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Autonomy for Ground Vehicles Status Report, July 2006 Sanjiv Singh Associate Research Professor Field Robotics Center Carnegie Mellon University
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Automation of All Terrain Vehicles Main issues: n Path tracking n Obstacle Detection at 4-6 m/s n Reliable operation n Low-cost system n Intuitive interface for u Teaching u Teleoperation u For conducting surveillance
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Vehicles 20032004
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Accomplishments (2003-2005) n Retrofitted 3 vehicles for autonomous operation n Helped design and Implemented general positioning solution n Designed and Implemented optimized design of laser scanner n Designed & implemented optimized electronics/power/computing package to support autonomy n Implemented path tracking at 6 m/s n Implemented method to detect “stuck condition” n Designed and Implemented collision avoidance merging data from two lasers scanners at 4 m/s n Designed and Implemented joint method of collision avoidance and path tracking. n Designed Implemented PDA control of vehicle and feedback of essential variables. n Designed and implemented method to plan smooth paths from via points specified on base station.
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Issues for 2006 n Perception u Calibration u New interface for sweeping laser u Vegetation detection n Guidance u Varied Traversability u Learned parameters u Improvement for tight spaces like tunnels n Experimentation u New vehicle testbed
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Calibration n New calibration method for calibrating relative pose of two laser scanners n Method was improved in Feb 2006.
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New Interface to Sweep Laser n Current version
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New Interface to Sweep Laser n New version
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New Interface to Sweep Laser n Board replaces Sync box.
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Vegetation Detection n In progress Example data shown to system Classification into three components
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Vegetation Detection n In progress Example data shown to system Classification into three components
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Vegetation Detection n In progress Example data shown to system Classification into three components
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Varied Traversability n Tested in simulation Vehicle avoids vegetation when possibleVehicle drives over vegetation if necessary
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Learned Parameters n Have added damping to decrease oscillations. Values are obtained from observation of human driving n Needs to be tested Vehicle oscillates when near many large obstacles New method (with damping) decreases oscillations
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Improved performance for Dodger n For tight spaces with possible GPS shift
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Comparison to other Algorithms n Compared with other methods
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Experimentation n Testing on real vehicle is very important. Golf cart is not suitable for high speed experimentation n Will give us ability to try different configurations n Status: ready to test end of July, 2006
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Potential Agenda n Done u Implementation of design with lower cost components u Smaller obstacle detection u Smoother steering n In progress u Navigation in cluttered environments u Immunity to small and medium vegetation u Immunity to GPS dropout n No plan yet u Road Following u Detection of moving obstacles u Immunity to large vegetation u Water/negative obstacle detection u All weather operation
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DARPA Urban Challenge 2007 n Fully Autonomous operation in Urban Environments n Competition to complete mission of 100 km in less than 6 hours n Other moving vehicles will be present (no pedestrians) n Vehicle must u Plan route automatically/ Replan routes when road is blocked u Follow roads u avoid collisions with other vehicles u Stop accurately at stop signs u Pass stopped vehicles u Go through intersections u Park in parking spot n CMU will enter one vehicle in competition
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Learned Parameters n New test vehicle has different steering characteristics than Grizzly n Learned parameters with damping for new vehicle n These are used in following vehicle tests
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Preventing Off-Path States n Add rows of obstacle points to keep vehicle closer to path n Vehicle is allowed to cross these points Border obstacles with cost 0.8 Border obstacles with cost 0.3 Border obstacles with cost 0.1 Detected obstacles with cost 1.0
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Planning Combined with Dodger n Previously: showed how planning helps prevent stuck situations u Planning only invoked when stuck state is predicted
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Planning Combined with Dodger n Previously: showed how planning helps prevent stuck situations u Planning only invoked when stuck state is predicted n Planning every iteration improves this further n Helps deal with desired path offsets (GPS jumps/outages) u Better at finding tunnel openings u Shifts goal point to center of tunnels
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Planning Combined with Dodger n Planning provides good direction for vehicle to go n Dodger provides: u smooth control u resistance to infeasible motions in the plan u another level of safety in obstacle avoidance
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Planning Combined with Dodger Planned path is jagged, not achievable by vehicle Dodger uses plan, but drives smoothly and gives more space to obstacles
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Planning Combined with Dodger n Show movies here: u Combination of movies taken at LTV on Friday F First three scenarios: MVI_0661 F Slalom (fourth): MVI_0667 F Wide and big slalom (fifth and sixth): MVI_0668 u Bonus footage: F 5 m/s complete loop: MVI_0669 F 6 m/s ¾ of the loop: MVI_0670 F Views from the vehicle: MOV05982, MOV05989 u dataReplayLoop.avi
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Tunnels Desired path has jumped to side of tunnel. Planner shifts goal point away from the wall.
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Tunnels n Regular operation: avoid obstacles that are meters away n In tunnels, walls are less than two meters away n Different set of parameters to track paths in a tunnel u Difficulty: When to switch parameter sets n Show movies here: u Tunnel movies from LTV: MVI_0678, MVI_0679 u dataReplayTunnel.avi
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Terrain Classification n System should classify laser data based on: u Height u How much it looks like a 2-d surface u How much it looks like a 3-d cluster n Show system hand-labeled data u System learns classifier using the three attributes
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Terrain Classification Results n Tested on data taken at LTV White: confident it’s ground Blue/purple: Classified as ground, but with low confidence Green: classified as not ground, confident it’s traversable Red: confident it’s not traversable
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Terrain Classification Results Ground Fence and bushes Grass Low grass
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Terrain Classification Results Road Cars Grass
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Terrain Classification Results Garage wall
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Terrain Classification Results Low grass Bush Grass
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Terrain Classification Results Road Grass
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Terrain Classification Results n Small boxes (should be labeled ‘obstacle’) sometimes classified incorrectly Box classified as traversable
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Terrain Classification Results n Small boxes (should be labeled ‘obstacle’) sometimes classified incorrectly n Need more training data with small obstacles
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