1. Collaborative Mobile Robots for High-Risk Urban Missions Report on Timeline, Activities, and Milestones
P. I.s: Leonidas J. Guibas and Jean-Claude Latombe
Computer Science Department
Stanford University
December 15, 1998
DARPA TMR Program

2. Timeline for Model Building
Computer Science Department, Stanford University

3. Timeline for Target Finding
Computer Science Department, Stanford University

4. Timeline for Target Tracking
Computer Science Department, Stanford University

5. Q2 - Map building: Next-Best View Techniques
Explanation: Next-best view technique for cooperative map-building with multiple robots (simulation).
Benefits:
2D map is used for deciding robot positions for complex sensing operations (Q4).
2D map can be used as workspace for target finding and tracking (Q3-Q7).
Cost: $64,225.
Computer Science Department, Stanford University

6. Q3 - Map building: localization uncertainty
Explanation: Next-best view technique for map building of interior terrain with real robot under large localization uncertainty.
Benefit:
Makes next-best view algorithm more robust for practical environments.
Generates more precise 2D maps for other tasks.
Cost: $66,500
Computer Science Department, Stanford University

7. Q3 - Target finding: extended sensory models
Explanation: Simulation of target finding planner with new sensory model.
Benefit:
Generates reliable motion plan that guarantees target cannot escape into already swept region.
Works even under restricted sensing conditions (e.g. cone of vision).
Used in Q5 for target finding techniques when there are not enough robots.
Cost: $64,105.

8. Q4 - Map building: art gallery techniques
Explanation: Randomized art-gallery technique to find a minimized set of locations allowing a camera-equipped robot to take pictures covering an entire environment.
Benefit:
Exploits 2D map constructed in Q2 and Q3 to compute robot positions.
Minimizes number of necessary complex sensing operations.
Cost: $61,950.
Computer Science Department, Stanford University

9. Q4 - Target Tracking: One Robot, One Target
Explanation: Target-tracking planner for one robot and one target, using a joystick robot as target.
Benefit:
Important step towards general goal of multiple robots tracking multiple targets (Q5-Q7).
Demonstrates key ideas used in target tracking.
Cost: $47,100.
Computer Science Department, Stanford University

10. Q5 - Target finding: non-guaranteed strategies
Explanation: Target finding strategy that minimizes the amount of space in which the targets may still hide, when there are not enough robots to reliably find all targets (simulation).
Benefit:
Guarantees targets are hiding in as small an area as possible.
Extends target finding technique when there are enough robots (developed in Q3).
Cost: $64,950
Computer Science Department, Stanford University

11. Q5 - Target finding with communication maintenance
Explanation: Target-finding technique where the robots maintain a communication network.
Benefit:
Robots protect each other.
Robots can share information through communication channels.
Cost: The cost of this milestone is integrated into the costs of the other target-finding milestones.
Note: This is an additional accomplishment that is not in the contract.
Computer Science Department, Stanford University

12. Q5 - Target Tracking: Several Robots, Several Targets
Explanation: Target-tracking planner allowing N robots to track Q robots, with N and Q roughly the same (simulation only).
Benefit:
Allows multiple robots to track multiple targets.
Permits robots to exchange tracked targets.
Cost: $46,500.
Computer Science Department, Stanford University

13. Q6 - Target Finding: One Robot
Explanation: Demonstration of target-finding capability with one robot in a real indoor environment.
Benefit:
Demonstrates effectiveness of target-finding technique developed in Q3.
Essential step towards demonstration of target finding with two robots in Q7.
Cost: $63,800.
Computer Science Department, Stanford University

14. Q6 - Target Tracking: Two Robots, One Target
Explanation: Target-tracking planner for two robots and one human target. If required, perception will be simplified by having the target carry an easily distinguishable pattern.
Benefit:
Demonstrates how multiple robots can coordinate to track one target.
Uses target tracking algorithm for several robots and several targets (Q5).
Cost: $46,700.
Computer Science Department, Stanford University

15. Q6 - Real-time path planning
Explanation: Planner that computes path in real time even in the presence of moving obstacles.
Benefit:
Can be incorporated in all planning techniques used for map building, target finding, and target tracking.
Real-time property does not affect speed of these techniques.
Cost: We hope to be able to support this milestone with separate funding (NSF fellowship).
Note: This is an additional accomplishment that is not in the contract.
Computer Science Department, Stanford University

16. Q7 - Target Finding: Two or More Robots
Explanation: Target-finding capability with two or more robots in a real indoor environment.
Benefit:
Demonstrates applicability of target-finding technique developed in Q3.
Is used to find targets in more complex buildings than earlier target finder (demonstrated in Q6).
Cost: $63,585.
Computer Science Department, Stanford University

17. Q7 - Target Tracking: Two or More Robots and Targets
Explanation: Target-tracking software for two (or more) robots and two (or more) human targets.
Benefit:
Demonstrates technique for multiple robots tracking multiple targets.
Allows robots to exchange targets being tracked.
Cost: $48,100.
Accomplishments leveraged: Robot controller that adjusts for delay (over network).
Computer Science Department, Stanford University

18. Q8/9 - Integrated Demonstration
Explanation: Demonstration of an integrated approach to implementing map building, target finding, and target-tracking capabilities in a government-approved scenario.
Benefit:
Final integration of techniques developed in the contract.
Final documentation and reporting of accomplishments during the TMR program.
Cost: $112,485.
Computer Science Department, Stanford University

19. Computer Science Department, Stanford University
Financial Summary
Changes to original contract: none.
Total funded: $188,000.
Amount spent (as of 11/30/98): $111,716.
Amount available (as of 12/1/98): $76,284.
We expect the current funding to enable us to continue the research through 1/31/99.
Computer Science Department, Stanford University

20. Interaction with other TMR Contractors
Kurt Konolige (SRI): Discussions to enable our robots to have omnidirectional vision by equipping them with SRI’s spherical mirror.
Greg Hager (Yale): Discussions to use Yale’s visual tracking algorithms in our motion-planning algorithms.
Computer Science Department, Stanford University