Segway RMP Experiments at Georgia Tech

DARPA MARS Segway WorkshopSeptember 23, 2003

Tom Collins

Major activities

n Interface to MissionLabn Teleoperation

experimentsn Use of laser range

finder for terrain characterization

Physical modificationsn Basic operation requires

mounting of only two components– Ruggedized case containing

laptop, power converters, 802.11, camera, and video encoder (on top plate, 4 bolts)

– Battery (on base, in custom bracket)

n SICK mount for laser experiments

n Optional protective kill switches (slight modification of UMassdesign)

Software and Interface

n Uses Kvaser LapCAN card for RMP interfacen RMP drivers run as part of HServer

application– HServer provides a uniform interface across

various robot platforms and sensors– RMP updates acquired at about 50 Hz

n MissionLab generates executable code for RMP like any other supported robot

n Interface to SICK and encoded video stream unchanged from our other platforms

Initial experiments

n Work stalled by bailment agreement

n Interface completed in lab during late July, with little actual robot usage

n Video shows first significant run –teleoperation at Ft. Benning

Lessons learned

n Only one unexplained instance of “dying” (in lab, very early in testing)

n Virtually no tipping until we started tryingn Vulnerable to tipping during sudden

acceleration in loose soil or graveln Hill climbing capabilities limitedn Battery power is impressive for vehicle sizen Speed, turning radius better than other

outdoor robots

Ft. Benning runs- MOUT

Ft. Benning run -- Sewer

Ft. Benning runs – Leader/Follower

Ft. Benning runs – Stress testing

Terrain characterization

n Laser rangefinders have been used extensively on robots– “On or near the ground”:

§ Road following§ Footfall selection § Vegetation characterization

– Localization and/or visualizationn RMP is at least as vulnerable to

discontinuities as a legged robotn RMP has a “free” tilt mechanismn Seems worthwhile to revisit the terrain issue

with the RMP in mind

Geometry of a SICK on the RMP

n First consider ONLY the reading taken directly ahead (azimuth = 0)

n Let – (x0, y0, z0) be the sensed

point on the ground in egocentric coordinates

– Angles, distances be defined as in figure

n Then

)sin()sin()cos()cos()cos()sin(

0

00

00

0

ραρρραρρ

+−−+=++−=

=

rdlwzrdly

x

RMP pitch behaviorn Data taken

directly from RMP pitch sensor

n Shows the “tip” needed to move across fairly level ground

Initiates motion Stops

Average Tilt

Closer look at pitchn “Gross” control

operates at ~0.15 Hz– varying with

payload, etc.– Maintains

speed?n Fine control

operates at about 1 Hz

n Hopefully, all of these effects can be made to disappear in range readings

Raw laser range readingsn Still considering

only the single reading straight ahead (and tilted down)

n Data taken during same maneuver as previous pitch data – During the active

movement phasen Note the

appearance of same frequencies

n More apparent when scaled sin(pitch) is superimposed (inset)

Corrected laser range readingsn Apply the

correction for z0n Plotted along with

raw range data for comparison

n Since this was fairly level ground, the plot should stay near zero– It actually

approximates ?z relative to z at wheel

– Deviation from zero mostly due to minor slopes

)sin()sin()cos( 00 ραρρ +−−+= rdlwz

Check for latency issuesn Pitch and range are

acquired from different devices

n Timestamps are applied at the computer running hServer

n Latency characteristics of RMP are unknown

n So, some high-frequency errors may be due to picking the “wrong” pitch data

n Graph shows that the best choice of pitch data is the most recent at the time of laser scan completion

3D terrain

n Use data for all azimuths, not just straight ahead

n Calculate x and y for every point

Terrain and obstacles

More visualizations

How this data can be usedn Autonomous operation

– Reactive sensing of terrain considerations (perceptual schemas)– Without even attempting to register data in a larger world map, it

provides§ Local positive and negative obstacles smaller than wheel width§ Sideslope of path ahead (RMP is vulnerable to side tipping)§ Fore/aft slope

– All of these features can be expressed as simple avoidance vectorsn Teleoperation

– Visualization of terrain (can be displayed alongside visual image)– Could be processed to produce simple operator cues (possibly

generated by same perceptual schemas above)– Low-light operation

Future work

n Full autonomous operationn Multiagent experiments

– Heterogeneous with existing GT robots– Homogeneous with MARS 2020 team

n Use SICK intensity information to display surface brightness (in IR)

n Estimation of surface material (sand, gravel, other “difficult” surfaces)