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Plan-Based Robot Control Joachim Hertzberg Contents What is Plan-Based Robot Control? Examples Research Areas Conclusion 1. What is Plan-Based Robot Control? Examples Research Areas Conclusion Planning in Autonomous Robotics/AI Robotics [Murphy]

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plan based robot control
Plan-Based Robot Control
  • Joachim Hertzberg
contents
Contents
  • What is Plan-Based Robot Control?
  • Examples
  • Research Areas
  • Conclusion
slide3
1.
  • What is Plan-Based Robot Control?
  • Examples
  • Research Areas
  • Conclusion
robot plans classical non classical

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
the application impact of ai robotics robot plans

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

slide6
2.
  • What is Plan-Based Robot Control?
  • Examples
  • Research Areas
  • Conclusion
view pose planning in 3d slam

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/

digression virtual flight through the 3d model
Digression: Virtual Flight through the 3D Model

Voxels coloured bylaser remission values

monitoring intricate closed loop motion
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]

mission level user interaction

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]

performance optimization in indoor navigation
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]

operator expansion hierarchy

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)

plan execution example

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 .…

expansion selection alternatives

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%

by products of the plan based representation
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

slide16

SHAKEY, 1969

3.
  • What is Plan-Based Robot Control?
  • Examples
  • Research Areas
  • Conclusion
integration robot control architectures

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]

turning sensor signals into symbols
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
topics list
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, …)

slide20
4.
  • What is Plan-Based Robot Control?
  • Examples
  • Research Areas
  • Conclusion
some sources of further information
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
conclusion robot plans
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

the end
The End
  • What is Plan-Based Robot Control?
  • Examples
  • Research Areas
  • Conclusion
  • The End
from point clouds to a 3d geometry model registration

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