Robotics. Haslum COMP3620/6320

Transcription

1 Robotics Haslum COMP3620/6320

2 Introduction Robotics Industrial Automation * Repetitive manipulation tasks (assembly, etc). * Well-known, controlled environment. * High-power, high-precision, very expensive. Mobile Robots * Acting in environments that are dangerous or difficult to access (Fire/rescue, mine clearing, Mars, deep oceans) * Domestic (vaccuum cleaners, guides, toys) * Unknown & dynamic environment. * Uncertain sensors & imprecise actuators. * Hard power/size/weight/cost limits.

3 Introduction

4 Introduction Hardware Sensors * Range finders: Sonar, laser, contact sensors. * Imaging: Cameras. * Proprioceptive: shaft encoders, odometers / tachometers, inertial sensors (gyro, accelerometer), compass, force/torque sensors. * External: GPS, beacons, fixed cameras. Effectors * Mobility: Wheels, tracks, legs, rotors / propellers, rudders. * Manipulators: Arm joints, grippers (mechanical, vaccuum, magnetic), attached tools. * Speakers, light signals, etc.

5 Basic Problems & Techniques Localisation & Mapping Two Basic Problems Localisation & Mapping * How does the world look, and where in it am I? * Localisation: Determining the robots position/pose w.r.t. a known environment. * Mapping (SLAM): Constructing a map of the environment. Path/Motion Planning * Knowing the world and where I am in it, how do I get from here to there? * Path planning: Finding a path from A to B free of collisions with the environment (obstacles). * Motion planning: Finding a sequence of motions that take the robot from A to B without collision.

6 Basic Problems & Techniques Localisation & Mapping Localisation Filtering Approach * P(x t ) = P(z t x t ) P(x t x t 1, a t 1 )P(x t 1 ) * Motion model (P(x t x t 1, a t 1 )): deterministic prediction + noise. * Sensor model (P(z t x t )): likelihood of making observation z if state is x. * Common assumption: Noise is Gaussian.

7 Basic Problems & Techniques Localisation & Mapping Representation of the Estimate * Gaussian ( EKF ): - Compact and fast. - Unimodal, assumes linear models. * Probability Grid ( Markov Loc. ): - Discretize configuration space, assign probability to each cell. - Computationally expensive. * Particle Filter ( Monte Carlo Loc. ): - Approximate distribution by a finite sample of configurations. - Computationally expensive (somewhat).

8 Basic Problems & Techniques Localisation & Mapping Simultaneous Localisation and Mapping (SLAM) * Filtering approach like localisation, but landmark positions part of the state. * Problem: Dimensionality of state changes dynamically as new landmarks detected. * Need to (reliably) reidentify landmarks. Demos by S. Thrun (

9 Basic Problems & Techniques Path & Motion Planning Two Basic Problems Localisation & Mapping * How does the world look, and where in it am I? * Localisation: Determining the robots position/pose w.r.t. a known environment. * Mapping (SLAM): Constructing a map of the environment. Path/Motion Planning * Knowing the world and where I am in it, how do I get from here to there? * Path planning: Finding a path from A to B free of collisions with the environment (obstacles). * Motion planning: Finding a sequence of motions that take the robot from A to B without collision.

10 Basic Problems & Techniques Path & Motion Planning Degree-of-Freedom (DOF) * Degree-of-Freedom (DOF): Independent direction of robot movement. * Configuration (state/ pose ) specified by value for each DOF. * Holonomic: # controllable DOF = # effective DOF. - Non-holonomic robot: Harder planning/control problem.

11 Basic Problems & Techniques Path & Motion Planning Work & Configuration Space * Work space (W ): 3D world Robot & environment have simple geometry Collision checking is easy. * Configuration space (C): Space of robot states/poses (dimension = # DOF) Robot is a point, obstacles have complex shapes. * Free space: Configurations robot can reach/occupy. * C W easy, W C hard & often ill-posed.

12 Basic Problems & Techniques Path & Motion Planning Path Planning * Search for a path in free space: Continuous space need to discretize. * Cell decomposition: - Regular grid, subdivision, exact From incomplete to optimal. - Computationally expensive with many dimensions. * Skeletonisation methods. * Probailistic roadmap: - Scales to high-dof problems. - Probabilistically complete, can yield large detours.

13 Basic Problems & Techniques System Architechture Robot System Architechtures Traditional/Hierarchical * Sense Plan Act : Build high-level world model, reason/plan in model, execute. * Too slow, too brittle (sensing/acting failures). Hybrid Reactive * Sense Act : No explicit internal model. * Intelligence emerges from combination of simple behaviours. *...only sometimes, it doesn t (inflexible, dumb ). * Sense Act & Monitor/Learn/(Re-)Plan/Adapt. * 3-layer architectures (deliberative, reactive, control).

14 Applications & Success Stories RHINO & Minerva: Robotic Museum Guides * Demonstrated over 3 days in 1997 at Deutsches Museum in Bonn, and 2 weeks in 1998 at the Smithsonian Museum in Washington. * Navigation in an uncontrolled and crowded environment. * Interaction with untrained users: Clearing the way, attracting attention, interpreting requests. uni-bonn.de/ rhino/ minerva/

15 Applications & Success Stories RoboCup * Robot soccer competition, held (almost) annually since In 2008, 70+ teams, in 4 leagues (+ other events/challenges). - Small (5/team, external camera/computers). - Midsize (4/team, all sensors/computers on-board). - Standard platform (Sony AIBO, Aldebaran NAO). - Humanoid (3/team, kid & teen sizes). * Contributed to advancing s.o.t.a. in mobile robotics hardware and programming from I see the ball to real teamplay. * By the year 2050, a team of fully autonomous humanoid robots that can win against the human world soccer champion team.

16 Applications & Success Stories Mars Exploration Rovers * 2 Rovers, on Mars for over 5 years, have travelled over 7.7 km / 15 km still going! * Daily targets, autonomous navigation & odometry, using stereo vision. * Some autonomous science (rare events captured in nav. cam images).

17 Applications & Success Stories DARPA Grand Challenge 2005 * Challenge: Autonomously driving a course of 212km, * Route defined by coordinates (1 point / 72m). * Dirt road, including some tricky spots. * 5 vehicles finished the course (18 didn t), the fastest in < 7 hours (average of 30 kph).

18 Applications & Success Stories v1.0 N * Note: The southern 6 waypoints in the Parking Lot (Zone 14) are Checkpoints DARPA Urban Challenge 2007 * Challenge: Autonomously driving a 38 checkpoint course on urban streets, given network map, with traffic, obeying road rules. * 11 autonomous and 30 human-driven vehicles simultaneously on the track 6 finished the course. Sample RNDF Waypoint Lane Zone Stop Sign Segment / Zone ID Checkpoint ID Parking Lot* Traffic Circle 4-way Stop