Sistemi per il Governo dei Robot (mod B): Organizzazione. 48 ore, 6 CFU alberto.finzi@unina.it. Lezioni: - Lunedì 14:00-16:00 (C11???

Transcription

1 Sistemi per il Governo dei Robot (mod B): Organizzazione 48 ore, 6 CFU alberto.finzi@unina.it web-page: people.na.infn.it/%7efinzi/didattica Lezioni: - Lunedì 14:00-16:00 (C11???) - Mercoledì 11:00-13:00 13 (Lab)

2 Obiettivi del Corso Completa il corso di Sistemi per il Governo di Robot (Modulo A): Robotica Probabilistica: metodi statistici in robotica Robotica Mobile: navigazione, localizzazione, mapping, esplorazione Architetture Ibride: esecuzione, monitoraggio, pianificazione

3 Autonomous Robots Coherent, flexible, adaptive, goal-oriented oriented behavior Unstructured environments Unstructured environments Field Robotics, Service Robotics (Mobile Robotics) Navigation: Localization, mapping, exploration Probabilistic Robotics: : statistical methods in robotics From navigation to deliberation: - SLAM, Exploration, DT Planning Argomenti Architectures for autonomy: High-level/Low level control loops (sense/plan/exec) Hierarchical, Reactive, Hybrid, Cognitive Architectures

4 Materiale Didattico Lucidi, dispense e articoli sul sito del corso Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, The MIT Press, 2004

5 Materiale didattico Slides, papers, on-line references Murphy R.R. - Introduction to AI robotics - MIT Press Arkin R.C. - Behavior-based robotics - MIT Press 1998 Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, The MIT Press, 2004

6 Modalità di accertamento del profitto: Modalità di accertamento del profitto: Progetto Presentazione e Discussione Esami

7 Autonomous: Greek: Automaton (auto + matos) auto: self matos: thinking, animated, willing Autonomous Robots Robots: Czech: Robota (work) and robotnik (worker) self-willed but task-oriented

8 Autonomous Robots Autonomous robots: robots that can perform desired tasks in unstructured environments without continuous human guidance. Industrial robots (fixed-base) are fast, accurate, ripetitive but limited in work space; To operate in the real/external world robots must be able to cope with: large, unstructured, dynamic, uncertain, partially observable environments

9 Autonomous Robots

10 Robotics Applications Industrial Robotics: Factory, mining Field Robotics: Umnanned Vehicles: UAV, UTV, Planetary Rovers Service Robotics: Personal services Entertainment t t Social Robotics

11 Robotics Applications: robots in the real world structured, controlled Industrial Unstructured, teleop., autonomous Field unstructured, proactive, interactive Service Manufacturing Surgery Aerospace Underwater Rescue autonomy Home Entertainment Health care 11

12 Service Robotics Robotica umanoide UAV UTV (mobile robotics)

13 Classic Robotics (AI '70): Model-based (representation = world), symbolic, no sensing, only reasoning Reactive Robotics (Ethology '80): No models (world is the model), reactive: sense-act (insects-like) Hybrid Architectures (Agents '90): Model-based (rep. abstract, but fine) + reactive (3T architectures) Probabilistic Robotics (Mobile Robotics '90): Robotic Paradigms Approximate/probabilistic models (rep.!= world), actuators Approximate/probabilistic models (rep.! world), actuators not reliable, sensors not accurate; Sensors/Actuators models tight integration.

14 Classical Paradigm Hierarchical Architecture Knowledge Representation and formal reasoning Closed World: Complete model of the environment Deterministic, i ti observable bl Functional decomposition of the activities [Shakey 1969] Classical Control Schema

15

16 1970-Shakey the robot Remote controlled by a computer. Reasoning program fed very selective spatial data. Weak edge-based processing of camera and laser range measurements. Plans involving moving from place to place and pushing blocks to achieve a goal.

17

18 Reactive Paradigm Situated: interacting with the env. No Memory, no model: memory and model are the external world (stygmergic) Behavior-based: sense e act coupuled and associated with the behavior (Fixed Action Patterns) Sense-Act Paradigm Subsumption Architecture [Brooks 1986] Potential Fields

19

20

21

22

23

24

25 Reactive Paradigm

26 Reactive Paradigm

27 Reactive Paradigm

28 Reactive Paradigm

29 Reactive Paradigm

30 Hybrid Paradigm It combines the two paradigms (3T [Gat 1996, Bonasso et. al 1998]) Model-based planning and reasoning Reactive at the low-level control E.g. ATLANTIS [Gat 1996]: - Control Layer, - Sequencing Layer, - Deliberative e Layer. Sense-Act + Plan

31 Hybrid Paradigm ATLANTIS [Gat 1992] Task Control Architecture [Simmons 1994] 3T [Bonasso 1996] RHINO [Burgard et al 1995] LAAS [Alami 1998] CIRCA [Musliner et al 1993] SSense-Act + Plan

32 3 Layered Architectures Deliberative layer: plan, reasoning, deliberation Esecutive layer: execution monitoring, scheduling, sequencing, dispatching, recovery, synchronization, etc., etc. Functional layer: specialized controllers, o perceptive p systems, sensory-motor oy oo loops, reactive behaviors

33 3T Architecture: ATLANTIS

34 3T Architecture LAAS architecture: Three Layers: 1. Deliberative (temporal planner) 2. Executive (PRS) 3. Functional (GENOME) Rover Control

35 RHINO Architecture Robotic tour guide - Bonn Science museum (1995); MINERVA Atlanta (1998) 3T mobile robot: 1. Functional: Mapping, Localizzation, Obstacle Avoidance 2. Executive: Sequencer, monitor 3. Deliberative: Task Planner (tour planner) RHINO Architetture

36 Xavier Architecture (1995) -Low-level control: high resolution, high frequency h l l l l l -High-level control: low resolution (abstract), low frequency

37 DS1 (Remote Agent) Mission: i testingti new technologies for the New Millennium Program (and observe Borrelly comet) First autonomous spacecraft Planner and smart executive system (RAX: Remote Agent Experiment). Planning, scheduling, adaptive execution, diagnosis, recovery.

38 3T: REX - Remote Agent: - Three Layers: - Mission Manager, Temporal planning and scheduling - Execution Monitoring/Dispatching, Mode Identification (Diagnosis) and Recovery (MIR) - Reflex control, sense (real-time)

39 3T: REX

40 3T Architectures Problems with 3T: Modular architectures but Heterogeneous (different models) Abstraction ti level l == control level: l - HL abstract deliberation (task and mission planning) - LL reaction (implicit model, no flexible) Interaction deliberative-reactive? - Plan-Exec interaction - Replanning - Several exec-monitor-control loops Ad hoc executive system (when too complex, only sequencer and dispatcher)

41 Executive Layer: the key stone

42 Executive Control: RAPs (Firby 1987) Balancing reactive and goal-orientedoriented behaviors

43 RAPs (Firby 1987)

44 Executive Layer: PRS (Ingrand 1990) BDI architecture: 1. Beliefs: DB 2. Desires: Goals 3. Intentions: Goal-oriented procedures

45 Task Control Architecture (Simmons 1994) TAC architecture: Interprocess comm Task decomposition and temporal constraints Resource allocation Execution monitoring Exception Handling Executive orchestrates multiple Executive orchestrates multiple perceptive, reactive, and deliberative processes

46 Task Control Architecture

47 Task Control Architecture

48 Task Control Architecture

49 Test-Action Pair CIRCA (Musliner 1993)

50 CIRCA (Musliner 1993)

51 2T Hybrid Architectures 3T Architectures: HL abstract deliberation (smart), LL execution/reaction (stupid) [Bonasso et al. 1998] 2T Architectures: Execution and deliberation distributed at all control levels [Claraty 2000] Claraty Architecture (from JPL) 3 layers

52 Claraty Architecture (Volpe & Nessans 2000)

53 CASPER (Estlin et al 2000) Continuous Planning and execution in CASPER (JPL)

54 IDEA: Muscettola (2002) Model-based executive control through reactive planning: - Reactive Planning and scheduling (executive layer) - Distributed components interacting with sense-plan-act act cycles Planning and scheling as an executive/reactive engine

55 Cognitive Architectures - Autonomous robot and flexible behavior (Field Robotics) - Interaction, Interpretation, Continous learning (Social Robotics) Robotic Architetture as Cognitive Architectures: -Sensorfusion - Reasoning - Deliberation -Learning - Perception/Recognition iti and Perception/Action - Attention and Executive Control - Sensory-motor coordination (synergies) - Motivations, emotions - Human-robot interaction (development robotics)

56 Architetture Cognitive: Alami (2006)

57 Prime Architetture Cognitive: ACT-R (1993) Plausibilità Cognitiva: Teoria della cognizione e verifica sperimentale Embodied Agent: utilizzata per controllo di robots. Due memorie: proceduarale e associativa Contina scelta della produzione più adatta al contesto t e al goal

58 Prime Architetture Cognitive: SOAR (1987) Plausibilità Cognitiva; Regole di produzione; tutti i task sono goal- oriented Ciclo di selezione e applicazione di operatori; se enpasse nuovo goal

59 Architetture Cognitive: ICARUS (2004) ICARUS è un rule-based system, include memorie per concetti è skills e memorie a breve termine per la percezione. Esecuzione; memoria; percezione

60 Architetture Cognitive: Alami (2006)

61 Architetture Cognitive: Alami (2006)

62 Functional Functionalities Avoidance Mapping Localization Navigation Perception/recognition iti object,situation,place,... Object manipulation Visual perception Human-robot interaction Esempio: GENOME functional Livello layer funzionale in

63 Functional level: Mission MER (2003) Twin Robots (Spirit e Opportunity) on Mars Instruments: 3 cameras (PAN, MI, Nav), 3 spectrometres (TES,MB,APXS), 1 drill. Control: supervision, Autonomous navigation (tested)

64 Functional Level: Mission MER (2003) Local Mapping Semiautonomous Navigazione: stereo camera, 3D mapping, obstacle avoidance, pathplanning, visual odometry Opportunity 141 metri nel sol 82 (more than Sujourner!) Real Data Opportunity Dati reali Opportunity Percorso di Spirit Sim. Ricostruzione Opportunity o Opportunity

65 Mars Mission Lab (2011) Navigation: path planning horizon 50 m CLARATY architecture: Animation Credit: NASA/JPL-Caltech/CMU

66 Deliberative layer Meccanismi di decisione: Task planning Reactive/Dynamic Planning Path Planning Temporal, dynamic reasoning,etc. Environment models (maps, constraints, cause-effects, effects dynamics, etc.) Robot Models (sensor/actuator) Decision i Models (utility, costs etc.) InteractionModels(HRI) Esempio: Timeline-based Planning

67 Deliberative: Mission MER (2003) Off-line planning 240 researchers (geologists, engineers, biologists, etc.) ask for experiments and observations. Night planning for the next sol No Planning on-bord

68 Executive Layer Between functional and deliberative: Planned activities Sensory-motor coordination Deliberative-reactive coordination Execution monitoring Error detection, diagnosis and recover Replanning Model Model Model SLAM PTU Navigation Execution Monitoring CAMERA 3D

69 Functional, Deliberative and Executive layers are also the architecture of the lectures: Functional layer: Mobile robotics and probabilistic robotics (mapping e localizzation, navigation, exploration, etc.). Bayesian a models, bayesian a filters Executive layer: Execution monitoring and dynamic planning; cognitive control and attentional systems. Temporal models, automata, cognitive models etc. Deliberative layer: Planning and scheduling; planning and execution; decision theoric planning; pa reinforcement e learning. Temporal models, markov models, etc.. Issues