DAMN : A Distributed Architecture for Mobile Navigation Julio K. Rosenblatt Presented By: Chris Miles

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

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

Fixed Priorities – Certain nodes always have precedence over other nodes Vector Sum / Potential Fields – Average desired actions Other Arbitration Methods

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

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

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

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

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

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

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

Why a Parabola? Keeps the solution from being smoothed into no-zones

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

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

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

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

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

Example Planner says turn either 25 degrees right, 15 degrees left, or 170 degrees right Obstacle Avoidance says don’t turn left there is a wall Vehicle dynamics behavior says don’t 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

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

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

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

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