A Practical, Decision-theoretic Approach to Multi-robot Mapping and Exploration

1. به نام خدا A Practical, Decision-theoretic Approach to Multi-robot Mapping and Exploration ارائه درس رباتيک توسط: صادق سليمان پور استاد درس : آقاي دكتر شيري تاريخ ارائه: 04/09/1385

2. Introduction • Dynamic Coordination Architecture • Design-theoretic Coordination • Partial Map Localization • Experiments • Conclusions and Future Research

3. Introduction • This approach uses an adapted version of particle filters • The risk of false-positive map matches is avoided by verifying match hypotheses using a rendezvous approach • Efficient exploration of an unknown environment • Exploration and map building for large teams of robots • Limited communication between robots • No assumptions about relative start locations of the robots • Dynamic assignment of processing tasks

4. Dynamic Coordination Architecture

5. Decision-theoretic Coordination

6. θdenote an assignment that determines which robot should move to which target • Cost: If the target is a frontier then the cost is given bythe minimum cost path from the robot’s position

7. Utilities: For simplicity, we assume that all robots have the same exploration capabilities, i.e. the utility only depend on the type of target and not the robot

8. Partial Map Localization

10. Implementation as particle filter

11. Partial resampling • In the basic particle filter, all samples are frequently resampled based on their accumulated weights • Unfortunately, in our context, such a resampling does not work since the weights of the samples may differ by orders of magnitude • So, samples are weighted by • As a result of this resampling procedure, all samples do not have the same weights, but carry the non-resampled weights with them into the next iteration. • A very important advantage of this approach is that samples outside the partial map are not deleted but tracked until they re-enter the map

13. After only short overlap, the best match is not yet correct. The summed probability of all samples inside the map is .497

14. The robot exits the map, but already determined the correct match. • Now, the probability of being inside the map dropped to 0.00037 , since all high-weight samples just exited the map

15. After moving about 50m outside the map, the robot returned and the match is correct (probability of this match is .799

20. Robots A and B start at unknown locations and explore independently. • The trajectories of A and B are shown as dotted and solid lines, respectively. • After some time, the robots reach positions Ia and Ib, and A estimates B’s location in its map. • The corresponding maps are shown in Ia and Ib. • The overlap between the two maps is not sufficient to create a hypothesis with probability above. Both robots keep exploring until, at positions IIa and IIb, A finds a very likely hypothesis for B’s position. • Both robots move to the meet point and verify the hypothesis. The maps are merged and the robots start coordinated exploration. • A moves to the left and B first moves into the small hallway in the lower part.

21. متشکرم ؟