIDAR) LIDAR – This type of sensor sends out a pulse of infrared light and times the reflection of the pulse. Knowing the speed of the infrared pulse, the distance can be calculated. However, this type of sensor is the size of critterbot, hence it is not very useful in helping critterbot detect distances.

16. Critterbot – Infrared Sensor (LIDAR) SHARP IR Sensor – utilizes the ideas behind the diffraction of light and the resulting triangulations to calculate distance. This sensor is much smaller, and used on critterbot to detect distances.

17. Critterbot – Infrared Sensor Infrared Sensor Used to detect the infrared range of the Electromagnetic Spectrum (750nm – 100μm), this sensor is used to detect the IR range further from the visible range (close to 100μm). This range represents the amount of heat given off by an object. These sensors are in the same location as the light sensors on the critterbot.

18. Critterbot – Gyroscope Sensor Gyroscope Sensor Device used for measuring the orientation based on the principles of angular momentum. It allows the critterbot to sense its balance, whether it’s sitting flat on the ground or travelling up an incline. Provides researchers with critterbot’s rate of rotation. This is an inertial sensor, meaning it senses the motion inside the critterbot without a frame of reference on the outside.

19. Critterbot – Accelerometer Accelerometer Used to detect the rate of change in travel for the critterbot. This is also an inertial sensor, meaning no external frame of reference is required. Measuring velocity requires an outside point of reference, so not as useful in some cases.

20. Critterbot – Sound Sensor & Bump Sensor Sound Sensor To allow critterbot to ‘hear’ its surroundings, helps critterbot gather more information about its surroundings so it may paint a better picture of its environment. Eventually critterbot may be able to decipher individuals from each other as well as day from night (the day tends to be noisier than night) Bump Sensor Allows critterbot to rub up against an object and detect the collision force. The value is not instantaneous but decays from a maximum value for several observations after the impact.

21. Critterbot – Magnetic Field Sensor & Battery Level Sensor Magnetic Field Sensor Critterbot uses this sensor to detect the local magnetic field. This sensor can be used as a compass for critterbot to decipher its orientation. Battery Level Sensor Used to detect the amount of battery left in critterbot, a measure if critterbot’s life line.

22. Critterbot –Motor Velocity Sensor Motor Velocity Sensor Detects how fast the motor is spinning. The researcher can calculate the velocity of the motor, and in turn calculate the velocity of critterbot.

23. Critterbot – Motor Current Sensor & Motor Temperature Sensor Motor Current Sensor Can detect the amount of electrical current each motor is using, which is a rough measure of the force the motor is exhorting. This can be used to determine how ‘tired’ critterbot is. Motor Temperature Sensor To determine how hot the motor is. This can also be used to determine the ‘tiredness’ of critterbot.

24. Critterbot – OMNI Wheel OMNI wheel Critterbot utilizes three OMNI wheels, which are wheels with spinners on it maximize its mobility. The wheels are organized in a triangular pattern to allow critterbot to move in all directions.

25. Critterbot – OMNI Wheel At first glance the wheels don’t look like critterbot will move anywhere other than spin around, however this set-up is purposely created to allow for full movement by the critterbot. If we use vector addition to look at the mobility of critterbot we will understand how this is a superior system than the standard four-wheeled cars we drive.

26. Critterbot – OMNI Wheel If we are trying to move critterbot towards the top of the page, we can add up the vector components of Motor 1 and Motor 2, the resultant will be directed towards the top of the page. Motor 0 will move towards the top because it is already in correct orientation.

27. Critterbot Perhaps one day critterbot trained to be useful in our everyday lives and critterbot will learn to bring you your newspaper or slippers…

28. Field Trips for Critterbot For information regarding field trips to the ‘University of Alberta – Computer Science Centre’ to see critterbot and other exciting new research on robotics and Machine Learning please contact: Sheryl Mayko smayko@cs.ualberta.ca

29. Practice Questions • Using LIDAR Sensors, an IR pulse is directed perpendicularly to an object and it takes the pulse 3.87x10-6s to travel from the emitter back to the sensor, how far away is the object? • Using LIDAR Sensors, this time the IR pulse is directed at an angle of 23°from the sensor. It takes the pulse 6.85x10-6s to travel from the emitter back to the sensor, how far away is the object?

30. Practice Questions 3. Using the SHARP-IR Sensor, an IR pulse is detected at the angle of 370 from the sensor. The two sensors are 1.07cm apart. How far from the sensor is the object?

31. Practice Questions • Using the magnetic field sensor, critterbot detects a magnetic field of 2.56T to the right. What pole is to the right of critterbot? • What are some of the purposes of having a magnetic field sensor in critterbot? • Critterbot detects 4.02x10-4Hz light, describe the type of light being shone onto critterbot. • If critterbot’s force sensor detects a force of 267N for 0.35s, what is the magnitude of the momentum of the collision?

32. Practice Questions 8. Critterbot is trying to move at 6.5m/s[up]. Motor 0 is moving critterbot 1.3m/s [up] while Motor 1 is moving critterbot 4.7m/s @ 49°E of N. What velocity should Motor 2 move critterbot? How can critterbot help you with your daily life?

33. Critterbot Resources: http://www.cs.ualberta.ca/~sokolsky/critterbot/ http://rlai.cs.ualberta.ca/RLAI/rlai.html http://www.cs.ualberta.ca/~sutton/book/ebook/node7.html

34. Centre for Mathematics Science and Technology Education (CMASTE) 382 Education South University of Alberta Edmonton AB T6G 2G5 www.CMASTE.ca To download: select Outreach, Alberta Ingenuity Resources and Centre for Machine Learning Filename: AICML6BrainTumourAnalysis Reinforcement Learning and Artificial Intelligence Department of Computing Science University of Alberta 2-21 Athabasca Hall Edmonton AB T6G 2E8 (780) 492-5640 http://rlai.cs.ualberta.ca/RLAI/ualberta.html Centre for Machine Learning Department of Computing Science University of Alberta 2-21 Athabasca Hall Edmonton AB T6G 2E8 (780) 492-4828 www.machinelearningcentre.ca Alberta Ingenuity 2410 Manulife Place, 10180-101 Street Edmonton AB T5J 3S4 (780) 423-5735 www.albertaingenuity.ca