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Lecture #2, Sept 8, 2005 |
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RSS II |
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Una-May O’Reilly |
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Intuition of BB design with an example |
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Overview |
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Practicalities |
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What task is the robot doing? |
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Searching for pucks |
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When it finds one, pushes it to vicinity of the
light source, goes to find another |
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Avoids or escapes from encounters with other
objects |
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Task is decomposed as a set of simple behaviors
(algorithms connecting sensors to actuation) that, when acting together,
produce the overall activity |
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The robots in BSim are circular differential
drive robots with a bumper, two IR proximity sensors, two photo sensors and
wheel encoders. The photo and IR sensors face diagonally from the front of
the robot at 45 degree angles. |
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Each robot supports a simple, yet powerful,
behavior-based programming system which includes a set of primitive
behaviors and a priority list arbiter. |
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A robot's program is called a task. A task is a
prioritized list of behaviors which all simultaneously compete to control
the robot. |
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arbiter chooses which behavior is successful. You can program each robot by
configuring a set of behaviors, prioritizing the behaviors for the arbiter,
and then loading the behaviors into the robot. |
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Cruise: drives the wheels at constant
speeds. The behavior can try to drive the wheels at any speed, positive or
negative, but the robot speed will max out at +/- 255. |
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Home: tries to drive the robot toward a light
source. It uses a proportional controller to home on a light source
whenever the robot’s photo sensors see light. The robot homes on the light
by pivoting in the direction of the light and then moving forward a step.
The robot determines the direction to the light by calculating the
difference between the two photo sensor measurements.. |
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Avoid: Moves robot forward and left if the right
proximity sensor is on, or forward and right is the left proximity sensor
is on (if gain is positive). With a negative gain (in collection task) it
goes toward an obstacle (eg a puck or wall) |
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Escape:a ballistic behavior triggered whenever
the robot bumps into something. The behavior is performed in three steps:
backup for a specified amount of time, spin a certain angle, and go forward
for a specified amount of time. |
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Anti-Moth: a ballistic behavior that triggers
whenever the total light intensity measured by a photocell exceeds a
threshold |
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Dark-push: a ballistic behavior. It triggers
whenever the robot tries to push something when no light is visible. |
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There’s no explicit FindPuck behavior |
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No PushPuck behavior |
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No DropPuck behavior |
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These emerge from the interaction of the more
primitive behaviors |
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System behavior is not deterministic, but has
random components |
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Overall behavior is robust - ultimately collects
pucks |
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No representation of the world and no state |
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Contrast between good old fashioned Artificial
Intelligence (GOFAI) and behavior-based AI |
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GOFAI: Thought experiments on the nature of
“intelligence” in creatures with bodies |
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BB-AI draws inspiration from neurobiology,
ethology, psychophysics, and sociology |
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Simple creatures occupy very complex worlds |
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they are not all knowing masters of the worlds |
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they act enough to capitalize on specific
features of the world |
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They do not have enough neurons to build full
reconstructions of the world |
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The `diameter’ of their nervous systems is very
small (about six for humans) |
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Handles multiple goals via different behaviors,
with mediation, running concurrently |
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Multiple sensors are not combined but
complementary |
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Robust: graceful degradation as upper layers are
lost |
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Additivity facilitates easy expansion for
hardware resources |
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How should the task be decomposed? |
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Not a science! |
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On behaviors and arbitration |
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How should it be debugged? |
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What will bite you! |
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State the problem clearly |
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Identify any unstated assumptions about human
competency that robot may not have |
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State simply the set of minimum competencies
needed to achieve the task |
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Look for methods that will enable each
competency using your robot h/w |
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Match the
questions that should be asked with sensors that can answer them |
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Write behaviors that implement the methods and
connect the behaviors to fixed priority arbiters |
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Assume sensors will be noisy! Plan for graceful
degradation |
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Accept methods that, on average, advance the
task |
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Strive for robustness ahead of efficiency |
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Whenever (X) do |
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Else-whenever(Y) do |
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Etc |
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Always
sensing, looks for trigger then exerts control: |
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A behavior always monitors specific sensors, |
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it uses
a threshold of their values to dictate when it will attempt to control a
set of actuators: TRIGGER |
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Servo behavior has a feedback loop |
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Eg: light-positioning behavior |
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Never completes |
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Ballistic behavior, once triggered continues to
completion without any sensing |
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Eg: Escape behavior |
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1. Back up a preset distance |
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2. Spin a preset number of degrees |
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3. Move forward a preset distance |
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Use with caution due to sequential nature |
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Try to solve with servo behavior first |
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Behaviors have no (or little) state |
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They live in the ‘here’ and ‘now’ without memory |
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Use an FSM to for analysis and design to see how
every event is being handled |
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What to do with behavior 1 is not distinct from
behavior 2: |
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Eg. While reacting to one collision another
occurs (while escape is running) |
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Don’t add in special cases “overloading a
behavior” |
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Create a third behavior that looks for the
trigger of behaviors 1 & 2, and controls that situation |
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Two different behaviors are alternatively given
control or two parts of one behavior contradict each other. |
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Remedy: cycle-detection behavior |
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A series of rapid back and forth wheel motions
or lack of progress |
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Remedy: Table analysis, |
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When to arbitrate: |
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Eg. wander-behavior and recharge-behavior |
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What to decide? Average, take turns, vote |
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Use urgency |
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Consider graceful degradation |
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Use fixed priority arbitration for most cases |
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Can have multiple arbiters for different
actuators |
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Arbiter can report how it arbitrated |
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Develop and test each behavior in turn |
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The difficulty will lie in understanding and
managing the interactions between behaviors |
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Example: thrashing |
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Set up a debug tool: indicated which behavior is
active, sensor values, state of arbiter |
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Could be tones or GUI |
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Example |
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Overview |
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Practicalities |
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Next |
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Consider implementation with Carmen and Java |
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Consider BB approach for challenge |
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More sophistication in BB creature - mapping |
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Subsumption: Example instance of BB design |
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Brooks, R. A., "New Approaches to
Robotics", Science (253), September 1991, pp. 1227-1232. |
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Brooks, R. A. and A. M. Flynn "Fast, Cheap
and Out of Control: A Robot Invasion of the Solar System", Journal of
the British Interplanetary Society, October 1989, pp. 478ﾐ485. |
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Brooks, R. A. "A Robust Layered Control
System for a Mobile Robot", IEEE Journal of Robotics and Automation,
Vol. 2, No. 1, March 1986, pp. 14-23; also MIT AI Memo 864, September 1985. |
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Robot Programming: A Practical Guide to
Behavior-based Robotics, Joseph L. Jones, McGraw-Hill, 2004. |
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Lecture #1, Introduction, Prof. Ian Horswill http://www.cs.northwestern.edu/academics/courses/special_topics/395-robotics/ |
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“Sensing and Manipulating Built-for-Human
Environments”, Brooks et al, International Journal of Humanoid Robotics,
Vol 1, #1, 2004. |
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Notes
