1. Middle East Technical University
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Subsumption Architecture
Case 1: Controlling a wheeled robot
Case 2: Controlling a hexapod robot
Erol Sahin
Dept of Computer Eng.
Middle East Technical University
Ankara, TURKEY

2. Subsumption Architecture
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Subsumption Architecture
Proposed by R. Brooks of MIT AI Lab.
Created a major paradigm shift
“A Robust Layered Control System for a Mobile Robot”, R.A. Brooks, IEEE Journal of Robotics and Automation RA-2, 14-23, 1986.
“A Robot that walks: Emergent Behaviors from a carefully evolved network”, R.A, Brooks, Neural Computation 1 (2), , 1989.
Adapted from Ö. Alan’s slides 2

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Traditional model
“The traditional model where cognition mediates between perceptions and plans of actions.”

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New model
“The new model where the perceptual and action subsystems are all there really is. Cognition is only in the eye of an observer.”

5. Traditional decomposition
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Traditional decomposition
Vertical decomposition of modules.
What should be the output of the perception module?

6. New decomposition Horizontal decomposition
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New decomposition
Horizontal decomposition
Each module is directly coupled to the world

7. Layered (incremental) control
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Layered (incremental) control
“Control is layered with higher level layers subsuming the roles of lower layers when they wish to take control.”
“The system can be partitioned at any level, and the layers below form a complete operational control system.”
Subsume: To classify, include, or incorporate in a more comprehensive category or under a general principle

8. Level of competences for a mobile robot
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Level of competences for a mobile robot
Level 0: Avoid contact with objects (whether the objects move or are stationary).
Level 1: Wander aimlessly without hitting things.
Level 2: “Explore” the world by seeing places in the distance which look reachable and heading for them.
Level 3: Build a map of the environment and plan routes from one place to another.
Level 4: Notice changes in the “static” environment.
Level 5: Reason about the world in terms of identifiable objects and perform tasks related to certain objects.
Level 6: Formulate and execute plans which involve changing the state of the world in a desirable way.
Level 7: Reason about the behavior of objects in the world and modify plans accordingly.
“A Robust Layered Control System for a Mobile Robot”, R.A. Brooks, IEEE Journal of Robotics and Automation RA-2, , 1986.

9. Modules A module has input and output lines, also called as “wires”.
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Modules S 10 1 3
Inputs
Suppressor
Outputs
Inhibitor
Reset
A module has input and output lines, also called as “wires”.
Input lines have single element buffers. The most recently arrived message is always available for inspection.
Input signals can be suppressed and replace with the suppressing signal.
Outputs are sent as messages over the wires.
Output signals can be inhibited.
A module can also be reset to the initial state.

10. Augmented Finite State Machines
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Augmented Finite State Machines
Finite
State
Machine S I
Registers
Timers
FSM
The arrival of a message, or the expiration of a timer, can trigger a change in the state of FSM. 10

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Modules S 10 1 3
Inputs
Suppressor
Outputs
Inhibitor
Reset
Wait on some event, dispatch to another state based on predicate registers
Compute a function of registers
Sensors deposit their values to certain registers
Certain outputs direct commands to actuators.
Adapted from Ö. Alan’s slides 11

12. Level 0: Avoid contact with objects
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Level 0: Avoid contact with objects
sonar: takes a vector of sonar readings, filters them and produces a robot- centered map of obstacles.
collide: monitors the map, and if it detects objects ahead, sends halt signal to the forward module. If the robot is already stationary, the halt message is lost.
feelforce: uses the map of obstacles to generate a single repulsive force.
runaway: Monitors the “force” and sends a heading direction to the turn module to avoid the obstacles.
turn and forward: uses the incoming heading and halt messages to control the robot’s motors. Encoder values are used as feedback in this control.

13. Level 1: Wander aimlessly
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Level 1: Wander aimlessly
The level 0 control system is augmented with the level 1 system.

14. Level 1: Wander aimlessly
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Level 1: Wander aimlessly
wander: generates a new heading for the robot every 10 seconds or so.
avoid: Takes the force and the [desired] heading direction to generate a new heading direction that subsumes the one generated by the runaway module.

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Level 1: Explore
The level 0 and 1 control systems augmented with the level 2 system.

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Level 1: Explore
Status: monitors turn and forward and returns whether the robot is busy or not.
Whenlook: monitors status and when “not busy” for a few seconds, it decides to look for corridors.
Look: initiates vision processing and waits for a candidate, and passes an acceptable one to pathplan.
Stereo: generates corridor candidates from visual data.
Integrate: accumulates reports of motions from the status module.
Pathplan: takes a goal specification and attempts to reach that goal by suppressing heading to avoid module. When the position of the robot is close to the target [through integrate module], it terminates.

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Week 4
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18. Experiments Conducted in simulation.
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Experiments
Conducted in simulation.
The simulated robot receives 12 sonar readings. Some sonar beams glance off the walls and do not return within a certain time.

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Level 0 and 1 in action
Under levels 0 and 1, the robot wanders around aimlessly. It does not hit obstacles.

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Level 0, 1 and 2, in action
With level 2 control, the robot tries to achieve commanded goals. The nominal goals are the two straight lines.
After reaching the second goal, since there are no new goals forthcoming, the robot reverts to aimless level 1 behavior.

21. Controlling a hexapod robot
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Controlling a hexapod robot
Control mechanism for a six-legged robot capable of walking on rough terrain and following people.
Incremental building of complex systems integrating sensory inputs and actuator outputs.
Emergent complex behaviors from simple reflexes with little centralized control.
A base for building automatically massive networks for complex tasks
“A Robot that walks: Emergent Behaviors from a carefully evolved network”, R.A, Brooks, Neural Computation 1 (2), , 1989.
Adapted from Ö. Alan’s slides 21

22. Robot Description (Genghis)‏
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Robot Description (Genghis)‏
Hexapod
35 cm long, 25 cm leg span, 1 kg weight
Legs 2 degrees of freedom (up/down and forward/backward)‏
Two front whiskers
Two inclinometer
Pyroelectric infra-red sensors
4 onboard 8 bit micro-processors
62.5 Kbaud token ring, 1KB Ram, 10 KB EPROM
3 silver zinc batteries
Adapted from Ö. Alan’s slides
Week 4 22

23. Behaviors Stand up Simple walk Force balancing Leg lifting Whiskers
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Behaviors
Stand up
Simple walk
Force balancing
Leg lifting
Whiskers
Pitch stabilization
Prowling
Steered Prowling
Adapted from Ö. Alan’s slides
Week 4
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24. Network of AFSM 16/10/07 alpha balance walk feeler IR prowl steer
leg
down
beta
pos
alpha
alpha balance
alpha advance s
Up leg
trigger
walk
force
balance d
collide
feeler
For/back
pitch i IR
sensors
prowl
steer

25. Stand-up   The robot should stand up when its turned on.
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Stand-up
The robot should stand up when its turned on.
Six copies of the following control: one for each leg.
alpha pos ()– the forward/backward state of the leg
beta pos () – the up/down state of the leg
Initial values of and  sets the desired positions for the motors.
AFSMs of this behavior also report the most recent commanded position for their motor.  
alpha
pos
beta
Triangle indicates
connection to the
actuators
Adapted from Ö. Alan’s slides 25

26. Simple Walk A leg down machine for each leg
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Simple Walk
A leg down machine for each leg
Whenever the leg is not in the down position, write the appropriate beta pos register in order to set it down. 
leg
down
alpha
pos
beta
Adapted from Ö. Alan’s slides 26

27. Simple Walk (Cont’d)‏  Alpha balance machine
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Simple Walk (Cont’d)‏
Alpha balance machine
Sums alpha pos values
Legs straight as zero
Legs forward as positive
Legs backward as negative
Sends a single identical message to all alpha pos machines to drive the sum to zero
If one leg moves forward somehow, all legs will receive a series of messages to move backward slightly 
leg
down
beta
pos
alpha
alpha balance
Band indicates that only one copy of the AFSM exists.
Adapted from Ö. Alan’s slides 27

28. Simple Walk (Cont’d)‏  Alpha advance machine
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Simple Walk (Cont’d)‏
Alpha advance machine
If a leg is raised somehow (check beta pos), it reflexively swings forward.
Suppress alpha balance machine (for that particular leg only)‏
All other legs automatically swing back to compensate 
leg
down
beta
pos
alpha
alpha balance
alpha advance s
Adapted from Ö. Alan’s slides 28

29. Simple Walk (Cont’d)‏ Up leg trigger machine
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Simple Walk (Cont’d)‏
Up leg trigger machine
Issues command to lift a leg by suppressing leg down AFSM.
Three registers (monitor beta pos, a trigger message, initially constant lift message to beta pos)‏
leg
down
beta
pos
alpha
alpha balance
alpha advance s
Up leg
trigger
Adapted from Ö. Alan’s slides 29

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Simple Walk (Cont’d)‏
Walk: Send coordinated trigger signals to up leg trigger machines
Two versions
Alternating tripod
Standard back to front ripple gait
leg
down
beta
pos
alpha
alpha balance
alpha advance s
Up leg
trigger
walk
Adapted from Ö. Alan’s slides 30

31. Force Balancing Walking over obstacles requires some force sensing.
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Force Balancing
Walking over obstacles requires some force sensing.
If a leg is placed on an obstacle, it has to roll (or pitch) the body in order to reach the beta preset value, increasing the load (force) on the motor.
Beta force machine
Monitors the beta motor forces
Beta balance
Sends out lift up message whenever the force is too high
Zero move messages
Standard deviation of pitch inclinometer fell from 3.6 to 2.3 
Adapted from Ö. Alan’s slides 31

32. Force Balancing (Cont’d)‏
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Force Balancing (Cont’d)‏
leg
down
beta
pos
alpha
alpha balance
alpha advance s
Up leg
trigger
walk
force
balance d
Adapted from Ö. Alan’s slides 32

33. Leg Lifting  Alpha collide
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Leg Lifting
Alpha collide
Measures the force on the forward swing (alpha) motor
Writes the height register in the up leg trigger at a higher value 
leg
down
beta
pos
alpha
alpha balance
alpha advance s
Up leg
trigger
walk
force
balance d
collide
Adapted from Ö. Alan’s slides 33

34. Whiskers  Feeler Machine Monitor the two whiskers in the front
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Whiskers
Feeler Machine
Monitor the two whiskers in the front
Increase the lift of left and right front legs before collision. 
Two copies (one for each side)‏
leg
down
beta
pos
alpha
alpha balance
alpha advance s
Up leg
trigger
walk
force
balance d
collide
feeler
Adapted from Ö. Alan’s slides 34

35. Pitch Stabilization Forward/backward pitch
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Pitch Stabilization
Forward/backward pitch
Monitor high pitch conditions
Inhibit the local beta balance machine output in appropriate circumstances
leg
down
beta
pos
alpha
alpha balance
alpha advance s
Up leg
trigger
walk
force
balance d
collide
feeler
For/back
pitch i
Adapted from Ö. Alan’s slides 35

36. Prowling IR sensors machine Prowl machine
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Prowling
IR sensors machine
Monitors six forward looking infra red sensors
Sends an activity message to the prowl machine when it detects motion
Prowl machine
Inhibits the leg lifting trigger messages
prowl – to rove or go about stealthily, as in search of prey, something to steal, etc.
Adapted from Ö. Alan’s slides 36

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Prowling (Cont’d)‏
leg
down
beta
pos
alpha
alpha balance
alpha advance s
Up leg
trigger
walk
force
balance d
collide
feeler
For/back
pitch i IR
sensors
prowl
The robot sits still until something comes by, the it moves forward a little
Adapted from Ö. Alan’s slides 37

38. Steered Prowling Follow a moving person Steer machine
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Steered Prowling
Follow a moving person
Steer machine
Writes into a register in each alpha pos machine for legs on the side of the activation
Reduces leg’s backswing
Adapted from Ö. Alan’s slides 38

39. Steered Prowling (Cont’d)‏
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Steered Prowling (Cont’d)‏
leg
down
beta
pos
alpha
alpha balance
alpha advance s
Up leg
trigger
walk
force
balance d
collide
feeler
For/back
pitch i IR
sensors
prowl
steer
Adapted from Ö. Alan’s slides 39

40. Conclusion A distributed system can achieve robust walking behavior.
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Conclusion
A distributed system can achieve robust walking behavior.
Macro behavior is a result of interaction of independent micro behaviors.
Integration of higher level behaviors into a system which controls lower level behaviors is relatively easy.
Adapted from Ö. Alan’s slides 40

41. Summary Layers of abstract behaviors
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Summary
Layers of abstract behaviors
Subsumption or inhibition of lower layers
no replacement
Difficult to design
what is the emergent behavior
No world model
Ego-centric and distributed
Adapted from C. Messom’s slides 41