1. DARPA TMR Program Collaborative Mobile Robots for High-Risk Urban Missions Third Quarterly IPR Meeting May 11, 1999 P. I.s: Leonidas J. Guibas and Jean-Claude Latombe Computer Science Department Stanford University http://underdog.stanford.edu/tmr

2. u P.I.s: Profs. Leonidas J. Guibas and Jean-Claude Latombe. u Post-docs: –Alon Efrat: map building, target finding. –T. M. Murali: map building, target finding. –Rafael Murrieta: target tracking, robot experiments. u Ph. D. Students: –H. Gonzalez-Banos: map building, target tracking. –Cheng-Yu Lee: target finding in 3D. –David Lin: target finding in 2D. Research Group

3. u Three primary tasks: –Map building: Given no or partial a priori map, navigate robots in the environment to collect data to form a 2D/3D map. –Target finding: Sweep environment with the robots to detect and localise potential targets in 3D. –Target tracking: Move robots to maintain visibility of detected targets in 3D environment. Research Focus u Gather information in an urban environment. –Automatic generation of motion strategies. –Multiple autonomous but coordinated robots.

4. 4 Research Philosophy u Plan in 2D, sense/respond in 3D u Robots move in 2D but sensors are 3D. –Build 3D models. –Find targets even if they are not on the floor. –Track targets when they move off the floor. u Sensor independence –Software takes sensor parameters as input. –Software adapts to different sensor properties.

5. Challenges and Issues u Limitations of sensing capabilities: –Range (minimum and maximum). –Incidence angle. u Limitations exist both in 2D and 3D.

6. Challenges and Issues u Errors in sensing and localisation. u Algorithms have to take registration and alignment constraints into account.

7. Map Building u Task: Navigate robots in a building to collect data to form a 2D/3D map. u Goal: Generate efficient multi-robot exploration strategies. u Techniques: –Build 2D map using next-best view technique. –Build 3D map using art-gallery algorithm. u Result: 2D layout and 3D model.

8. Map-Building Strategy

9. Tomorrow’s Demo u Remotely control robot over the internet. u Demo of next-best-view algorithm. u Demo of art-gallery algorithm

10. 2D Map: Next-Best-View Algorithm u Task: Given current view and a partial model, compute the next sensing location. –take sensor limitations into account. –reach next viewpoint without collision. –ensure overlap between views to allow registration. u Goal: reduce number of sensing locations.

11. Domain of NBV computation u Compute safe region: guaranteed collision-free region. u Boundary of safe region consists of environment edges and “free” edges.

12. Computing the next-best view u Compute next-best view using random sampling. u Sample points in the interior of safe region. u Next location is sample with highest potential.

13. Example of a next-best-view computation

14. 3D Map: Art-Gallery Algorithm u Task: given a 2D map, compute a set of locations in the map for 3D sensing. –each boundary point should be visible from some location. –take sensor’s 3D limitations into account. –ensure overlap between views to allow registration. u Goal: compute a small set of locations.

15. Results of Art-Gallery Algorithm No visibility constraints Incidence constraint of 60 deg.

16. More Art-Gallery Results Visibility bounded in range Range reduced by 1/3

17. Features of Map-Building Algorithm u Makes global decisions. –Reduces total distance travelled, number of sensing locations. u Scales to multiple robots: –in 2D: Send robots to sampled locations with high potential that are far apart. –in 3D: Cluster sensing locations, send robots to different clusters. u Minimises number of 3D sensing operations.

18. 18 u Implemented next-best view planner for constructing 2D model of an urban environment. u Implemented target-finding planner for robots with cone vision. u Implemented target-finding algorithm for aerial observer moving in a set of buildings. u Developed algorithms for target-finding for a team of robots that maintain communication links. u Implemented real-time planner for motion in the presence of moving obstacles. Achievements

19. 19 Collaboration u SRI: – combining mapping and next-best-view software. –combining human tracking with target-tracking planner. u SAIC: –multi-robot target finding and target tracking algorithms.

20. Progress to Date