Gas Source Declaration With a Mobile Robot
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1 Gas Source Declaration With a Mobile Robot Achim Lilienthal, Holger Ulmer, Holger Fröhlich, Andreas Stützle, Felix Werner, Andreas Zell University of Tübingen, WSI
2 Contents 1) Applications for Gas-Sensitive Mobile Robots 2) Gas Source Localisation 3) Gas Source Declaration Strategy 4) Experimental Setup 5) Classification (Support Vector Machine) 6) Results 7) Summary & Outlook 2
3 1 Gas-Sensitive Mobile Robots the catastropy in Baden-Baden
4 1 Gas-Sensitive Mobile Robots 4
5 1 Application Domains dedicated systems security, surveillance: "electronic watchman" rescue robots additional benefit for available robots smell a leaking gas pipe detect a fire at its initial stage (CO) monitor pollutants in the environment 5
6 2 Gas Source Localisation sub-tasks gas finding gas source tracing gas source declaration main difficulties state-of-the art gas sensors turbulent gas distribution no analytical model available 6
7 2 Gas Source Localisation gas distribution in a real-world environment diffusion convection turbulence Smyth & Moum
8 3 Gas Source Declaration Strategy gas source declaration without using additional sensors using general characteristics no analytical model available rotation maneouvre 90 left, 180 right, 90 left 8 sectors with 45 8
9 3 Gas Source Declaration Strategy gas source declaration without using additional sensors using general characteristics no analytical model available rotation maneouvre 90 left, 180 right, 90 left 8 sectors with 45 9
10 3 Data Pre-Processing feature extraction mean and/or standard deviation (µ, σ, µσ) different sensor combinations (7 sensors) linear mapping to [0,1] vertical horizontal f 11, f 12, f 13, f 14, f 15, f 16, f 17, f 18 f 21, f 22, f 23, f 24, f 25, f 26, f 27, f f N1, f N2, f N3, f N4, f N5, f N6, f N7, f N8 10
11 3 Data Pre-Processing 14 sensor combinations 1 sensor: 2 sensors: 3 sensors: 4 sensors: 11
12 3 Data Pre-Processing 14 sensor combinations 5 sensors: 7 sensors: 12
13 4 Experimental Setup Robot, Gas Source "Arthur" (ATRV-Jr.) footprint = 80 x 65 cm, height = 55 cm Sensors 7 metal oxide gas sensors odometry laser range scanner Gas Source bowl with Whiskey (D = 14cm) 13
14 4 Experimental Setup - Environment office room uncontrolled environment 14
15 4 Experimental Setup - Environment office room 3 different positions of the gas source 15
16 4 Experimental Setup - Environment office room "direct in front of the source" pos. examples 16
17 4 Experimental Setup - Environment d 5 cm neg. examples 4 neg./ 4 pos. trials for each distance 1056 trials 17
18 5 Classification positive examples (+1) mean value (µ) 18
19 5 Classification negative examples mean value (µ) 19
20 5 Classification negative examples mean value (µ) 20
21 5 Classification negative examples mean value (µ) 21
22 5 Classification - Machine Learning Support Vector Machine (SVM) optimal separating hyperplane in the feature space maximise margin between two classes transformation implicitely done by kernel functions kernel = similarity measure between feature vectors learning quadratic optimisation problem 22
23 5 Classification - Machine Learning Support Vector Machine (SVM) RBF kernel SVM parameters kernel parameter γ penalty parameter C 23
24 6 Results grid search γ = 2 m with m = 6, 5.75,, 5.75, 6 C = 2 n with n = 3, 2.75,, 8.75, 9 cross-validation hit rate 5-fold cross-validation (C*,γ*) max. cross.-val. hit rate train predict
25 6 Results dependency on the distance to the gas source distinguish d = 0 from d d ns different distances d ns d ns = 60 cm d = 60/80/100 cm d ns = 80 cm positive examples negative examples 25
26 6 Results vertical normalisation, (µσ), HR [%] d ns = 60 cm d ns = 80 cm HR = 89.5 % 26 d ns [cm]
27 6 Results horizontal normalisation, (µσ), HR [%] d ns = 60 cm d ns = 80 cm HR = 78.6 % 27 d ns [cm]
28 6 Results comparison with threshold classifier HR [%] best, vert. best, hor. d ns = 60 cm d ns = 80 cm 75.5 % 28 d ns [cm]
29 6 Results roughly constant plateau HR [%] best, vert. best, hor. 29 d ns [cm]
30 6 Results comparison of sensor configurations rel. HR [%] 30
31 7 Summary gas source declaration method introduced based on gas sensor measurements only machine learning for classification demonstration of the feasibility of the approach analysis of the classification performance high classification rates with SVM >> simple threshold classifier 31
32 7 Outlook maneuovre optimisation optimal rotational speed other maneuvres feature selection optimal sensor location regression Bayesian learning 32
33
34 Gas Source Declaration With a Mobile Robot Thank you! Achim Lilienthal, Holger Ulmer, Holger Fröhlich, Andreas Stützle, Felix Werner, Andreas Zell University of Tübingen, WSI
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