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Critterbot – Lesson 1 (Sensors)

Critterbot – Lesson 1 (Sensors). Science 8 Outcomes. This lesson will address the following outcomes from: Science 8 Program-Of-Study: Unit D: Mechanical Systems (Science and Technology Emphasis) Outcomes for Science, Technology and Society (STS) and Knowledge

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Critterbot – Lesson 1 (Sensors)

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  1. Critterbot – Lesson 1 (Sensors)

  2. Science 8 Outcomes • This lesson will address the following outcomes from: • Science 8 Program-Of-Study: • Unit D: Mechanical Systems (Science and Technology Emphasis) • Outcomes for Science, Technology and Society (STS) and Knowledge • 2. Analyze machines by describing the structures and functions of the overall system, the subsystems and the component parts • Analyze a mechanical device, by: • Describing the overall function of the device • Describing the contribution of individual components or subsystems to the overall function of the device

  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 robotic sensors to gain information about its surroundings. It uses this information to create higher-level knowledge (such as what to do when it comes up to a wall) which allows it to be 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 reinforcement through computer code, like the 'yummy treats' you give your pet for their good behaviour.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’ located either on your computer or at Reinforcement Learning for Critterbot Science 8.

  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 (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 Temperature Sensor

  12. Critterbot – Light Sensor Light Sensor Is used to detect the visible range of the Electromagnetic Spectrum which includes the colors of the rainbow. Not only does it detect the color, it can also detect the brightness of the light source.

  13. Critterbot – Infrared Sensor Infrared Sensor 1 Critterbot has two types of infrared sensors, this one uses a pulse of light to detect the distance critterbot is to a specific object.

  14. Critterbot – Infrared Sensor Infrared Sensor Used to detect the infrared range of the Electromagnetic Spectrum which represents the amount of heat given off by an object. They can detect where any object that gives off heat (people) are located.

  15. Critterbot – Gyroscope Sensor Gyroscope Sensor Device used for measuring the orientation of critterbot. It allows critterbot to sense its balance, whether it’s sitting flat on the ground or travelling up a ramp.

  16. Critterbot – Accelerometer Accelerometer Used to detect how fast critterbot is increasing its speed.

  17. Critterbot – Sound Sensor & Bump Sensor Sound Sensor To allow critterbot to ‘hear’ its surroundings. It helps critterbot gather more information about its surroundings. Eventually critterbot may be able to decipher individuals from each other Bump Sensor Allows critterbot to detect the amount of pressure someone is pushing against critterbot with.

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

  19. Critterbot – Motor Temperature Sensor Motor Temperature Sensor To determine how hot the motor is. This sensor can also be used to determine the ‘tiredness’ of critterbot.

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

  21. 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

  22. Practice Questions • Why does critterbot need so many different types of sensors? • How can critterbot help you with your daily life? • Do you think critterbot is the ‘ideal pet’? Why or why not? • What other functions could we train critterbot to do to make it more useful to society? • What improvements could be made to critterbot to make it more useful?

  23. 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

  24. 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

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