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The Wayfarer Modular Navigation Payload for Intelligent Robot Infrastructure. Brian Yamauchi [email protected]. Autonomous Urban Recon. Critical Army need Daily casualties during patrols Hazards Snipers Improvised explosive devices RPGs Teleop is not enough Limited comms range - PowerPoint PPT Presentation
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The Wayfarer Modular Navigation Payload for Intelligent Robot
Infrastructure
Brian [email protected]
Autonomous Urban Recon• Critical Army need• Daily casualties during
patrols• Hazards
– Snipers– Improvised explosive devices– RPGs
• Teleop is not enough– Limited comms range– Line-of-sight restrictions– Limited bandwidth
Modular Navigation Infrastructure
Wayfarer Modular Navigation Payload
PackBot ►
◄ R-Gator
FCS SUGV▼
Wayfarer Objectives• Focused, applied research project• Develop technology to enable small UGVs to
perform autonomous recon missions• Enable small UGVs to:
– Navigate autonomously down urban streets– Record digital video (EO/IR) and build map– Return autonomously to starting point– Provide video log and map to warfighters
Urban Reconnaissance Missions• Perimeter Recon
– Follow outside wall of building
• Route Recon– Follow current street for specified distance
• Street Recon– Follow GPS waypoints and street directions
• Record EO/IR video and build map• Return to starting point
Wayfarer Sensors• SICK LD OEM 360-degree
planar LADAR• Point Grey Bumblebee
stereo vision system• Organic GPS receiver• Crossbow six-axis IMU• Color and low-light B/W
video cameras• Indigo Omega FLIR camera• Swiss Ranger 3D flash
LADAR
Wayfarer PackBot
Scaled Vector Field Histogram (SVFH)
• Extension of Borenstein & Koren’s Vector Field Histogram (VFH) obstacle avoidance
• VFH: Map each range reading from sensors to corresponding polar coordinate sector
• SVFH: Spread each reading to vote for all sectors within an arc length inversely proportional to range:
= k / r
arc length (radians),
k: constant (0.4), r: range (meters)
SVFH Hallway Example
SVFH Bins Clear Vectors
SVFH Intersection Example
SVFH Bins Clear Vectors
Wayfarer Obstacle Avoidance
Perimeter Following• Detect and follow building walls• Hough Transform
– Detects linear features in range data– Finds building walls and other street-aligned features
(e.g. cars, curbs)– Collects votes to determine current wall heading
• Integrated with obstacle avoidance
Hallway Wall Detection
Best LineAll Lines
Intersection Wall Detection
Best LineAll Lines
Outdoor Perimeter Detection
Wall Tracking Landscape Tracking
Wayfarer Perimeter Following
Autonomous Mapping• Build occupancy grid map on robot• Transmit local region back to OCU to minimize
bandwidth usage• Runs in parallel with navigation and avoidance• Robot can travel beyond communications range and
return with a map• Can also run in background during teleoperation
Wayfarer Autonomous Mapping
Indoor Map
Localization• Heading tracking using Hough transform
– Track orientation of linear features– Use to estimate robot orientation
• Alternative approaches– Scan matching– GPS/Compass/INS– Kalman filters– Particle filters (Monte Carlo SLAM)
Future Work• Outdoor testing of perimeter following• Street following• GPS-based street navigation
Conclusions• Wayfarer will provide autonomous urban navigation
technology for man-portable UGVs• By September 2005, we will have two fully-
operational Wayfarer UGV prototypes able to perform urban reconnaissance missions
• Wayfarer navigation payload will provide intelligent navigation infrastructure for PackBots and R-Gators, as well as FCS SUGV and other UGVs