S.N.A.K.E.: A Dynamically Reconfigurable Artificial Sensate Skin ...

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3.4.3 Communications unit . . . . . . . . . . . . . . . . . . . . . . . . . . 76 4 Software 79 4.1 Firmware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 4.1.1 Bootloader . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.1.2 Sampling Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 4.1.3 Node Application Code . . . . . . . . . . . . . . . . . . . . . . . . . 89 4.1.4 Brain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 4.2 PC Graphical User Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 4.2.1 Network detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 4.2.2 Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 4.2.3 Sampling and Display . . . . . . . . . . . . . . . . . . . . . . . . . . 97 4.2.4 Other Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 4.3 Communications Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 4.3.1 I2C Backbone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 4.3.2 Peer to Peer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 4.3.3 USB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 5 Analysis of Results and Evaluation 105 5.1 Mechanical Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 5.1.1 Substrate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 5.1.2 Connections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 5.1.3 Pressure Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 5.2 Sensor Data and Sensing Ranges . . . . . . . . . . . . . . . . . . . . . . . . 109 5.2.1 Skin Patches as Data-extraction Devices . . . . . . . . . . . . . . . . 109 5.2.2 Skin Patches as Stand-alone Devices . . . . . . . . . . . . . . . . . . 130 5.3 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 5.3.1 Network Performance . . . . . . . . . . . . . . . . . . . . . . . . . . 133 5.3.2 Power Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 5.3.3 Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 6 Conclusions and Future Work 141 6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 6.2 Possible Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 6.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 A Abbreviations and Symbols 147 B Bill of Materials 149 C Schematics and PCB Layouts 151 D Firmware 167 E PC Code 183 Bibliography 207 10
List of Figures 2-1 Created by MIT Media Lab’s Responsive Environments group, Tribble is a Distributed Sensor Network that works like an Artificial Sensate Skin . . . 25 2-2 Leonardo’s Hand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2-3 Pushpin Computing Platform . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2-4 Dense sensor arrays as artificial skins . . . . . . . . . . . . . . . . . . . . . . 29 2-5 Dense sensor arrays as artificial skins . . . . . . . . . . . . . . . . . . . . . . 31 2-6 Types of Flex circuits[46] . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3-1 S.N.A.K.E. Skin Network Topology . . . . . . . . . . . . . . . . . . . . . . . 37 3-2 Skin Patch modes of operation . . . . . . . . . . . . . . . . . . . . . . . . . 38 3-3 I 2 C Routing pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3-4 I 2 C Active pull-up devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3-5 Flex Nodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3-6 Node substrate material stack . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3-7 Flex circuit cost as a function of fabrication complexity[46] . . . . . . . . . 47 3-8 Rigid node prototype . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3-9 Skin Patch mechanical terminations . . . . . . . . . . . . . . . . . . . . . . 50 3-10 Node with electrical components being flexed . . . . . . . . . . . . . . . . . 51 3-11 Routing techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3-12 Power components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3-13 Microcontroller with programming header . . . . . . . . . . . . . . . . . . . 57 3-14 Strain gage layer prototype . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 3-15 Wheatstone bridge used for Strain gage conditioning . . . . . . . . . . . . . 65 3-16 QTC response curve for a QTC pressure sensor[52] . . . . . . . . . . . . . . 66 3-17 QTC pressure sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3-18 QTC pressure sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 3-19 Toshiba TPS851/52 Ambient Light Sensors . . . . . . . . . . . . . . . . . . 71 3-20 Brain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 4-1 Bootloader memory map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 4-2 Bootloader Flow Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 4-3 Sampling timing example . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 4-4 GUI Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 4-5 Automatic configuration of Brains . . . . . . . . . . . . . . . . . . . . . . . 95 4-6 GUI Programming Environment . . . . . . . . . . . . . . . . . . . . . . . . 96 11
Page 1: Author S.N.A.K.E.: A Dynamically Re
Page 5: S.N.A.K.E.: A Dynamically Reconfigu
Page 9: Table of Contents Abstract 3 Acknow
Page 13: E-1 OpenGL Control Header File . .
Page 17 and 18: Chapter 1 Introduction “Imaginati
Page 19 and 20: The current project, named S.N.A.K.
Page 21: Hardware Each Skin Patch is compose
Page 24 and 25: living tissue. Particularly in the
Page 26 and 27: Figure 2-2: Leonardo’s Hand great
Page 28 and 29: Artificial Sensate Skins elsewhere
Page 30 and 31: 2.1.2 Ultra-dense Sensor Arrays Thi
Page 32 and 33: 2.2 Flexible Circuits Although, not
Page 34 and 35: 2.2.2 Other Technologies Figure 2-6
Page 36 and 37: human skin. The following design pr
Page 38 and 39: (a) Skin Patches as data extraction
Page 40 and 41: Figure 3-3: I 2 C Routing pattern T
Page 42 and 43: 3.2.2 Peer to Peer As we already me
Page 44 and 45: 5. Audio: by adding a MEMS micropho
Page 46 and 47: Table 3.1: Material thicknesses and
Page 48 and 49: (a) Node Front (b) Node Back Figure
Page 50 and 51: Figure 3-9: Skin Patch mechanical t
Page 52 and 53: used, which only further complicate
Page 54 and 55: 8. Teardrops and filleting were add
Page 56 and 57: (a) 7.4V, 2.2mAh Li-ion Battery (b)
Page 58 and 59: in more detail in the Results Chapt
Page 60 and 61:
has a 30mA forward current with a f
Page 62 and 63:
Figure 3-14: Strain gage layer prot
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an unbalanced Wheatstone bridge, fr
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Figure 3-16: QTC response curve for
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(a) Prototype boards used to calibr
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whisker is epoxied to the package o
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Sound The only sensing modality add
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Figure 3-20: Brain regular PCB fabr
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3.4.3 Communications unit The Commu
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Chapter 4 Software “A Scientist d
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Networks. Why is this relevant? Mai
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Figure 4-1: Bootloader memory map C
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Bit Flag Name Description 0x01 FILE
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Figure 4-3: Sampling timing example
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We also mentioned before that there
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Code Instruction Description 0xA0 R
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Component Description 1 Indicates t
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last chapter. Figure 4-5: Automatic
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An important point to stress is tha
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(a) Rotation control (b) Zooming co
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4.3 Communications Software The las
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4.3.2 Peer to Peer Peer to peer com
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Chapter 5 Analysis of Results and E
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Figure 5-1: Stress concentration po
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of the assembled skin patch don’t
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Node SG1 SG2 R(X) R(Y) Δ(X) Δ(Y)
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Or, ∆R = R3 R3 R3 + RC (5.2) Howe
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R3 Microstrain 162Ω −99.7337969
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Figure 5-3: Strain Gage electronic
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(a) Data from both strain gages (b)
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(a) Pressure data being extracted f
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many of these did not work as expec
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Data plots were also extracted for
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(a) Temperature sensor data plots (
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Figure 5-12: Raw microphone data wi
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(a) Activity sensor data (b) Whiske
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5.3 Performance Metrics We have pre
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(a) I 2 C data when a 1500Ωs resi
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Component # per node Current consum
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Finally, since power is readily ava
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Chapter 6 Conclusions and Future Wo
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surface can be used to sense pressu
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Implement P2P Communications: curre
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Appendix A Abbreviations and Symbol
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Appendix B Bill of Materials 149
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Appendix C Schematics and PCB Layou
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1 1 2 2 3 3 4 4 D D C C B B A A Tit
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Amp1 P0Amp101 P0Amp102 P0Amp103 P0A
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P0Cin02 P0East06 P0North06 P0South0
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Cain P0C ain01 P0Cain02 L1 P0L101 P
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Figure C-10: Node Bottom Overlay 16
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4 3 2 1 A A Brain Communications Ac
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C3v P0C3v01 P0C3v02 Cavcc P0Cavcc01
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Appendix D Firmware 167
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C:\Documents and Settings\Gerardo\M
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Appendix E PC Code 183
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1 c:\Documents and Settings\Gerardo
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12 c:\Documents and Settings\Gerard
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Bibliography [1] John C. Ansel. Ski
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[33] Joseph Jacobson, B Comiskey, C
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[68] Vilis Tutis. The physiology of
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