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Topics: Introduction to Robotics CS 491/691(X)

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  1. Topics: Introduction to RoboticsCS 491/691(X) Lecture 2 Instructor: Monica Nicolescu

  2. Review • Definitions • Robots, robotics • Robot components • Sensors, actuators, control • State, state space • Representation • Spectrum of robot control • Reactive, deliberative CS 491/691(X) - Lecture 2

  3. Robot Control • Robot control is the means by which the sensing and action of a robot are coordinated • The infinitely many possible robot control programs all fall along a well-defined control spectrum • The spectrum ranges from reacting to deliberating CS 491/691(X) - Lecture 2

  4. Spectrum of robot control From “Behavior-Based Robotics” by R. Arkin, MIT Press, 1998 CS 491/691(X) - Lecture 2

  5. Robot control approaches • Reactive Control • Don’t think, (re)act. • Deliberative (Planner-based) Control • Think hard, act later. • Hybrid Control • Think and act separately & concurrently. • Behavior-Based Control (BBC) • Think the way you act. CS 491/691(X) - Lecture 2

  6. Technique for tightly coupling perception and action to provide fast responses to changing, unstructured environments Collection of stimulus-response rules Limitations No/minimal state No memory No internal representations of the world Unable to plan ahead Unable to learn Advantages Very fast and reactive Powerful method: animals are largely reactive Reactive Control:Don’t think, react! CS 491/691(X) - Lecture 2

  7. Deliberative Control: Think hard, then act! • In DC the robot uses all the available sensory information and stored internal knowledge to create a plan of action: sense  plan  act (SPA) paradigm • Limitations • Planning requires search through potentially all possible plans  these take a long time • Requires a world model, which may become outdated • Too slow for real-time response • Advantages • Capable of learning and prediction • Finds strategic solutions CS 491/691(X) - Lecture 2

  8. Hybrid Control: Think and act independently & concurrently! • Combination of reactive and deliberative control • Reactive layer (bottom): deals with immediate reaction • Deliberative layer (top): creates plans • Middle layer: connects the two layers • Usually called “three-layer systems” • Major challenge: design of the middle layer • Reactive and deliberative layers operate on very different time-scales and representations (signals vs. symbols) • These layers must operate concurrently • Currently one of the two dominant control paradigms in robotics CS 491/691(X) - Lecture 2

  9. Behavior-Based Control:Think the way you act! • An alternative to hybrid control, inspired from biology • Has the same capabilities as hybrid control: • Act reactively and deliberatively • Also built from layers • However, there is no intermediate layer • Components have a uniform representation and time-scale • Behaviors: concurrent processes that take inputs from sensors and other behaviors and send outputs to a robot’s actuators or other behaviors CS 491/691(X) - Lecture 2

  10. Behavior-Based Control:Think the way you act! • “Thinking” is performed through a network of behaviors • Utilize distributed representations • Respond in real-time • are reactive • Are not stateless • not merely reactive • Allow for a variety of behavior coordination mechanisms CS 491/691(X) - Lecture 2

  11. Fundamental Differences of Control • Time-scale: How fast do things happen? • how quickly the robot has to respond to the environment, compared to how quickly it can sense and think • Modularity: What are the components for control? • Refers to the way the control system is broken up into modules and how they interact with each other • Representation: What does the robot keep in its brain? • The form in which information is stored or encoded in the robot CS 491/691(X) - Lecture 2

  12. A Brief History of Robotics • Robotics grew out of the fields of control theory, cyberneticsandAI • Robotics, in the modern sense, can be considered to have started around the time of cybernetics (1940s) • Early AI had a strong impact on how it evolved (1950s-1970s), emphasizing reasoning and abstraction, removal from direct situatedness and embodiment • In the 1980s a new set of methods was introduced and robots were put back into the physical world CS 491/691(X) - Lecture 2

  13. Control Theory • The mathematical study of the properties of automated control systems • Helps understand the fundamental concepts governing all mechanical systems (steam engines, aeroplanes, etc.) • Relies on the idea of feedback control • Thought to have originated with the ancient greeks • Time measuring devices (water clocks), water systems • Forgotten and rediscovered in Renaissance Europe • Heat-regulated furnaces (Drebbel, Reaumur, Bonnemain) • Windmills • James Watt’s steam engine (the governor) CS 491/691(X) - Lecture 2

  14. Feedback Control • Definition:technique for bringing and maintaining a system in a goal state, as the external conditions vary • Idea: continuously feeding back the current state and comparing it to the desired state, then adjusting the current state to minimize the difference (negative feedback). • The system is said to be self-regulating • E.g.: thermostats • if too hot, turn down, if too cold, turn up CS 491/691(X) - Lecture 2

  15. Cybernetics • Pioneered by Norbert Wiener in the 1940s • Comes from the Greek word “kibernts” – governor, steersman • Combines principles of control theory, information science and biology • Sought principles common to animals and machines, especially with regards to control and communication • Studied the coupling between an organism and its environment CS 491/691(X) - Lecture 2

  16. W. Grey Walter’s Tortoise • Machina Speculatrix” (1953) • 1 photocell, 1 bump sensor, 1 motor, 3 wheels, 1 battery • Behaviors: • seek light • head toward moderate light • back from bright light • turn and push • recharge battery • Uses reactive control, with behavior prioritization CS 491/691(X) - Lecture 2

  17. Principles of Walter’s Tortoise • Parsimony • Simple is better • Exploration or speculation • Never stay still, except when feeding (i.e., recharging) • Attraction (positive tropism) • Motivation to move toward some object (light source) • Aversion (negative tropism) • Avoidance of negative stimuli (heavy obstacles, slopes) • Discernment • Distinguish between productive/unproductive behavior (adaptation) CS 491/691(X) - Lecture 2

  18. Braitenberg Vehicles • Valentino Braitenberg (1980) • Thought experiments • Use direct coupling between sensors and motors • Simple robots (“vehicles”) produce complex behaviors that appear very animal, life-like • Excitatory connection • The stronger the sensory input, the stronger the motor output • Light sensor  wheel: photophilic robot (loves the light) • Inhibitory connection • The stronger the sensory input, the weaker the motor output • Light sensor  wheel: photophobic robot (afraid of the light) CS 491/691(X) - Lecture 2

  19. Example Vehicles • Wide range of vehicles can be designed, by changing the connections and their strength • Vehicle 1: • One motor, one sensor • Vehicle 2: • Two motors, two sensors • Excitatory connections • Vehicle 3: • Two motors, two sensors • Inhibitory connections Vehicle 1 Being “ALIVE” “FEAR” and “AGGRESSION” Vehicle 2 “LOVE” CS 491/691(X) - Lecture 2

  20. Artificial Intelligence • Officially born in 1956 at Dartmouth University • Marvin Minsky, John McCarthy, Herbert Simon • Intelligence in machines • Internal models of the world • Search through possible solutions • Plan to solve problems • Symbolic representation of information • Hierarchical system organization • Sequential program execution CS 491/691(X) - Lecture 2

  21. AI and Robotics • AI influence to robotics: • Knowledge and knowledge representation are central to intelligence • Perception and action are more central to robotics • New solutions developed: behavior-based systems • “Planning is just a way of avoiding figuring out what to do next” (Rodney Brooks, 1987) • Distributed AI (DAI) • Society of Mind (Marvin Minsky, 1986): simple, multiple agents can generate highly complex intelligence • First robots were mostly influenced by AI (deliberative) CS 491/691(X) - Lecture 2

  22. Shakey • At Stanford Research Institute (late 1960s) • A deliberative system • Visual navigation in a very special world • STRIPS planner • Vision and contact sensors CS 491/691(X) - Lecture 2

  23. Early AI Robots: HILARE • Late 1970s • At LAAS in Toulouse • Video, ultrasound, laser rangefinder • Was in use for almost 2 decades • One of the earliest hybrid architectures • Multi-level spatial representations CS 491/691(X) - Lecture 2

  24. Early Robots: CART/Rover • Hans Moravec’s early robots • Stanford Cart (1977) followed by CMU rover (1983) • Sonar and vision CS 491/691(X) - Lecture 2

  25. Lessons Learned • Move faster • Think in such a way as to allow this action • New types of robot control: • Reactive, hybrid, behavior-based • Control theory • Continues to thrive in numerous applications • Cybernetics • Biologically inspired robot control • AI • Non-physical, “disembodied thinking” CS 491/691(X) - Lecture 2

  26. Challenges • Perception • Limited, noisy sensors • Actuation • Limited effectors • Thinking • Time consuming in large state spaces • Dynamic environments • Impose fast reaction times CS 491/691(X) - Lecture 2

  27. Key Issues of Behavior-Based Control • Situatedness: • Robot is entirely situated in the real world • Embodiment: • Robot has a physical body • Emergence: • Intelligence from the interaction with the environment • Grounding in reality • Correlation with the reality • Scalability • Reaching high-level intelligence CS 491/691(X) - Lecture 2

  28. Effectors & Actuators • Effector • Any device robot that has an impact on the environment • Effectors must match a robot’s task • Controllers command the effectors to achieve the desired task • Actuator • A robot mechanism that enables the effector to execute an action • Robot effectors are very different than biological ones • Robots: wheels, tracks, grippers • Robot actuators: • Electric motors, hydraulic, pneumatic cylinders, temperature-sensitive materials CS 491/691(X) - Lecture 2

  29. Actuation • Passive actuation • Use potential energy and interaction with the environment • E.g.: gliding (flying squirrels) • Robotics examples: • Tad McGeer’s passive walker • Actuated by gravity CS 491/691(X) - Lecture 2

  30. Types of Actuators • Electric motors • Hydraulics • Pneumatics • Photo-reactive materials • Chemically reactive materials • Thermally reactive materials • Piezoelectric materials CS 491/691(X) - Lecture 2

  31. DC Motors • DC (direct current) motors • Convert electrical energy into mechanical energy • Small, cheap, reasonably efficient, easy to use • How do they work? • Electrical current through loops of wires mounted on a rotating shaft • When current is flowing, loops of wire generate a magnetic field, which reacts against the magnetic fields of permanent magnets positioned around the wire loops • These magnetic fields push against one another and the armature turns CS 491/691(X) - Lecture 2

  32. Motor Efficiency • DC motors are not perfectly efficient • Some limitations (mechanical friction) of motors • Some energy is wasted as heat • Industrial-grade motors (good quality): 90% • Toy motors (cheap): efficiencies of 50% • Electrostatic micro-motors for miniature robots: 50% CS 491/691(X) - Lecture 2

  33. Operating Voltage • Making the motor run requires electrical power in the right voltage range • Most motors will run fine at lower voltages, though they will be less powerful • Can operate at higher voltages at expense of operating life CS 491/691(X) - Lecture 2

  34. Operating/Stall Current • When provided with a constant voltage, a DC motor draws current proportional to how much work it is doing • When there is no resistance to its motion, the motor draws the least amount of current • Moving in free space  less current • Pushing against an obstacle  drain more current • If the resistance becomes very high the motor stalls and draws the maximum amount of current at its specified voltage (stall current) CS 491/691(X) - Lecture 2

  35. Torque • Torque: rotational force that a motor can deliver at a certain distance from the shaft • The more current through a motor, the more torque at the motor’s shaft • Strength of magnetic field generated in loops of wire is directly proportional to amount of current flowing through them and thus the torque produced on motor’s shaft CS 491/691(X) - Lecture 2

  36. Stall Torque • Stall torque:the amount of rotational force produced when the motor is stalled at its recommended operating voltage, drawing the maximal stall current at this voltage • Typical torque units: ounce-inches • 5 oz.-in. torque means motor can pull weight of 5 oz up through a pulley 1 inch away from the shaft CS 491/691(X) - Lecture 2

  37. Power of a Motor • Power: product of the output shaft’s rotational velocity and torque • No load on the shaft • Rotational velocity is at its highest, but the torque is zero • The motor is spinning freely (it is not driving any mechanism) • Motor is stalled • It is producing its maximal torque • Rotational velocity is zero A motor produces the most power in the middle of its performance range. CS 491/691(X) - Lecture 2

  38. How Fast do Motors Turn? • Free spinning speeds (most motors): • 3000-9000 RPM (revolutions per minute) [50-150 RPS] • High-speed, low torque • Drive light things that rotate very fast • What about driving a heavy robot body or lifting a heavy manipulator? • Need more torque and less speed CS 491/691(X) - Lecture 2

  39. Readings • F. Martin: Section 4.1 • M. Matarić: Chapters 2, 4 CS 491/691(X) - Lecture 2