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Constant Following Distance Simulations

CS547 Final ProjectDecember 6, 1999

Jeremy Elson

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research goals

• Motivation: Explore the effect of different radio models on the accuracy of simulation

• Two opposing models:– Arena: Smother everything and noise; forces

you to build algorithms that are noise-resistant– network simulators: Extremely detailed

• In network simulation, very accurate radio models might be required

• What about in robotics? (Easier target...)

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experimental goals

• To explore this, the idea was:– Implement a simple set of behaviors in

simulation across different radio models– Characterize the behavior across radio models

(perhaps across behavior sets)– Implement the algorithms in reality– Compare simulation to reality

• This work is still entirely simulation; implementation coming

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the experiment

• Idea: Use radio contact to allow “constant distance following”– Both robots follow the same path, one tries to

keep a constant distance behind the other– Path: Endlessly circling SAL

• Assume that the radio acts as a “proximity detector”– Within radio range: you’re too close– Out of range: you’re too far away

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simple SAL model

20 m

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robot sonar model

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3 45

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Exact sonar readings are never used; all 7 sonars areclassified into NEAR (< 2m), MID (2-5 m), or FAR (>5m)

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desired steady state

don’t care

!NEAR MIDNEAR=collisionFAR=corner coming soon

MIDNEAR=too close to wallFAR=corner crossing or too far from wall

!NEAR

Goal: endlessly circle SAL, clockwise

FAR

!NEAR

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basic behaviors

• Speed and direction controlled independently• Direction:

if (state[6] == FAR) { turnRate = 4.0; // Sharp right turn to follow the wall } else if (state[5] == FAR || state[1] == NEAR || state[2] == NEAR) { turnRate = 2.0; // Shallow right for left collision, or drift away from wall

} else if (state[3] != FAR || state[4] == NEAR || state[5] == NEAR || state[6] == NEAR) {

turnRate = -2.0; // Shallow left for right or dead on collision, } // or getting too close to wall

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basic behaviors

• Speed:

if (state[2] == NEAR || state[3] == NEAR || state[4] == NEAR) speed = -1.0; // Go backwards if we’re running into something else if (state[6] == FAR) speed = 2.5; // Slow down a lot if we’re past the end of the wall else if (state[5] == FAR) speed = 5.0; // Slow down a little if the wall end is coming else speed = nominalSpeed; // 7.0

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resulting path (1 robot)

Tic marks are atconstant timeintervals

Note robot slowsdown as it reachescorners

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repeated traverses

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obstacle avoidance

Robot backs up,turns, goes forwards

Robot takeswide turn

Obstacle avoidanceoverrides

wall following

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repeated traverses

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proximity detection• Simple model of Radiometrix (or similar) radio

-- xmit success probability drops off sharply as distance increases

• Our goal: live on the slope of the curve

• Simulated with “(d + error) squared threshold” model

• Potential range of error is a percentage of d; chosen at random uniformly from that range

– The percentage is a parameter

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idealized model

nominal transmit range = 5m

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simulated model

nominal transmit range = 5m

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following behavior• One robot is leader (nominal speed = 7.0),

one is follower (nominal speed is variable)

if (following) { getLossRate(&shortLossRate); if (shortLossRate > 80) nominalSpeed = 9.0; else if (shortLossRate < 20) nominalSpeed = 5.0; else nominalSpeed = 7.0;} else { // leader nominalSpeed = 7.0;}

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results

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conclusions

• Simple rules are best– More complex rules never worked as well!– Priority is (naturally) critical!

• Using proximity detection seems to work reasonably– Given that it is not entirely easy to track speed

and distance around turns, etc.

• Surprisingly, percent-distance error seemed to have little impact on the outcome

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future work

• Additional Radio Models & Behaviors– e.g., bit error rate model: packet success

becomes sensitive to packet length– Then, probe with both short and long packets

• Implementation on real Pioneer-2– Compare performance under simulation to that

of the real implementation

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that’s all, folks!