Robotica Lecture 3 Robot Control Robot control is

Robotica Lecture 3

Robot Control • Robot control is the mean 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 Lecture 3 2

Robot Control Architectures • There are infinitely many ways to program a robot, but there are only few types of robot control: – Deliberative control – Reactive control – Hybrid control – Behavior-based control • Numerous “architectures” are developed, specifically designed for a particular control problem • However, they all fit into one of the categories above Lecture 3 3

Spectrum of robot control From “Behavior-Based Robotics” by R. Arkin, MIT Press, 1998 Lecture 3 4

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 – It evolves from reactive control. Lecture 3 5

Thinking vs. Acting • Thinking/Deliberation – slow, speed decreases with complexity – involves planning (looking into the future) to avoid bad solutions – thinking too long may be dangerous – requires (a lot of) accurate information – flexible for increasing complexity • Acting/Reaction – fast, regardless of complexity – innate/built-in or learned (from looking into the past) – limited flexibility for increasing complexity Lecture 3 6

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 • Advantages – No/minimal state – Very fast and reactive – No memory – Powerful method: animals are largely reactive – No internal representations of the world – Unable to plan ahead – Unable to learn Lecture 3 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search through potentially all possible plans – It takes a long time – It requires a world model, which may become outdated – Too slow for real-time response • Advantages – Capable of learning and prediction – Finds strategic solutions Lecture 3 8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 • 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 Lecture 3 9

Behavior-Based Control: Think the way you act! • It evolves from reactive control, inspired from biology • It has more capabilities than reactive control: – Act reactively using moderate representation • Built from layers – Components have 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 to achieve some goals Lecture 3 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 only reactive • Allow for a variety of behavior coordination mechanisms Lecture 3 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 of the control system? – 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 Lecture 3 12

How to Choose a Control Architecture? • For any robot, task, or environment consider: – Is there a lot of sensor noise? – Does the environment change or is static? – Can the robot sense all that it needs? – How quickly should the robot sense or act? – Should the robot remember the past to get the job done? – Should the robot look ahead to get the job done? – Does the robot need to improve its behavior and be able to learn new things? Lecture 3 13

A Robotic Example • Use feedback to design a wall following robot • What sensors to use, what info will they provide? – Contact: the least information – IR: information about a possible wall, but not distance – Sonar, laser: would provide distance – Bend sensor: would provide distance • Control If distance-to-wall is right, then keep going If distance-to-wall is larger then turn toward the wall else turn away from the wall Lecture 3 14

Control Behavior • What is a behavior? – A set of actions, each of which associated with a given perceptual schema (reflex), such that they can be interpreted as a method to achieve and/or maintain a well specified goal. Lecture 3 15

Feedback Control • Feedback control = having a system achieve and maintain a desired state by continuously comparing its current and desired states, then adjusting the current state to minimize the difference • Also called closed loop control Lecture 3 16