Topics: Introduction to Robotics CS 491/691(X) Lecture 2 Instructor: Monica Nicolescu

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

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

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

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

Reactive Control: Don’t think, react! 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 CS 491/691(X) - Lecture 2

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search through potentially all possible plans  these take a long time Requires a world model, which may become outdated Too slow for real-time response Advantages Capable of learning and prediction Finds strategic solutions CS 491/691(X) - Lecture 2

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

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

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

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

A Brief History of Robotics Robotics grew out of the fields of control theory, cybernetics and AI 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

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

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

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

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

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

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

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: Inhibitory connections Vehicle 1 Being “ALIVE” “FEAR” and “AGGRESSION” Vehicle 2 “LOVE” CS 491/691(X) - Lecture 2

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

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

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

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

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

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

Challenges Perception Actuation Thinking Dynamic environments 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

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

Effectors & Actuators Effector Actuator 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

Actuation Passive actuation E.g.: gliding (flying squirrels) 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

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

DC Motors DC (direct current) motors How do they work? 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

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

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

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

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

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

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

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

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