Autonomy for Ground Vehicles Status Report, July 2006

Autonomy for Ground Vehicles

Status Report, July 2006

Sanjiv SinghAssociate Research ProfessorField Robotics CenterCarnegie Mellon University

Automation of All Terrain VehiclesMain issues: Path tracking Obstacle Detection at 4-6

m/s Reliable operation Low-cost system Intuitive interface for

Teaching Teleoperation For conducting surveillance

Vehicles

2003 2004

Accomplishments (2003-2005) Retrofitted 3 vehicles for autonomous operation Helped design and Implemented general positioning solution Designed and Implemented optimized design of laser scanner Designed & implemented optimized electronics/power/computing

package to support autonomy Implemented path tracking at 6 m/s Implemented method to detect “stuck condition” Designed and Implemented collision avoidance merging data from

two lasers scanners at 4 m/s Designed and Implemented joint method of collision avoidance and

path tracking. Designed Implemented PDA control of vehicle and feedback of

essential variables. Designed and implemented method to plan smooth paths from via

points specified on base station.

Issues for 2006 Perception

Calibration New interface for sweeping laser Vegetation detection

Guidance Varied Traversability Learned parameters Improvement for tight spaces like tunnels

Experimentation New vehicle testbed

Calibration New calibration method for calibrating relative pose of two

laser scanners Method was improved in Feb 2006.

New Interface to Sweep Laser

Current version


New version


Board replaces Sync box.

Vegetation Detection In progress

Example data shown to system

Classification into three components







Varied Traversability Tested in simulation

Vehicle avoids vegetation when possible Vehicle drives over vegetation if necessary

Learned Parameters Have added damping to decrease oscillations. Values are

obtained from observation of human driving Needs to be tested

Vehicle oscillates when near many large obstacles New method (with damping) decreases oscillations

Improved performance for Dodger

For tight spaces with possible GPS shift

QuickTime™ and aCinepak decompressor

are needed to see this picture.

Comparison to other Algorithms

Compared with other methods

Experimentation Testing on real vehicle is very important. Golf cart is not suitable for high speed experimentation Will give us ability to try different configurations Status: ready to test end of July, 2006

Potential Agenda Done

Implementation of design with lower cost components Smaller obstacle detection Smoother steering

In progress Navigation in cluttered environments Immunity to small and medium vegetation Immunity to GPS dropout

No plan yet Road Following Detection of moving obstacles Immunity to large vegetation Water/negative obstacle detection All weather operation

DARPA Urban Challenge 2007 Fully Autonomous operation in Urban Environments Competition to complete mission of 100 km in less than 6 hours Other moving vehicles will be present (no pedestrians) Vehicle must

Plan route automatically/ Replan routes when road is blocked Follow roads avoid collisions with other vehicles Stop accurately at stop signs Pass stopped vehicles Go through intersections Park in parking spot

CMU will enter one vehicle in competition

Learned Parameters

New test vehicle has different steering characteristics than Grizzly

Learned parameters with damping for new vehicle These are used in following vehicle tests

Preventing Off-Path States Add rows of

obstacle points to keep vehicle closer to path

Vehicle is allowed to cross these points

Border obstacles with cost 0.8



Detected obstacles with cost 1.0

Planning Combined with Dodger

Previously: showed how planning helps prevent stuck situations Planning only invoked when stuck state is predicted


Previously: showed how planning helps prevent stuck situations Planning only invoked when stuck state is predicted

Planning every iteration improves this further Helps deal with desired path offsets (GPS

jumps/outages) Better at finding tunnel openings Shifts goal point to center of tunnels


Planning provides good direction for vehicle to go Dodger provides:

smooth control resistance to infeasible motions in the plan another level of safety in obstacle avoidance


Planned path is jagged, not achievable by vehicle

Dodger uses plan, but drives smoothly and gives more space to

obstacles


Show movies here: Combination of movies taken at LTV on Friday

First three scenarios: MVI_0661 Slalom (fourth): MVI_0667 Wide and big slalom (fifth and sixth): MVI_0668

Bonus footage: 5 m/s complete loop: MVI_0669 6 m/s ¾ of the loop: MVI_0670 Views from the vehicle: MOV05982, MOV05989

dataReplayLoop.avi

Tunnels

Desired path has jumped to side of tunnel.Planner shifts goal point away from the wall.

Tunnels

Regular operation: avoid obstacles that are meters away

In tunnels, walls are less than two meters away Different set of parameters to track paths in a

tunnel Difficulty: When to switch parameter sets

Show movies here: Tunnel movies from LTV: MVI_0678, MVI_0679 dataReplayTunnel.avi

Terrain Classification

System should classify laser data based on: Height How much it looks like a 2-d surface How much it looks like a 3-d cluster

Show system hand-labeled data System learns classifier using the three

attributes

Terrain Classification Results

Tested on data taken at LTV White: confident it’s ground

Blue/purple:

Classified as ground, but with low confidenceGreen: classified

as not ground, confident it’s traversable

Red: confident

it’s not traversable


GroundFence and bushes

Grass Lowgrass


Road

Cars

Grass


Garage wall


Low grass

BushGrass


Road

Grass

GrassGrass

Grass


Small boxes (should be labeled ‘obstacle’) sometimes classified incorrectly

Box classified as traversable


Small boxes (should be labeled ‘obstacle’) sometimes classified incorrectly

Need more training data with small obstacles

Documents

Autonomy for Ground Vehicles Status Report, July 2006