DAMN: A Distributed Architecture for Mobile Navigation

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DAMNA Distributed Architecture for Mobile
Navigation

• Julio K. Rosenblatt
• Presented By Chris Miles

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Goal

• Behavior based systems
• Easy arbitration between all kinds of behaviors
• Working towards different objectives
• Different time scales
• Work towards simultaneous objectives

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Motivation

• Behavior based systems are good
• It is difficult to arbitrate between the desired
  actions of different nodes however
• To overcome this an arbitration architecture
  should be imposed

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Other Arbitration Methods

• Fixed Priorities Certain nodes always have
  precedence over other nodes
• Vector Sum / Potential Fields Average desired
  actions

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Problems

• Lose information
• Nodes cannot specify their range of desired
  behavior, only their optimal solution
• Often times the 90 solution is enough
• Leads to two party system
• Does not find actions that satisfy multiple
  objectives simultaneously

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Potential Field Problems

• Behaviors cannot easily specify the actions they
  want / do not want
• Undesired behavior
• Do anything but drive into that rock
• Point strongly away
• Non-specific behavior
• just kinda go left
• Cannot be done

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Simultaneous Objectives

• In many cases in both robot and human life one is
  working towards a number of objectives
  simultaneously
• It is best to perform actions that help reach all
  or many of them at once, versus only focusing on
  a single objective at a time

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DAMN Arbitration

• Behaviors vote across the range of possible
  behaviors
• Each possibility is given a value from -1 to 1
  representing the nodes preference towards that
  action
• Each node has a weight, which determines how many
  votes it gets
• allows for meta level control

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Example Collision Detection

• A obstacle avoidance behavior
• Determines how long each action will take to lead
  to a collision
• If I turn left, how long until I hit the wall
• Votes for each action based on that time
• Since this behavior has a very strong weight,
  any actions leading to collision are strongly
  discouraged

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The DAMN arbiters

• The actions voted on are discretized along
  appropriate dualities
• Turn
• left, straight, right
• Speed
• Fast, Slow, Stop, Reverse
• Field of Regard
• Where to look / point cameras

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Counting The Votes

• Vote in discrete space
• Combine them into continuous space Similar to
  defuzzification
• Add up those votes
• Smooth the resultant
• Fit a parabola to the optimal neighboring
  values
• Take the peak of the parabola as the action

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Why a Parabola?

• Keeps the solution from being smoothed into
  no-zones

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DAMN Behaviors

• They mention a number of behaviors, how they vote
  and why they are important

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Collision Detection

• Similar to my example, only vote lower for paths
  that lead to near collisions as well

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Vehicle Dynamics

• Vote against behaviors that exceed the vehicles
  capabilities
• Vital for any robot that moves more then 2 mph
• Aimed at keeping the robot from rolling over as
  it turns
• Note curvature is proportional to center of
  gravity
• An SUV twice as high as a car can take half the
  turn

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Goal Directed Behaviors

• Paper describes a number of high level behaviors
• DAMN does not restrict behaviors from operating
  at different voting frequencies Slower
  behaviors vote less often
• Not sure what happens between those votes
• Planners vote for actions that move too satisfy
  their objectives

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Weights

• Determine how strongly this behaviors opinion
  counts
• Low level behaviors have higher weights
• Obstacle avoidance takes precedence over path
  following
• Behaviors can vote for flat areas where any
  behavior is good
• The less weighted behaviors vote is then the
  deciding factor

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Example

• Planner says turn either 25 degrees right, 15
  degrees left, or 170 degrees right
• Obstacle Avoidance says dont turn left there is
  a wall
• Vehicle dynamics behavior says dont turn more
  then 30 in either direction based on current
  speed
• The other behaviors rule out the two unsafe
  behaviors associated with the planner
• No interference in the planners final preference

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Conclusions

• DAMN uses voting to arbitrate between behaviors
• Very natural behaviors
• Combine for powerful results

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Other Arbitration Schemes In DAMN

• Other arbitration schemes can be implemented in
  DAMN
• Nodes voting entirely for a single field
  Priority based
• If nodes vote in triangular fashion Potential
  fields

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Pros

• Behaviors are insanely obvious
• Natural and powerful idea
• Beats the electoral system
• General Objective Designed Distributed
  Architecture for Mobile Navigation systems are
  even more powerful

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Cons

• Have to vote across the array of all possible
  actions
• Many degrees of freedoms -gt many continuums to
  vote over
• Difficulties in dealing with interwoven
  continuums speed / turn reactions
• Vehicle Dynamics behavior seems to deal with that
  very well