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Nattee Niparnan. Towards Autonomous Robot A robot that can think how to perform the task.

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Presentation on theme: "Nattee Niparnan. Towards Autonomous Robot A robot that can think how to perform the task."— Presentation transcript:

1 Nattee Niparnan


3 Towards Autonomous Robot A robot that can think how to perform the task

4 Autonomous? Able to do things by itself. Robot Control System A system that decide what / when / how to do a particular thing to achieve the given task

5 Hierarchy of Control Reductionism Follow the white rabbit Get dress walk to the pub talk choose a shirt wear a shirt Move a hand to wardrobe

6 Robot = ??? A device that connects sensing to actuation in an intelligent way Intelligent

7 Model-Based approach Sense Plan Act

8 Model-Based approach Understand the world Planning according to the state of the world Result in rules for actions If … then ….

9 Remember the Shakey?

10 Robot Control Issue Model of the world? Robust?

11 Problem of model based It seems reasonable Does it work well in practice? Model can hardly be realized Model based is more appropriated with structured environment Parallel nature? GIGO issue

12 Problem of model based Example, Self Charging Walk to beacon Engage charger approach maneuver Plug-in stop What if we are near the charger?

13 Problem of model based What if we are near the charger? Does our plan cover this case? Coupling between requirement Usually bug prone Model based is sometime computer oriented

14 Computer vs. Robot All computers are equivalent (turing machine) Any two robots are different

15 Truth about Robot Robots have sensors that measure the aspect of external worlds Robots have actuators that can act on the robot and on the world The output of a robots sensors always includes noise and other errors The commands given to a mobile robots actuators are never executed faithfully.

16 Sensing For us (human)… For them (robot)…

17 Actuation Electrical signal Physical quantity Always has some error

18 Intelligence

19 Mobile vs. Immobile Robots

20 MobileImmobile Unknown world Dynamic Environment Localization and mapping problem Highly structured world Static Environment

21 Example Collecting a puck and put it into light

22 Tasks Show gizmo and collection tasks in Bsim What we have as a low level command?

23 Behavior based control What are used in Gizmo

24 Example of Behavior Based

25 Behavior based robotics

26 Reflexive Shortest time from sense act Carefully engineered the reflex to actually perform the task

27 Principle World = what robot sees Plan less Check Act more Be highly adaptable to change Agility?


29 Lower Level Control Given desired output Find input that yield such output

30 System Input U Black Box (grey box) System Output Y

31 Control We hardly understand our system The mathematical model approximately describe the system There always be some error There might be some unknown rule!

32 Example Do we know the speed of motor If we apply some specific voltage? Without actually measuring? i.e., forward computation We have all the theory, right?

33 So what? If we dont really understand the system How do we calculate U for given Y? I want my motor to spin at 200rpm What voltage should I put? Who knows?

34 The Solution Control System Open loop Closed loop

35 Control System Open loop

36 Open Loop Just supply input From the model Example Light bulb Electric fan

37 Open Loop Neglect input Hence, does not adapt itself to the world Very simple Easily failed Work perfectly if we know perfect model of the system Which is not usually the case

38 Control System Open loop

39 Control System Closed loop

40 Feedback Control Very important to accommodate error We already did that all the time Your body Your brain Your eco system

41 Trichotomy Measurement Yes More Less

42 Proportional Controller Feedback with degree Include error of the output Multiply by the proportion of the error i.e., gain of the control

43 Closed-Loop Control Example Position Control

44 BSim Gizmo task

45 Problems Slow to adapt Solve by increase gain

46 BSim again Try to increase gain

47 Control System Catastrophe

48 Latency Problem Result from the control does not actually reflect the current state Lead to instability Sometime to catastrophe

49 Control System Stability

50 PID Controller

51 Proportional Part Normal close loop Differential Part Adjust input by the differential of the error Integral Part Adjust input by the

52 Tuning PID Adjust P to converge

53 Tuning PID Add D to solve overshoot

54 Tuning PID Add I to solve Steady State

55 Tuning PID Actually an black-art Tuning the knob has highly coupling effect Lets try it

56 Tuning PID summary Change in parameter Rise TimeOvershootS-S ErrorSettling Time Increase PLessMoreLessMinor Increase DLessMoreEliminateMore Increase IMinorLessMinorLess

57 Saturation, Backlash, Dead Zone


59 Open Loop Enhancement Parameters States

60 Bang-Bang Controller

61 Hysteresis

62 More control scheme Feed forward Predictive Adaptive

63 Dynamic System Even if we perfectly understand the system, it is still not trivial to achieve good control

64 Example We can solve for u for a given y Input u System with perfect knowledge Output y

65 Example Taken from Stephen Boyd class Input 2 dimension Output 2 dimension x˙ = Ax + Bu, y = Cx, x(0) = 0 Differential equation Says, we want y = (1,-2) We can solve u to be (-0.63,36)

66 Use the simple

67 Example

68 Final Words You cannot learn how to program robot from looking at this slide BSim? What works well in sim does not always works well in practice Lets do LEGO!

69 Introduce Lego Mindstorm








77 Example Show example of Roverbot Pushbot Guardbot Explorer Mozart

78 Assignment Pick a robot from LEGO kit Do something with it Its 10%

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