1. Subsuption Architecture
Robotic Course presentation
Amirali Salehi Abari
I want to see if these notes will also be displayed, or not.
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2. Rodney Brooks-1986 MIT
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3. What is Subsuption Architecture?
It is a architecture for controlling mobile robot and It is behaviour Model.layers of control system are built to let the robot operate at increasing level of competence.
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4. Architecture Before SA
The traditional architecture used for mobile robot control systems -- decomposition into functional modules as follows:
sensors
perception
modeling
planning
task
execution
motor
control
effectors
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5. Dominant approach to building mobile robots: sense-plan-act cycle
Sense-plan-act (SPA)
Dominant approach to building mobile robots: sense-plan-act cycle
Sense: Sensors determine the values of state variables .
Plan: Modeling the world, finding a plan to satisfy the goal.
Act: Execute the plan (its first action), then go back to the first step.
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6. Problem of SPA Planning is so hard. World modelling is difficult
An instance of each piece must be built in order to run robot at all.
Changing in each piece is difficult.
It is not Robust.
It is difficult to be distributed.
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7. Brook ‘s Requirment Multiple goals Multiple sensors Robustness
Conflicting
Importance(priority)
Multiple sensors
Robustness
Sensor fail
Environment changes
Additivity
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8. A Subsumption Architecture
Level 4
Level 3
Level 2
Level 1
Level 0
sensors
effectors
The levels/layers are added from the bottom up, and remain.
Higher-level layers subsume lower-level layers when they
want to take control.
A level can potentially communicate with any other level.
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9. Subsumption Architecture
reason about behavior of objects
plan changes to the world
identify objects
monitor changes
build maps
explore
wander
avoid objects
sensors
effectors
Each behavior may be simple or complex.
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10. Features of a Subsumption Architecture
Layers of control allow the robot to operate at increasing levels of competence. The control system can be constructed one layer at a time, beginning with the lowest level.
Each layer is composed of asynchronous modules that communicate over low-bandwidth channels.
lowest layers handle most basic tasks
Lower layers represent less abstract behaviours, e.g. obstacle avoidance in physically embodied agents. And Higher layers represent more abstract ideas, e.g. move to the other side of the room
Higher levels can subsume the roles of lower levels by inhibiting their inputs or suppressing their outputs.
If higher levels fail, lower levels continue to function. This provides robustness.
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11. Features of a Subsumption Architecture
Different layers can work on separate goals concurrently. As you go higher in levels, computation time is slower. Only one layer selects the outputs at a time.
Each module is a separate piece of code, called a behavior-producing module (BPM), which may run on its own processor. The BPMs produce the observable behaviors of the robot. In Brooks’ original conception, a BPM was implemented as a finite-state machine.(FSM)
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12. A Finite-State Machine (FSM)
Start here
Condition for state transition
Outputs during transition
State 1
Next-state transition
State 2
State 3
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13. Augmented Finite State machine
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14. AFSM Feature
Each behavior is represented as an augmented finite state machine (AFSMs)
Stimulus (input) or response (output) can be inhibited or suppressed by other active behaviors.
An AFSM can be in one state at a time, can receive one or more inputs, and send one or more outputs
AFSMs are connected communication wires, which pass input and output messages between them; only the last message is kept
AFSMs run asynchronously
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15. Example Modules In Level 0 Control System
collide
HALT
sonar
motor
select
default
move
MOVE
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16. Example Modules In Level 0,1 Control System
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17. Example Modules In Level 0,1,2 Control System
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18. Advantage Of SA could achieve several goals simultaneously
might still work when one of the sensors fails
seems to allow the addition of new capabilities
No high-level planning is required
No need to know the environment
Robust, graceful degradation
Very simple to implement
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19. Disadvantage of SA
difficult to decide to which level a behavior belongs
Effective agents be built with a small number of layers, about 10 maximum, but it is much harder to build agents that contain many layers. The dynamics of the interactions between the different behaviours become too complex to understand.
It is difficult to see how purely reactive agents can be designed to learn from their experience, and improve their performance over time
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20. Thank you!
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