Introduction to Information Science and Technology (IST) Part IV: - PowerPoint PPT Presentation

Introduction to Information Science and Technology (IST) Part IV: Intelligent Machines and Robotics Control Sören Schwertfeger / 师泽 仁 ShanghaiTech University

2 IST ShanghaiTech University - SIST - 19.05.2016 Most important capability (for autonomous mobile robots) How to get from place A to place B? (safely and efficiently)

3 IST ShanghaiTech University - SIST - 19.05.2016 General Control Scheme for Mobile Robot Systems Localization Cognition & AI Position Map Building Path Planning Global Map Environment Model Path Local Map Information Path Extraction Execution Motion Control Vision Perception This lecture: Navigation Actuator Commands Raw data Control and Navigation Sensing Acting Real World Environment With material from Roland Siegwart and Davide Scaramuzza, ETH Zurich

4 IST ShanghaiTech University - SIST - 19.05.2016 • Autonomous mobile robots • Different levels: move around in the • Control: environment. Therefore ALL of • How much power to the motors to move in that direction, reach desired them: speed • They need to know where they • Navigation: are . • Avoid obstacles • They need to know where their • Classify the terrain in front of you goal is. • Predict the behavior (motion) of other agents (humans, robots, animals, • They need to know how to get machines) there. • Planning: • Long distance path planning • What is the way, optimize for certain parameters

5 IST ShanghaiTech University - SIST - 19.05.2016 Navigation, Motion & Motor Control Global Goal • Navigation/ Motion Control: Planner • Where to drive to next in order to reach goal • Output: motion vector (direction) and speed Path (0.1 Hz) • For example: • follow path (Big Model) Path Following/ 3 Local Goal • go to unexplored area (Big Model) • drive forward (Small Model) Navigation • be attracted to goal area (Small Model) Goal (10 Hz) • Motion Control: 2 Motion Control • How use propulsion to achieve motion vector Wheel • Motor Control: Speeds (1000 Hz) • How much power to achieve propulsion (wheel 1 speed) Motor Control

6 IST ShanghaiTech University - SIST - 19.05.2016 MOTOR & MOTION CONTROL

7 IST ShanghaiTech University - SIST - 19.05.2016 Overview • Assume we have a goal pose Goal (close by) Pose • Calculate Inverse Kinematics (last lecture, LaValle) => Kinematics • Desired wheel speeds Wheel • Typically not just one wheel => Speeds • Many motor controllers, motors, encoders Measured Motor Control Speeds • Motor control loop Power Rotation of • Pose control loop Axis Encoder Motor

8 IST ShanghaiTech University - SIST - 19.05.2016 Motor Control • How much power is needed for desired reference speed? • Inertia of the motor + robot: • Need more power during acceleration of robot vs. constant speed • Up hill/ down hill different power needs • Motors are even used to break the robot! Image: wikipedia • Closed loop control (negative feedback) • Proportional-Integral- Reference Derivative controller (PID) Speed Error Power Speed Controller Motor + • Motor speed reacts slowly to - power changes Measured speed Encoder

9 IST ShanghaiTech University - SIST - 19.05.2016 Image: zembedded.com Motor Driver • How can Controller control power? • Pulse Width Modulation (PWM) • Frequency in kHz (= period less than 1ms) • Signal (e.g. 3V) to Motor Driver • Motor Driver switches according to signal and direction => H-bridge • Output: Power (e.g. 24V, 4A) • PWM modulated Motor Power Driver • Sound of motors from PWM! PWM (PWM Signal modulated) + Direction Reference Reference Speed Speed Error Error Power Speed Speed Controller Controller Motor Motor + + - - Measured speed Measured speed Encoder Encoder Steven M. LaValle, “Mobile Robotics: An Information Space Approach”

10 IST ShanghaiTech University - SIST - 19.05.2016 Pose Control • Ground robots: • Check difference current pose ó desired pose • Using wheel encoders => Forward kinematics => Odometry • Using external localization (e.g. in map, via markers) • => slow update rate (10 – 0.1 Hz) • Quadcopters: • Translation: roll or pitch • Very sensitive to roll and pitch • => keep robot at desired roll/ pitch angles Image from: Weihua Zhao, Tiauw Hiong Go, “Quadcopter formation • Very high update rate > 1000 Hz flight control combining MPC and robust feedback linearization”

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12 IST ShanghaiTech University - SIST - 19.05.2016 Video • Localization of robot pose via external cameras • Control loops: • Task control: Position of net => position of robots • Take dynamics into account • Pose control: Position of each quadcopter • Flight control: Roll and pitch to reach pose • Motor control

13 IST ShanghaiTech University - SIST - 19.05.2016 BEHAVIOR BASED ROBOTICS “Small Model” – “Little Brain”

14 IST ShanghaiTech University - SIST - 19.05.2016 Control Architectures / Strategies • Control Loop § Two Approaches • dynamically changing § Classical AI (Big Model) • no compact model available • complete modeling • function based • many sources of uncertainty • horizontal decomposition § New AI (Nouvelle AI; "Position" Small Model; Behavior Localization Cognition Global Map Based Robotics) • sparse or no modeling Environment Model Path Local Map • behavior based • vertical decomposition Real World Perception Motion Control Environment • bottom up

15 IST ShanghaiTech University - SIST - 19.05.2016 Two Approaches § Classical AI (model based navigation) § complete modeling § function based § horizontal decomposition § New AI (behavior based navigation) § sparse or no modeling § behavior based § vertical decomposition § bottom up § Possible Solution § Combine Approaches (= Hybrid Approach)

16 IST ShanghaiTech University - SIST - 19.05.2016 Mixed Approach Depicted into the General Control Scheme Localization Cognition Position Position Local Map Local Map Perception to Feedback Avoidance Environment Position Obstacle Action Path Model Local Map Real World Environment Perception Motion Control

17 IST ShanghaiTech University - SIST - 19.05.2016 Emergence • Adaptive behavior • emerges from complex interactions between body, world and brain • Non-centrally controlled (or designed) behavior • results from the interactions of multiple simple components • Meanings: • Surprising situations or behaviors • Property of system not contained in any of its parts • Behavior resulting from agent-environment interaction not explicitly programmed • Ant colony: • self-organized; simple individuals; local interactions => • emergent behavior - No global control

18 IST ShanghaiTech University - SIST - 19.05.2016 Grey Walter’s Tortoise • Turtle shape robots 1949 • Purely analogue electronics • Phototaxis: go towards the light • Sensors: • 1 photocell, • 1 bump sensor • 2 motors • Reactive control

19 IST ShanghaiTech University - SIST - 19.05.2016 Grey Walter’s Tortoise • Behaviors: • Seek light • Head toward weak light • Back away from bright light • Turn and push (obstacle avoidance) • Recharge battery

20 IST ShanghaiTech University - SIST - 19.05.2016 Turtle Principles • Simple is better • e.g., clever recharging strategy • Exploration/ speculation: keeps moving • except when charging • Attraction: • motivation to approach light • Aversion: • motivation to avoid obstacles, slopes

21 IST ShanghaiTech University - SIST - 19.05.2016 Tortoise behavior • A path: a candle on top of the shell Video …

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23 IST ShanghaiTech University - SIST - 19.05.2016 Braitenberg’s Vehicles • Valentino Braitenberg Wheel/motor (1926) Inhibitory Circuit • 1984: “Vehicles: Experiments in Synthetic Light Sensor Body Psychology” Excitatory Circuit Non-motored wheels under here To keep it standing Material: Tommy Walsh http://my.fit.edu/~twalsh/Braitenberg%20Vehicles.ppt

24 IST ShanghaiTech University - SIST - 19.05.2016 Definitions • Inhibitory circuit: when sensor gets activated, motor slows • Excitatory circuit: when sensor gets activated, motor speeds • Sensor is a light sensor, unless otherwise noted

25 IST ShanghaiTech University - SIST - 19.05.2016 Vehicle 1: Alive Basic Braitenberg vehicle: Goes towards light source

26 IST ShanghaiTech University - SIST - 19.05.2016 More light right → Vehicle 2: Cowardly right wheel turns faster → turns towards the left, away from the light. Demonstrates “fight or flight” instinct in animals Turns away from light if one sensor is activated more than the other If both are equal, light source is “attacked”

27 IST ShanghaiTech University - SIST - 19.05.2016 Vehicle 2b: Aggressive Faces light source and drives toward it

28 IST ShanghaiTech University - SIST - 19.05.2016 Vehicle 3: Loving Drives forward Faces the light source and slows down Models love/adoration