1 DARPA TMR Program Collaborative Mobile Robots for High-Risk Urban Missions Second Quarterly IPR Meeting January 13, 1999 P. I.s: Leonidas J. Guibas and Jean-Claude Latombe Computer Science Department Stanford University

2 P.I.s: Profs. Leonidas J. Guibas and Jean-Claude Latombe. Post-docs: –Alon Efrat: map building, target finding. –T. M. Murali: map building, target finding. –Rafael Murrieta: target tracking, robot experiments. 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 Research Focus Gather information in an urban environment. –Automatic generation of motion strategies. –Multiple autonomous but coordinated robots. Three primary tasks: –Map building: Given no or partial a priori map, navigate robots in the environment to collect data to form a map. –Target finding: Sweep the environment with the robots to detect and localise potential targets. –Target tracking: Move robots to maintain visibility of detected targets.

4 Challenges and Issues Limitations of sensing capabilities: –Range, incidence angles. –Trade-off between sensing models and motion planning strategies. Errors in sensing and localisation.

5 Challenges and Issues Collaboration between multiple robots: –Avoiding replication of work. –Maintaining communication network to share information. –Relative localisation. Collaboration between air and ground robots.

6 Experimental Setup One Nomad SuperScout: SICK’s time-of-flight range sensor for 2D map building. One Nomad 200: triangulating laser sensor for 3D sensing. Two Nomad SuperScouts: tracking cameras for target finding and target tracking. One Nomad 200: camera for target finding and target tracking, target itself.

7 Map Building Task: Given no or partial a priori map, navigate robots in a building to collect data to form a map. Goal: Algorithms for efficient exploration strategies. –Minimise time to build the map. –Coordinate multiple robots. –Take sensing limitations into account. Technique: interactions between 2D and 3D. –Build 2D map using next-best view technique. –Exploit 2D map to decide where to perform complex 3D sensing operations.

8 Environment Model Set of 2D layouts: –Geometric representation (polygons). –Also represent uncertainty. Layouts include obstacles: –Obstruct motion (glass windows, mines). –Obstruct visibility. Layouts include landmarks for localisation. Set of partial 3D models (images from selected points in the 2D maps).

9 Sensor Model for Map Building 2D map building (range finder, stereo camera): –minimum and maximum range. –maximum incidence angle. –cone of visibility. 3D sensing (colour camera): –focal length, depth of field. –cone of visibility.

10 Next-Best View Map-Building Algorithm Identify unexplored portions of the boundary of the map built so far. Travel towards boundary edge with highest rank. –Estimate new information gained by exploring an edge. –Compute cost of travelling to that edge. –Rank of an edge is ratio of information and cost.

11 Features of Map-Building Algorithm Makes global decisions. –Minimises total distance travelled. Can exploit a priori information about the environment. Scales to multiple robots: –Send robots to edges with high rank that are far apart. –Different robots explore different portions of environment.

12 Target Finding Task: Sweep the environment with the robots to detect and localise potential targets. Goal: Generate reliable motion strategies for the robots. Techniques: reliable in spite of recontamination.

13 Target Finding in 2D Restricted visibility models (cone of vision, minimum/maximum range): –Robot moves with “back to the wall.” Communication maintenance: –Robots move while maintaining a network of communication links. –Robots protect each other and share information.

14 Target Finding in 3D Observer is aerial (helicopter). Targets are on the ground. Obstacles are buildings. Compute a path for helicopter that sweeps the buildings.

15 Target Tracking Task: Move robots to maintain visibility of detected targets. Goal: On-line techniques to decide how robots should move to minimise chance of targets moving out of sight. Technique: Use map to estimate motion of targets. ÊCompute next position of robots to minimise escape time for the targets. ËAllow dynamic exchanges of targets tracked among the robots.

16 Progress to Date

17 Achievements Report on models developed for representing environment, sensors, mobility, and motion plans. Implemented next-best view planner for constructing 2D model of an urban environment. Implemented target-finding planner in 2D for single robot with cone vision. Implementation of target-finding planner for single aerial robot. Developed algorithms for target-finding in 2D for a team of robots that maintain communication links. Implemented real-time planner for motion in the presence of moving obstacles.