The Wayfarer Modular Navigation Payload for Intelligent Robot Infrastructure

1. The Wayfarer Modular Navigation Payload for Intelligent Robot Infrastructure Brian Yamauchi yamauchi@irobot.com

2. 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

3. Modular Navigation Infrastructure PackBot ► FCS SUGV ▼ Wayfarer Modular Navigation Payload ◄ R-Gator

4. 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

5. 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

6. 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

7. Wayfarer PackBot

8. 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: q = k / r q: arc length (radians), k: constant (0.4), r: range (meters)

9. SVFH Hallway Example SVFH Bins Clear Vectors

10. SVFH Intersection Example SVFH Bins Clear Vectors

11. Wayfarer Obstacle Avoidance

12. 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

13. Hallway Wall Detection All Lines Best Line

14. Intersection Wall Detection All Lines Best Line

15. Outdoor Perimeter Detection Wall Tracking Landscape Tracking

16. Wayfarer Perimeter Following

17. 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

18. Wayfarer Autonomous Mapping

19. Indoor Map

20. 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)

21. Future Work • Outdoor testing of perimeter following • Street following • GPS-based street navigation

22. 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