Transcript יכולות נבונות Telerobotic Features

Advanced Multi-Agent-System
for Security applications
Dr. Reuven Granot
Faculty of Science and Scientific Education
University of Haifa, Israel
[email protected]
Robotic activities at University of
Haifa
• The new Faculty of Science and Scientific Education’s
mission is focused toward interdisciplinary research and
education.
• The robotic activities have their background in the initiative of the
Research & Technology Unit at MAFAT Israel MoD were I served in
the last decade as Scientific Deputy.
• We have concentrated interest and research in Multi – Agent
Supervised Autonomous Systems (Tele robotics), while
continuing steady support of the Manual Remote operations in
different combat environments.
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Overview
• The Tele-robotics paradigm.
• The Control Agent as the implementation of the
relevant behavior.
• Human Robot Interaction.
• JAUS and Real time Control System
Architectures.
• Evaluation of concepts using Small Size Scaled
Model.
• Video demonstration.
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The Need of Unmanned Systems
Regarding Defense and Security the
need is well recognized to perform
tasks that are:
• DDD
– Dull
– Dirty
– Dangerous
• Distant – at different scale
– Macro: space,
– Micro: telesurgery, micro and
nano devices
All these applications require an effective interface between the
machine and a human in charge of operating/ commanding the
machine.
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The Tele-robotics paradigm
Telerobotics is a form of Supervised Autonomous Control.
A machine can be distantly operated by:
• continuous control: the HO is responsible to
continuously supply the robot all the needed
control commands.
• a coherent cooperation between man and
machine, which is known to be a hard task.
Supervision and intervention by a human would provide the
advantages of on-line fault correction and debugging, and
would relax the amount of structure needed in the
environment, since a human supervisor could anticipate and
account
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2006situations.
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Remote Controlled vehicles in combat environment
 RC is still preferred by designers
o Simple, but not practical for combat environment because
the human operator:
is very much dependent upon the controlled process
 needs long readjustment time to switch between the
controlled and the local (combat) environment.
 The state of the art of the current technology has not yet
solved the problem of controlling complex tasks autonomously
in unexpected contingent environments.
o dealing with unexpected contingent events remains to be a
major problem of robotics.
 Consequence: A human operator should be able to interfere:
remains at least in the supervisory loop.
Juneneeded
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The
Human
Supervised Autonomous
Why Security Systems should make use of
the Telerobotic paradigm
• Require
–
–
–
–
Reduced number of human operators.
HO should control simultaneously several systems.
High flexibility and factor of surprise.
HO should be capable to deal with other duties in somehow
relaxed mode of operation.
• Means:
– Distributed systems.
– Coherent collaboration of human intelligence with machine
superior capabilities.
– Make the machine an agent in human operator’s
service.
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The spectrum of control modes.
A telerobot can use:
• traded control:
control is or at
operator or at the
autonomous subsystem.
• shared control: the
instructions given by
HO and by the robot
are combined.
• strict supervisory
control: the HO
instructs the robot,
then observes its
autonomous actions.
Solid line= major loops are closed through computer, minor loops through human.
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Human Robot Interaction
• In supervised autonomously controlled equipment,
a human operator generates tasks, and a computer
autonomously closes some of the controlled loops.
• Control bandwidth
– Robot SW: high
– Human response: slow
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The Agent
• An agent is a computer system capable of autonomous
action in some environments.
• A general way in which the term agent is used is to denote
a hardware or software-based computer system that enjoys
the following properties:
– autonomy: agents operate without the direct intervention of
humans or others, and have some kind of control over their actions
and internal state;
– social ability: agents interact with other agents (and possibly
humans) via some kind of agent-communication language;
– reactivity: agents perceive their environment, (which may be the
physical world, a user via a graphical user interface, or a collection
of other agents), and respond in a timely fashion to changes that
occur in it;
– pro-activeness: agents do not simply act in response to their
environment; they are able to exhibit goal-directed behavior by
taking the initiative.
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Agents are not Objects
• Agents may act inside the robot software to implement
behaviors:
 Feedback controllers
 Control subassemblies
 Perform Local Goals/ tasks
• Differ from Objects
– autonomous, reactive and pro-active
– encapsulate some state,
– are more than expert systems
• are situated in their environment and take action instead of
just advising to do so.
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The Control Agent
• The agent is a control subassembly.
• It may be built upon a primitive task or composed
of an assembly of subordinate agents.
• The agent hierarchy for a specific task is preplanned or defined by the human operator as part
of the preparation for execution of the task.
• The final sequence of operation is deducted from
the hierarchy or negotiated between agents in the
hierarchy.
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Agent control loop
• agent starts in some initial internal state i0 .
• observes its environment state e, and generates a
percept see(e).
• internal state of the agent is then updated via next
function, becoming next_(i0, see(e)).
• the action selected by agent is
action (next(i0, see(e))))
This action is then performed.
• Goto (2).
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Human Operator
• Monitors the activities and the performance of the assembly of
agents.
• Responsible for the completion of the major task (global goal)
– may interfere by sending change orders.
• emergent (executed immediately)
• “as is ordered” or
• normal
– checked by the interface agent
– which negotiates execution with other agents in order to
optimize execution performance
– Conflict resolution algorithm
• defined as default, or
• defined by the human operator in its change order or
• suggested to the operator by a simplified decision support
algorithm.
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Man Machine Interface is
still one of the most
recognized technology
gaps/ challenges of semi
autonomous systems.
Intelligent Control will be achieved using Intelligent Agents.
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Interface Agent
• A software entity, which is capable to represent
the human in the computer SW environment.
• It acts on behalf of the human
• Follows rules and has a well defined expected
attitude/ action.
• May be instructed on the fly and may receive
during mission updated commands from the
human operator.
We need to build agents in order to carry out the
tasks, without the need to tell the agents how to
perform these tasks.
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Task-level supervisory control system block diagram.
formatted
outputs
Controlling agent
desired
tasks
Task level
controller
control
signals Robot hardware
raw
robot
outputs
• An agent can be considered as a control subassembly,
also called behavior.
• The feedback is given to the agent in both processed and raw
form.
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RCS
JAUS
Embeds a hierarchy of agents within
a hierarchy of organizational units:
Intelligent Nodes or RCS_Nodes.
Platoon
Commander
Squad
Commander
Squad
Commander
Vehicle
Commander
Squad
Commander
Vehicle
Commander
Vehicle
Commander
Squad
Commander
Vehicle
Commander
From M. W. Torrie
A hierarchy of Commanders
different resolution in space and time
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RCS_Node
Sensory
Processing
Update
Predicted
Input
Observed
Input
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Plan
Results
Situation
Evaluation
Perceived
Objects &
Events
Commanded
Task (Goal)
Value
Judgment
World
Modeling
Knowledge
Database
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Plan
Behavior
Generation
State
Commanded
Actions
(Subgoals)
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Agents in Behavior
Generation hierarchy
• Tasks are decomposed and
assigned in a command
chain.
• Actions are coordinated
•
Resources are allocated as
plan approved.
•
Tasks achievements are
monitored (VJ)
• Execution in parallel
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Evaluation of concept
• As an emerging scientific field, the field of
robotics (like AI) lacks the metrics and
quantifiable measures of performance.
• Evaluation is done against common sense
and qualitative experimental results.
• the legitimacy of transfer of conclusions
over different scale applications or different
implementations remains to be decided by
specific designs.
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Small Size Scaled Model
• The implementation differs by mechanical,
perceptual and control elements from the full scale
application.
• It still may help to identify unusual situations
which the software agent must be capable to deal
with.
• Full scale machines may be tested only at field
ranges, which are time consuming and very
expensive.
• A small scale model may be tested in office
environment, enabling the software developers to
shorten test cycles by orders of magnitude.
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D9 Bulldozer
• A good starting project:
– earthmoving tasks are loosely coupled with
locomotion tasks.
– earthmoving tasks are not really simple and
– locomotion tasks are not really complicated.
• The operator has very limited
information about his surroundings
or machine performance.
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Expected situations
• The bulldozer moves forward placing the blade too low
– The human decides: the blade should be placed higher
 Command issued: “lift the blade”.
• experiencing too much power to enable earth moving
forward
– the human operator would prefer to withdraw and
attack the soil from a new position behind
– the human operator is distant
– the bulldozer is “close” to the ditch;
> a better practice would be to first complete the maneuver.
 Bulldozer using Fuzzy Control decides to perform the
better practice and withdraws only after the maneuver is
completed.
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The Model
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Drawbacks
• DC motors are of relatively weak power and small
dimensions
– which reduce our choice of suitable sensors.
– therefore, we implemented
• simulated beacon
• CMUcam placed above - is a simulation of the
"Flying Eye" concept of FCS
– We were unable to control the speed of the vehicle.
• We had to restrict our testing to control
– the vehicle rotation around a perpendicular axis
– to manipulate the raising of the blade.
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autonomous-bulldozer\robot.WMV
4 min
autonomous-bulldozer\robot.mpg
3 min
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Conclusions
• Security systems should use the advantages of the
Telerobotic paradigm in order to perform complex
tasks with few operators.
• Agents are implementations of behaviors.
• Behavior based Architectures are better
implemented using the Multi Agent technology.
• Human Machine Interaction is better implemented
through the Interface Agent.
• Machine Intelligence may be achieved
implementing agents into the JAUS/ RCS Model
Architecture.
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Some References
• NATO Core Group in Robotics (members) 2005: Bridging the Gap in
military Robotics (to be published as NATO document)
www.fgan.de/~natoeuro/EuropeanRobotics-Publication.pdf
• Sheridan, T.B., Telerobotics, Automation, and Human Supervisory Control,
MIT Press, 1992
• Granot R, Agent based Human Robot Interaction. at IPMM 2005,
Monterey, California, 19-25 July 2005
• Granot, R., Feldman, M., 2004: "Agent based Human Robot Interaction of a
combat bulldozer." Unmanned Ground Vehicle Technology IV, at SPIE
Defense & Security Symposium 2004 (formerly AeroSense) 12-16 April
2004, Gaylord Palms Resort and Convention Center Orlando, Florida USA,
paper number 5422-25
• Granot, R., 2002: "Architecture for Human Supervised Autonomously
Controlled Off-road Equipment. Automation Technology for Off-road
Equipment", ASAE, Chicago, Il, USA, July 26-28, 2002, p24
• Meystael M. A. and Albus, S. J. "Intelligent Systems. Architecture, Design,
and Control", John Wiley & Sons Inc., 2002
• Michael Wooldridge, "Intelligent Agents: Theory and Practice"
http://www.csc.liv.ac.uk/~mjw/pubs/ker95/
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Contact
Dr. Reuven Granot
• [email protected]
• [email protected]
University of Haifa
Faculty of Science and Scientific Education
Mount Carmel Haifa 31905 ISRAEL
Office +972 4-828-8422
• http://math.haifa.ac.il/robotics
cellular +972 52 341-0193
This presentation is downloadable from
http://math.haifa.ac.il/robotics/Projects/MyPapers/RISE2006.ppt
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