Location: EU Supported » PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning » Regression in Robotics - Approaches and Applications

Optimized Information Gatheringin Robotics and Sensor Networks

author:Andreas Krause, Computer Science Department, California Institute of Technology
published: Aug. 3, 2009,   recorded: June 2009,   views: 50
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Slides

Slides
0:00 Optimized Information Gathering in Robotics and Sensor Networks
0:02 Monitoring algal blooms
0:37 Monitoring rivers and lakes
1:27 Monitoring water networks
2:16 Information gathering problems
2:51 Related work
3:32 Running example
3:39 Monitoring as bayesian regression
4:19 Why is this useful?
4:56 Making observations -1
5:44 Making observations -2
5:57 A different outcome...
6:06 Value of information
7:25 Other example rewerd functions
7:42 Value of information
8:37 Optimizing Value of Information
9:05 Performance of greedy
9:34 Key observation
10:42 One reason submodelarity is useful
11:52 Competition
12:47 BWSN Competition results
13:27 What was the trick?
15:01 Challenges for environmental monitoring
15:47 Why do we need robustness?
16:37 How does the greedy algorithm do?
18:09 The SATURATE algorithm
19:20 Theoretical guarantees
19:36 Example: Lake monitoring
20:50 Comparisson with state of the art
21:44 Results on water networks
22:22 Informative path planning
23:20 The pSPIEL Algorithm
24:20 Guarantees for pSPIEL
24:35 pSPIEL Results
25:33 Structure in ML/AI problems
26:48 Conclusions

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