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  1. Plan-Based Robot Control • Joachim Hertzberg

  2. Contents • What is Plan-Based Robot Control? • Examples • Research Areas • Conclusion

  3. 1. • What is Plan-Based Robot Control? • Examples • Research Areas • Conclusion

  4. Planning in Autonomous Robotics/AI Robotics [Murphy] The plan is that part of the robot’s program,whose future execution the robot reasons about explicitly[D. McDermott, 1992] …and typically does so on-line and on-board. [JH, 2003] Robot Plans – Classical & Non-Classical • Types of Planning in Automation-Style Robotics • Path Planning • Trajectory Planning • Motion Planning • Inverse Kinematics • Control Theory • Scheduling • …

  5. Performance optimization (plan generation, plan patching) • Scheduling • Failure recovery (explanation-based diagnosis) • High-level learning (symbolic learning techniques) • Communication with users or fellow robots * • Engineering/structuring of the control software Plans serve as abstract, high-granular descriptions of robot action * http://www.AgenTec.de The Application Impact of AI Robotics & Robot Plans • Increase the automation degree in poorly controlled environments • Populated areas (e.g., supermarket cleaning, airport courier DTVs) • Areas with independent processes going on (e.g., multi-agent domains) • Areas with adversarial processes going on (e.g., military/security applications) • Areas with lack of detailed domain knowledge (e.g., inspection, space, rescue) NASA Mars Exploration Rover Mission

  6. 2. • What is Plan-Based Robot Control? • Examples • Research Areas • Conclusion

  7. Registration Pose planning View Pose Planning in 3D SLAM • 3D SLAM means: • Registration of scans from different poses; • planning and collision-free execution • of feasible trajectories through known open space • to poses of locally maximal expected info. gain [Surmann&al., ISR-2001] [Nüchter&al., ICAR-2003]http://www.ais.fhg.de/ARC/3D/

  8. Digression: Virtual Flight through the 3D Model Voxels coloured bylaser remission values

  9. Monitoring Intricate Closed-Loop Motion Application Task Control closed-loop turn or turn&climb manoeuvres of an articulated 21 DOF multi-segement robot in confined space (sewer pipe junctions) Technical Problem Provide robust on-line error diagnosis and recovery in case the manoeuvre fails(and detect failure in the first place) Solution idea Represent turning manoeuvres as (carefully handcrafted!) HTNs with alternative expansions and sense-able operator preconditions and postconditions [Streich et al., 2000; Robotik-2000][Rome et al., 1999; J.Urban Water]

  10. Inspektions-Protokoll Interface Mission-Level User Interaction • Sewer map • Sensor modules • Inspection tasks • Proceed along path p; thereby:with localization do: • Note house inlets • Note changes in pipe diameter • Take photos of grown-in tree roots [Streich et al., Robotik-2000]

  11. Performance Optimization in Indoor Navigation Application Task (RHINO robot): Indoor navigation using a given map Technical Problem Optimize expected travel time by context-dependent changes of navigation control Solution Idea Represent mid-level navigation actions as HTNs with alternative expansions and sense-able operator preconditions and postconditions(e.g. SET-TARGET(x,y,d), TURN-TO(x,y), MOVE-FWD(d); APPROACH-POINT(x,y,d), MDPGOTO(x,y)) [Belker et al., 2003; ICRA]

  12. dx,dy in {1m, 2m, 4m} dist. d e {0.5m, 1m, 2m} APPROACH-POINT(dx,dy,d) SET-TARGET(dx,dy,d) TURN-TO(x,y) SET-TARGET(dx,dy,d) MOVE-BWD(30cm) SET-TARGET(dx,dy,d) TURN-TO(x,y) SET-TARGET(dx,dy,d) TURN-TO-FREE() MOVE-FWD(50cm) Operator Expansion Hierarchy MDPGOTO(x,y)

  13. APPROACH-POINT(2,pending, 1434,1009,1m) …(1,expanded,…) SET-TARGET(3,pending, 1434,1009,1m) …(2,expanded,…) …(1,…) APPROACH-POINT(4,pending, 1477,1158,1m) …(1,…) SET-TARGET(5,pending, 1477,1158,1m) …(4, expanded,…)) …(1,…) MOVE-BACKWARD(6,pending, 30cm) SET-TARGET(7,pending, 1477,1158,1m) …(4,…) …(1,…) Plan Execution Example MDPGOTO(1,pending, 1521,1563) Assume executionwith success No Admissible Trajectory!! etc .…

  14. Solution 2 Project results of different HTN expansions; acquire models for expected execution times by learning Learned Prediction Rule (expl.) if path curvature < 1.05 and not crosses-door and path length ≥ 110 and path length < 130then duration = 1/23.99 * path length Gain: ≈ 40% Expansion Selection Alternatives Solution 1 Hand-code context-dependencies(e.g., “If space gets narrow, set target points closer”). Gain: ≈ 30%

  15. By-Products of the Plan-Based Representation By-Product I Transparent behavior in coping with navigation set-backs(e.g., blocked pathways, occlusion of intermediate target points) By-Product II Rational reconstruction of part of the navigation system, which allows for much higher code transparency

  16. SHAKEY, 1969 3. • What is Plan-Based Robot Control? • Examples • Research Areas • Conclusion

  17. a? dv/dt? Integration/ Robot Control Architectures • “Eternal Constraints” • Never run down your batteries! • Give priority to directors’ missions! • Schedule for June 16, 2003 • Update map of 1st floor • Deliver mail at 10:00 • Pick up visitor at the gate at 13:30 • Recent Information • Elevator maintenance 8:00–10:00 • Secretary is on vacation The modern solution: Hybrid Architectures [Murphy, 2000]

  18. Turning Sensor Signals into Symbols Potentially very rich sensor information is available for mobile robots • To use it in plan-based robot control, • Symbolic facts / fact hypotheses need to be extracted from that; • on-line update of knowledge bases needs to be performed; • sensor readings may be unreliable, knowledge may come with different time stamps; • planning must work on possibly inconsistent/para-consistent knowledge bases

  19. Topics List • Robot plan ontologies • Planning under uncertainty • Planning under inconsistency • Anytime planning for robot control • Practical knowledge base update • Plan execution monitoring • Symbol grounding / object anchoring • Learning for robot plan optimization • Learning for robot plan ontology optimization • … Whatever it is,remember you are dealing with complete robot systems(mechanics, electronics, sensors, control theory, …)

  20. 4. • What is Plan-Based Robot Control? • Examples • Research Areas • Conclusion

  21. Some Sources of Further Information • Beetz/ Hertzberg/ Ghallab/ Pollack (eds.):Advances in Plan-Based Control of Robotic AgentsSpringer (LNAI vol. 2466), 2002 • Robin Murphy:Introduction to AI RoboticsMIT Press, 2000 • Stay tuned to the NASA Mars Exploration Rover Missionafter landing in early January 2004

  22. Conclusion: Robot Plans … • … are control program bits that the robot is supposed to reason about • … are but one small part of an overall robot system • … may come in different syntactic forms and on different granularity levels • … may serve many different purposes, such as • performance optimization, • failure recovery, • learning, and more • … have been successful on a number of experimental or prototype robot systems • … have not yet been used in mass-market AI-type robots (“Service Robots”) • … still involve some basic research problems (integration, symbol grounding) • … are an enabling technology for building AI robot applications. Plan-based robot control has to offer an enabling technology for increasing the automation degree in poorly controlled environments

  23. The End • What is Plan-Based Robot Control? • Examples • Research Areas • Conclusion • The End

  24. Scan Matching • Algorithm ICP (Besl, McKay & al., 1992) • Our variant: • on-line, on-board • registers 2 scans (181x256) in <1.4 sec. • Robot pose correction as a by-product • Registration of multiple scans From Point Clouds to a 3D Geometry Model: Registration AIS 3D-Laser Scanner