Intelligent Robotics

Transcription

1 Intelligent Robotics Intelligent Robotics lectures/2014ws/vorlesung/ir Jianwei Zhang / Eugen Richter University of Hamburg Faculty of Mathematics, Informatics and Natural Sciences Technical Aspects of Multimodal Systems Winter 2014/2015 1

2 Lecture Intelligent Robotics Lecture Monday 14:15-15:45 Room F-334 Web ws/vorlesung/ir Organizers Prof. Dr. Jianwei Zhang / Eugen Richter O ce F-308 / F-225 Phone / {zhang, erichter}@informatik.uni-hamburg.de Secretariat Tatjana Tetsis O ce F-311 Phone tetsis@informatik.uni-hamburg.de 2

3 Seminar Intelligent Robotics Seminar Monday 16:15-17:45 Uhr Room F-334 Web ws/seminar/ir Organizer Benjamin Adler O ce F-307 Phone adler@informatik.uni-hamburg.de 3

4 Outline Intelligent Robotics 1. Introduction 2. Fundamentals 4

5 1Introduction University of Hamburg Outline Intelligent Robotics 1. Introduction Motivation 2. Fundamentals 5

6 1.1 Introduction - Motivation Intelligent Robotics Intelligent robots? A general definition is hard to establish I There is no general answer I Intelligence often equaled to autonomy Depends on the tasks executed by the robot I Sensor data acquisition and processing I Fusion and interpretation of sensor data I Localization I Path planning I Motion / Manipulation I Human-machine interaction I... 6

7 1.1 Introduction - Motivation Intelligent Robotics An interdisciplinary field The field of robotics combines many disciplines I Mechatronics I Architecture- and system design I Control theory I Image processing I Speech processing I Neural networks I Artificial intelligence I... 7

8 1.1 Introduction - Motivation Intelligent Robotics Example: Personal Robot 2 (PR2) 8

9 1.1 Introduction - Motivation Intelligent Robotics Example: Personal Robot 2 (PR2) Hardware platform I Mobile base I 4-wheel omnidirectional drive I Telescoping spine I Fixed laser range finders I 2 hours runtime I Two compliant arms I 4DOFarms I 3DOFwrists I 1.8 kg payload I Passive counterbalance I Gripper camera 9

10 1.1 Introduction - Motivation Intelligent Robotics Example: Personal Robot 2 (PR2) Sensor head I 2DOF(pan&tilt) I 5 MP RGB camera I Kinect RGB-D camera (not shown) I Environment stereo camera I Manipulation stereo camera I LED texture projector I Inertial measurement unit I Tilting laser range finder 10

11 1.1 Introduction - Motivation Intelligent Robotics Example: Personal Robot 2 (PR2) Two grippers I 1 degree of freedom I 90mm range of motion I 3-axis accelerometers I Fingertip pressure sensor arrays I Gripper cameras in forearm 11

12 1.1 Introduction - Motivation Intelligent Robotics Example: Personal Robot 2 (PR2) Two on-board computers I Dual Quad-Core i7 Xeon I 24 GB main memory I 1.5 TB removable hard drives I Multi-gigabit interconnections I Synchronized network cameras I EtherCAT communication bus I 1 khz motor control loop 12

13 1.1 Introduction - Motivation Intelligent Robotics Other robot examples Very di erent platforms I Service robots (TASER) I Humanoid robots (HOAP-2) I Mobile platforms (Pioneer) I Modular robots (GZ-1) I Cleaning robots (SkyCleaner) I Educational robots (NXT,...) ) System architectures ) Mechanics ) Sensors, Actuators )... 13

14 1.1 Introduction - Motivation Intelligent Robotics What is the purpose of this lecture? This lecture will cover topics such as I Fundamental sensor/actuator technology I e.g. Rotation, motion, grasping,... I Established data processing/fusion algorithms I e.g. State estimation, image processing, control,... I Application examples 14

15 2Fundamentals University of Hamburg Outline Intelligent Robotics 1. Introduction 2. Fundamentals Introduction Sensor data acquisition Sensor characteristics Literature 15

16 2.1 Fundamentals - Introduction Intelligent Robotics Sensor applications in robotics I Integration of sensors continues to gain importance in the development of autonomous and intelligent robotic systems I Throughout the development process the perception-action-cycle is of primary importance I The perception-action-cycle governs the sensing of the environment through the sensors and the transition to the adaptive manipulation of the environment as a result of an action I In case of interactive use of robotic systems a situation-based alteration of the sequence of actions is particularly important 16

17 2.1 Fundamentals - Introduction Intelligent Robotics Perception-Action-Cycle Sensor data preprocessing Sensor data fusion Environment Perception-Action-Cycle Feature extraction Environment modeling Pattern recognition 17

18 2.1 Fundamentals - Introduction Intelligent Robotics Perception-Action-Cycle (cont.) 1. Data acquisition: The sensors convert the stimuli and output an analog or digital signal 2. Data preprocessing: Acquired data is filtered, normalized and/or scaled, etc. 3. Data fusion: Redundant and multi-dimensional sensor data is combined/fused in order to obtain more robust measurement results 4. Feature extraction: Extraction of features representing a mathematical model of the sensed environment in order to approximate the natural human perception 18

19 2.1 Fundamentals - Introduction Intelligent Robotics Perception-Action-Cycle (cont.) 5. Pattern recognition: Extracted features are searched for patterns in order to classify the data 6. Environment modeling: Successfully classified patterns are used to model the environment of the robotic system n. Manipulation: Based on the model of the environment sets of goal-oriented actions are executed manipulating the environment (using robotic arms, grippers, wheels, etc.) 19

20 2.1 Fundamentals - Introduction Intelligent Robotics Sensor examples I Intrinsic sensors: Incremental encoder, angle encoder, tachometer, gyroscope,... I Extrinsic sensors (force/pressure): Strain gauge, force-torque sensor, piezoelectric sensor (crystal and ceramic),... I Extrinsic sensors (distance): Sonar sensor, infrared sensor, laser range finder,... I Visual sensors: Linear camera, CCD-/CMOS-camera, stereo vision system, omnidirectional vision system,... 20

21 2.1 Fundamentals - Introduction Intelligent Robotics Asimpleexample 21

22 2.1 Fundamentals - Introduction Intelligent Robotics What is a sensor? A sensor consists of two parts: I The water level indicator I The human eye ) Perception of the level indicator results in a signal to the brain Definition A sensor is a unit, which I receives a signal or stimulus I and reacts to it 22

23 2.1.1 Fundamentals - Introduction - Natural and physical sensors Intelligent Robotics Natural and physical sensors Natural sensors: I A reaction is an electrochemical signal on neural pathways I Examples: Auditory sense, visual sense, tactile sense,... Physical sensors: Definition A physical sensor is a unit, which I receives a signal or stimulus I and reacts to it with an electrical signal 23

24 2.1.1 Fundamentals - Introduction - Natural and physical sensors Intelligent Robotics Input signal I A sensor converts a (generally) non-electrical signal into an electrical one I This signal is referred to as a stimulus Definition A stimulus is a I quantity, I characteristic or I state, which is perceived and converted into an electrical signal 24

25 2.1.1 Fundamentals - Introduction - Natural and physical sensors Intelligent Robotics Output signal I The output signal can be I I I a voltage, acurrentor a charge I Furthermore, there s the option to distinguish by I I I amplitude, frequency or phase 25

26 2.1.2 Fundamentals - Introduction - Sensor types Intelligent Robotics Sensor types I Intrinsic: Information about the internal system state I Extrinsic: Information about the system environment I Active: Modify applied electrical signal on alteration of the stimulus I Passive: Create an electrical signal directly on alteration of the stimulus 26

27 2.1.2 Fundamentals - Introduction - Sensor types Intelligent Robotics Sensor types: 1.: extrinsic, passive 2. und 3.: intrinsic, passive 4.: intrinsic, active 5.: intrinsic (in data acquisition), passive 27

28 2.1.3 Fundamentals - Introduction - Sensor classification Intelligent Robotics Sensor classification Classification of sensors by: I Type of stimulus I Characteristics, specification and parameters I Type of stimulus detection I Conversion of stimulus to output signal I Sensor material I Field of application 28

29 2.1.3 Fundamentals - Introduction - Sensor classification Intelligent Robotics Classification example SENSORS INTRINSIC EXTRINSIC Encoder Tachometer Gyroscope CONTACT NON- CONTACT Bumper Force- Torque Microphone Infrared Laser range finder Camera 29

30 2.2 Fundamentals - Sensor data acquisition Intelligent Robotics Outline 2. Fundamentals Introduction Sensor data acquisition Sensor characteristics Literature 30

31 2.2 Fundamentals - Sensor data acquisition Intelligent Robotics Measurement with sensors I Important scientific criterion: Reproducibility I Scientific statements have to be comparable I Statements must be quantitative and based on measurements I Measurement result consists of: I I Numerical value Measuring unit I Additionally: Declaration of measurement accuracy Measuring errors No measuring process yields a flawless and entirely accurate result! 31

32 2.2.1 Fundamentals - Sensor data acquisition - Measurement deviation Intelligent Robotics Measurement deviation (Measuring error) Systematic deviation ("systematic error"): I Deviation is caused by the sensor I For example: wrong calibration, persistent sources of interference like friction, etc. I Elimination only possible through elaborate examination of the error source Random deviation ("random or statistical error"): I Deviation is caused by inevitable, disorderly interference I Repeated measurements yield di erent results I Individual results fluctuate around a mean value 32

33 2.2.2 Fundamentals - Sensor data acquisition - Error declaration Intelligent Robotics Error declaration I Measurement is always a icted with uncertainty I Example: Distance measurement I Distance to an object is measured repeatedly Individual measurement results: 4, 40 m 4, 40 m 4, 38 m 4, 41 m 4, 42 m 4, 39 m 4, 40 m 4, 39 m 4, 40 m 4, 41 m I Individual measurement results vary 33

34 2.2.2 Fundamentals - Sensor data acquisition - Error declaration Intelligent Robotics Histogram Measurements can be illustrated in a histogram: 34

35 2.2.2 Fundamentals - Sensor data acquisition - Error declaration Intelligent Robotics Mean value The mean value x of the individual measurements x i is determined as follows: x = 1 NX x i N The mean value is also called arithmetic average or best estimate for the true value µ i=1 Note: µ is the mean or expected value of the population (the set of all possible measurement values), frequently called "true" value x w of the measured parameter X: E(X) =µ = x w. We assume that the measured parameter X is a (normally distributed) stochastic variable. 35

36 2.2.2 Fundamentals - Sensor data acquisition - Error declaration Intelligent Robotics Absolute and relative measurement error Deviation is specified in two di erent fashions: I Absolute measurement deviation ("Absolute error"): The absolute error x i of a single measurement x i equals the deviation from the mean value x of all N measurements {x n n 2{1...N}} I Uses the same unit as the measured value I x i = x i x I Relative measurement deviation ("Relative error"): The relative error x rel is the ratio between absolute error and measured value x i x i I Has no dimension, usually specified as a percentage (%) I x i rel= xi x i 36

37 2.2.2 Fundamentals - Sensor data acquisition - Error declaration Intelligent Robotics Variance of a measurement series The distribution of the single measurement values x i around the arithmetical mean x may also be represented by the variance (variance of a measurement series): s 2 = ( x) 2 = = 1 N 1 1 N 1 NX ( x i ) 2 i=1 NX (x i x) 2 i=1 37

38 2.2.2 Fundamentals - Sensor data acquisition - Error declaration Intelligent Robotics Standard deviation of a measurement series The positive square root of the variance is the called the standard deviation (standard deviation of a measurement series): v u s = t 1 N 1 NX (x i x) 2 i=1 The standard deviation is also known as the average or mean error of a single measurement 38

39 2.2.2 Fundamentals - Sensor data acquisition - Error declaration Intelligent Robotics Standard deviation of the mean I For N!1a discrete distribution of a measurement series transitions into a continuous distribution I The measurements of a physical/technical quantity X are usually assumed to be normally distributed I N!1: x! µ and s! Definition Normalized density function (Gaussian distribution) f (x) = 1 p 2 e (x µ)

40 2.2.2 Fundamentals - Sensor data acquisition - Error declaration Intelligent Robotics Standard deviation of the mean (cont.) 0.4 f(x) µ = 10 σ = µ = 10 σ = µ = 10 σ =2 15 x 20 f (x) = 1 p 2 e (x µ)

41 2.2.2 Fundamentals - Sensor data acquisition - Error declaration Intelligent Robotics Standard deviation of the mean (cont.) The standard deviation of the mean value (also known as error of the mean value) is calculated as follows: s x = = v u t 1 NX (x i x) N(N 1) 2 x p N = s p N i=1 s x is the distribution of the mean values from individual measurement series x around the "true" mean value µ 41

42 2.2.2 Fundamentals - Sensor data acquisition - Error declaration Intelligent Robotics Result of a measurement Definition As the result of a measurement we get: x =( x ± s x )[Unit] 42

43 2.2.3 Fundamentals - Sensor data acquisition - Confidence limit Intelligent Robotics Confidence interval I Interval around a determined mean value of a measurement series that is said to contain the true mean value with a given probability I A confidence interval of ± s x is said to contain the true mean value with a probability of 68 % I For a certainty of 95 % the interval must be extended to ± 2 s x I A certainty of 99 % requires an interval of ± 3 s x 43

44 2.2.4 Fundamentals - Sensor data acquisition - Error propagation Intelligent Robotics Error propagation I Ameasurementuncertaintymustbespecifiedfora measurement value calculated from several measurement parameters I If the measurement value z is defined as z = f (x 1,...,x n ) and x i the measurement uncertainty (maximum error) of each individual measurement parameter, the measurement uncertainty z of the calculated value is z x x n 44

45 2.2.4 Fundamentals - Sensor data acquisition - Error propagation Intelligent Robotics Error propagation (cont.) I The partial derivatives are weight factors for the propagation of individual errors I Weight factors should generally be calculated prior to the measurement I This is the only way to detect the errors with the largest impact on the combined result I Corresponding measurement values have to be determined particularly accurate I The measurement result of an indirectly determined measurement parameter reads as follows: z = z ± z 45

46 2.2.4 Fundamentals - Sensor data acquisition - Error propagation Intelligent Robotics Error propagation (cont.) Two rules of thumb: I Summation und subtraction accumulate the absolute errors I Multiplication and division accumulate the relative errors I Squaring doubles, extracting the square root halves the relative error I The di erence of two parameters with nearly equal values results in a big relative error ) better: measuring di erence directly 46