1. دانشکده مهندسي کامپيوتر و فنآوري اطلاعات – دانشگاه صنعتي اميرکبير ( پلي تکنيک تهران ) A Practical, Decision-theoretic Approach to Multi-robot Mapping and Exploration توسط : صادق سليمان پور استاد درس : آقاي دكتر شيري ارائه درس رباتيک تاريخ ارائه : 04/09/1385 به نام خدا

2. 2 20 u Introduction u Dynamic Coordination Architecture u Design-theoretic Coordination u Partial Map Localization u Experiments u Conclusions and Future Research

3. 3 20 Introduction u This approach uses an adapted version of particle filters u The risk of false-positive map matches is avoided by verifying match hypotheses using a rendezvous approach u Efficient exploration of an unknown environment u Exploration and map building for large teams of robots n Limited communication between robots n No assumptions about relative start locations of the robots n Dynamic assignment of processing tasks

4. 4 20 Dynamic Coordination Architecture

5. 5 20 Decision-theoretic Coordination

6. 6 20 u θ denote an assignment that determines which robot should move to which target u Cost: If the target is a frontier then the cost is given by the minimum cost path from the robot’s position

7. 7 20 u 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. 8 20 Partial Map Localization

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10. 10 20 Implementation as particle filter

11. 11 20 Partial resampling u In the basic particle filter, all samples are frequently resampled based on their accumulated weights u Unfortunately, in our context, such a resampling does not work since the weights of the samples may differ by orders of magnitude u So, samples are weighted by u 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. u 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

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13. 13 20 u After only short overlap, the best match is not yet correct. The summed probability of all samples inside the map is.497

14. 14 20 u The robot exits the map, but already determined the correct match. u Now, the probability of being inside the map dropped to 0.00037, since all high-weight samples just exited the map

15. 15 20 u After moving about 50m outside the map, the robot returned and the match is correct (probability of this match is.799

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20. u Robots A and B start at unknown locations and explore independently. u The trajectories of A and B are shown as dotted and solid lines, respectively. u After some time, the robots reach positions Ia and Ib, and A estimates B’s location in its map. u The corresponding maps are shown in Ia and Ib. u 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. u Both robots move to the meet point and verify the hypothesis. The maps are merged and the robots start coordinated exploration. u A moves to the left and B first moves into the small hallway in the lower part.

21. 21 20 ؟ متشکرم