path planning optimization using adams and heeds

MSC Software Confidential 2012 International Ground Vehicle Users Symposium

Path Optimization using ADAMS/Car

and HEEDS/MDO 2012 International Ground Vehicle Users Symposium

Presented By: Jesper Slattengren, Pratt & Miller Engineering

October 23, 2012


• Find a trajectory for the driver to follow so that

– The vehicle clears the boundaries of the given path

– Follows this maneuver at a specified speed, or maximizes the speed

through the desired path

– Need only to find A feasible solution, not the BEST solution

Problem


• Limit handling problem

• Small variation of factors can cause vehicle roll-over

– Up to a third of evaluations result in roll-over

– Error evaluations do not contribute to solution space

What makes this interesting?


Background

4

Speed requirement has

increased over the years

HMMWV was designed for 40

mph

Current requirement is usually

45-50 mph

50 mph is used as target in this

presentations

Path outline is scaled to

vehicle length and width

Among standard

requirements for

military vehicles in

the US is to pass a

NATO lane change

AVTP 03-160W at

specified speed


Animation


Traditional approach

6

Copy and paste to driver

control data file (dcd)

Simulate, postprocess, adjust

trajectory, repeat…

Manual iteration of

path using Excel to

calculate trajectory


• Create request for clearance of cones for outside of all four

corners

– 3 requests components for each corner

• Each segment of the track

• Set clearance to 1000 when not in segment

– Minimum of each request is used as constraint

• Total of 12 constraints

• The objective was to clear all constraints and maximize the sum of all

clearances

– Request statement:

IF(-DX(body.mal_front_corner,ground.MAR_Outline2, ground.MAR_Outline2)*

DX(body.mal_front_corner, ground.MAR_Outline4, ground.MAR_Outline4):

1000,1000,

DY(body.mal_front_corner, ground.MAR_Outline4, ground.MAR_Outline4))

Clearance request


Baseline responses

Target: Min of each curve > 1 mm

Each corner clearance is the composite of the three section requests


• HEEDS will copy specified files to a local working directory

– Used for local evaluation

• HEEDS will start ADAMS/Car in batch, using an input command

file “setup.cmd” that:

– Executes NATO.py that builds the dcd-file based on provided parameters

– Loads the binary file with the vehicle assembly

– Runs the simulation

– Writes an output file “HEEDS.out” that lists the relevant results from the

simulation.

• HEEDS uses “acar11batchclose.bat setup.cmd” as start

command for ADAMS.

– acar11batchclose.bat starts ADAMS/Car in batch mode and then closes

the window call "C:\MSC.Software\MD_Adams_x64\2011\common\mdi.bat" acar ru-acar b %1

exit

Process

9


• Dcf and dcd files should be local as should NATO.py and

setup.cmd

– Listed as input files to HEEDS

• Dcf.xml and NATO.py are used to tag optimization factors

(inputs; determined by HEEDS)

• HEEDS.out will be written to local working directory

– Specified as output file to HEEDS

File structure

10


• Use local dcf and dcd-files

– Bypass database search

– HEEDS copies required files to local working directory

• Python script to generate trajectory and build dcd-file

• ADAMS command file to

– Run python script

– Load binary

– Run simulation

– Generate ASCII output file

• Input tagging directly in python script and driver control file

(dcf.xml)

• Output tagging in ASCII file “HEEDS.out”

Using HEEDS/MDO with ADAMS/Car


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0

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2

-160 -140 -120 -100 -80 -60 -40 -20 0

Late

ral A

xis

(m)

Longitudinal Axis (m)

ADAMS Trajectory

z1Entryz1Entryz1Exit

z2Exit z2Entry

z3Entryz3Exit

trans1Entry

trans1Exittrans2Entry

trans2Exit

trans1Shifttrans2Shift

Direction of Travel

Parameterized trajectory


• Besides the trajectory factors, the driver lateral controller

parameters should be included

Factors

Min Baseline Max Res #points Tagged in

LatPthP 0.75 1 1.25 0.01 51 dcf.xml

LatPthI 0.25 0.1 2 0.01 176 dcf.xml

LatPthD 0 1.00E-05 1.00E-04 1.00E-06 101 dcf.xml

minPreviewDistance -1 -1 -1 constant dcf.xml

z1Entry -2.5 0 2.5 0.05 101 NATO.py

z1Exit -2.5 0 2.5 0.05 101 NATO.py

z2Entry -2 2.8 6 0.05 161 NATO.py

z2Exit -2 3.9 6 0.05 161 NATO.py

z3Entry -2 0.5 6 0.05 161 NATO.py

z3Exit -2 0.2 6 0.05 161 NATO.py

trans1Entry -2 4 8 0.05 201 NATO.py

trans1Exit -2 2 8 0.05 201 NATO.py

trans2Entry -12 5 8 0.05 401 NATO.py

trans2Exit -2 0.25 8 0.05 201 NATO.py

trans1Shift -1 0 1 0.02 101 NATO.py

trans2Shift -1 0 1 0.02 101 NATO.py

Speed 22352 22352 22352 constant dcf.xml


system &

command_text = "\"C:\\MSC.Software\\MD_Adams_x64\\2011\\common\\mdi.bat\" python NATO.py" &

send_output_to_info_window = off &

echo_to_logfile = off

file binary read file="E:/project/work/base_v61/Test_DLC.bin“

.

.

acar analysis full_vehicle sdi submit &

assembly=.Test_model &

output_prefix="Test" &

analysis_mode=interactive &

road_data_file="mdids://acar_shared/roads.tbl/2d_flat.rdf" &

dcf_file=“dcf.xml" &

log_file=yes

.

.

file text open file="Heeds.out" open=overwrite

file text write file="Heeds.out" &

format="FL_1 = %f" &

values=(eval(MIN( .Test_model.Test_dcf.DLC_Clearance_Left.U2 ))) &

newline=yes

.

.

file text close file="Heeds.out"

quit conf=no

setup.cmd

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class natoLC:

def __init__(self):

### Following parameters can be modified ###

self.z1Entry = 0.0

self.z1Exit = 0.0

self.z2Entry = 2.8

self.z2Exit = 3.9

self.z3Entry = 0.5

self.z3Exit = 0.2

self.trans1Entry = 4.0

self.trans1Exit = 2.0

self.trans2Entry = 5.0

self.trans2Exit = 0.25

self.trans1Shift = 0.0

self.trans2Shift = 0.0

self.VehWidth = 2.32

self.vehLength = 4.57

self.DLC_X_offset = 0.0

self.DLC_Y_offset = 0.0

self.outFile = "tempDCD.dcd"

.

.

.

NATO.py

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FL_1 = 32.472361

FL_2 = 189.953480

FL_3 = 181.878024

RL_1 = 98.256731

RL_2 = 150.678916

RL_3 = 352.526555

FR_1 = 240.500143

FR_2 = 78.848340

FR_3 = 124.713861

RR_1 = 238.895725

RR_2 = 128.640251

RR_3 = -8.220754

MaxRoll = 6.707427

SW_RMS = 41.060484

Dist = 110294.928090

HEEDS.out

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These values are the output from the

simulation, extracted by the ADAMS

command file.

The minimum of the 12 clearance

requests are used for constraints.

The “MaxRoll” and “Dist” are used to

determine if the simulation was

successful.

SW_RMS (Steering Wheel angle RMS)

is an indication on how nervous the

driver controller behaves and minimizing

this can be used as objective.


• Which trajectory should the optimization start from?

– Best manual optimized?

– Path that works for lower speed?

– Nominal, center-of-path line?

– Straight line?

• Which optimization method works best?

• Can maximization of speed be included as well as trajectory

search?

Questions

No, that was a bad idea to start with


• SHERPA, QP, Simplex methods evaluated

• Simplex gives very fast convergence only IF baseline guess is

VERY close to feasible solution

– Did not work well in this case

• QP converges fast if baseline is reasonable close to feasible

solution

• SHERPA does the job in almost all cases

Optimization method


• Starting with trajectory that worked for 43 mph at a lighter

configuration

• Simplex did not find a solution

• Sherpa found a feasible solution after 551 evaluations

• QP needed only 57 iterations

• Not much difference in trajectory

• Quite a bit of difference in driver controller

Results

Baseline SHERPA

551 QP 57

LatPthP 1 1.62 1.05

LatPthI 0.1 1.55 0.22

LatPthI 1.00E-05 2.60E-05 1.07E-05


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Cones

Baseline

SHERPA 551

QP 57

Results


Different problem

21

A “best manual” baseline was

used

On a different

vehicle, very

different results

were achieved

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Cones

Baseline

Optimized


Different problem (cont.)

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Neither QP or Simplex did

converge

solution too far off from baseline

SHERPA found a solution

in 149 evaluations

Starting from middle-

of-the-road “nominal

trajectory”

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Cones

Baseline

Optimized Sherpa


Driver trajectory and corner paths

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The corners of the vehicle will

not follow the driver trajectory

due to vehicle and driver

dynamics

Optimized driver path

and corner

trajectories

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Cones

Baseline

Optimized Sherpa


• HEEDS and ADAMS/Car works well together for path

optimization

• Use SHERPA if no good guess is available

– Use “middle-of-the-road” path

• Use QP if initial trajectory is sufficiently close to final trajectory

• No success in maximizing speed

– Multiple tries were performed using

• The same 12 constraints

• Maximize speed as objective

– Could not get speed increase even when starting from 43 mph

• As seen here, 50 mph is feasible

Conclusion

path planning optimization using adams and heeds

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