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E-raamat: Sensory Systems for Robotic Applications

Edited by (Northeastern University, Bendable Electronics and Sustainable Technologies (BEST) Research Group, Electrical and Computer Engineering Department, Boston, USA), Edited by (Technical University of Munich, Germany), Edited by (University of Derby, UK)
  • Formaat: PDF+DRM
  • Sari: Control, Robotics and Sensors
  • Ilmumisaeg: 15-Dec-2022
  • Kirjastus: Institution of Engineering and Technology
  • Keel: eng
  • ISBN-13: 9781849199490
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  • Formaat: PDF+DRM
  • Sari: Control, Robotics and Sensors
  • Ilmumisaeg: 15-Dec-2022
  • Kirjastus: Institution of Engineering and Technology
  • Keel: eng
  • ISBN-13: 9781849199490
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Robots have come a long way thanks to advances in sensing and computer vision technologies and can be found today in healthcare, medicine and industry. Researchers have been looking at providing them with senses such as the ability to see, smell, hear and perceive touch in order to mimic and interact with humans and their surrounding environments.



Topics covered in this edited book include various types of sensors used in robotics, sensing schemes (e-skin, tactile skin, e-nose, neuromorphic vision and touch), sensing technologies and their applications including healthcare, prosthetics, robotics and wearables.



This book will appeal to researchers, scientists, engineers, and graduate and advanced students working in robotics, sensor technologies and electronics, and their applications in robotics, haptics, prosthetics, wearable and interactive systems, cognitive engineering, neuro-engineering, computational neuroscience, medicine and healthcare technologies.
About the Editors xi
1 Development of tactile sensors for intelligent robotics research
1(26)
Yoshiyuki Ohmura
Akihiko Nagakubo
Yasuo Kuniyoshi
1.1 Introduction
1(2)
1.2 Developed tactile sensors and implementations
3(10)
1.2.1 Conformable and scalable tactile sensor for 3D-curved surfaces
3(3)
1.2.2 High-density tactile sensor for bare-hand-like sensor gloves
6(4)
1.2.3 Stretchable tactile sensor based on inverse problem analysis
10(3)
1.3 Tactile sensing and robotics: future direction
13(7)
1.3.1 Automaton model
14(2)
1.3.2 State-action model
16(3)
1.3.3 Future direction
19(1)
1.4 Conclusion
20(7)
References
23(4)
2 Developmental soft robotics
27(28)
Luca Scimeca
Fumiya Iida
2.1 Introduction
27(2)
2.2 Bio-inspired soft robotics
29(4)
2.2.1 Soft materials and soft actuation
29(3)
2.2.2 Soft robot control, simulation and learning
32(1)
2.3 Developmental soft robotics
33(9)
2.3.1 Facets of development
33(2)
2.3.2 Soft robotics and developmental time scales
35(2)
2.3.3 Design principles
37(4)
2.3.4 Ontogenetics and adaptivity
41(1)
2.4 Challenges and perspectives
42(13)
2.4.1 Evolutionary robotics
43(1)
2.4.2 Complexity and scalability
44(1)
2.4.3 Learning through the body
44(1)
References
45(10)
3 Three-axis tactile sensor using optical transduction mechanism
55(20)
Masahiro Ohka
Hanafiah Yussof
3.1 Introduction
55(3)
3.2 Design concept of the optical three-axis tactile sensor
58(4)
3.2.1 Basic principle
58(1)
3.2.2 Conical-columnar feeler-type optical three-axis tactile sensor
59(2)
3.2.3 Tracking-centroid-movement-type optical three-axis tactile sensor
61(1)
3.3 Actual design of the optical three-axis tactile sensor
62(5)
3.3.1 Aluminum-dome type
62(1)
3.3.2 Rubber-dome type
62(3)
3.3.3 Tracking-contact-area-movement type
65(2)
3.4 Applications
67(3)
3.4.1 Tasks achieved by three-axis tactile sensing
67(1)
3.4.2 Picking-up and counting paper
68(2)
3.4.3 Human-robot communication
70(1)
3.5 Conclusion
70(5)
References
71(4)
4 Strain sensors for soft robotic applications
75(16)
Oliver Ozioko
Ravinder Dahiya
4.1 Introduction
75(1)
4.2 Mechanisms for strain sensors
76(2)
4.2.1 Strain sensing based on intrinsic properties of materials and tunneling effect
76(1)
4.2.2 Disconnection and microcrack propagation mechanism
77(1)
4.3 Classification of strain sensors
78(6)
4.3.1 Piezoresistive strain sensors
78(3)
4.3.2 Capacitive-type strain sensors
81(2)
4.3.3 Triboelectric-type strain sensors
83(1)
4.4 Conclusion
84(7)
References
85(6)
5 Neuromorphic principles for large-scale robot skin
91(34)
Florian Bergner
Emmanuel Dean-Leon
Gordon Cheng
5.1 Classical engineering approaches are reaching their limits
91(2)
5.1.1 Motivations for robot skin
91(1)
5.1.2 Robot skin
92(1)
5.1.3 Challenges and limits of robot skin
92(1)
5.2 Biology employs a toolbox full of optimized principles
93(4)
5.2.1 Skin receptors are tuned to sense specific stimulus features
93(1)
5.2.2 Skin receptors transduce stimuli features to binary action potentials
94(1)
5.2.3 Skin information is encoded by different neural codes
94(1)
5.2.4 Skin information ascends somatotopically ordered
95(1)
5.2.5 Skin information is structured and processed hierarchically
95(1)
5.2.6 The cognitive where
96(1)
5.2.7 The cognitive what
97(1)
5.3 Biological principles are the key to large-scale robot skin
97(1)
5.3.1 Neuromorphic event-driven sensors
97(1)
5.3.2 Neuromorphic information representation in hierarchical structures
98(1)
5.4 Neuromorphic systems realize biological principles
98(14)
5.4.1 Neuromorphic event-driven vision has been engineered first
98(3)
5.4.2 The neuromorphic AER is a standard for transmitting events
101(3)
5.4.3 The send-on-delta principle allows event-driven transmission and processing in synchronous systems
104(1)
5.4.4 Neuromorphic event-driven skin is under development
105(3)
5.4.5 Neuromorphic information representations mimic the primary somatosensory cortex
108(2)
5.4.6 Neuromorphic parallel information streams of the cognitive where and what
110(2)
5.5 The realization of an event-driven large-scale robot skin system
112(13)
5.5.1 Robot skin system
112(3)
5.5.2 Event-driven reactive skin control
115(2)
5.5.3 The benefits
117(1)
References
117(8)
6 Soft three-axial tactile sensors with integrated electronics for robot skin
125(48)
Alexander Schmitz
Sophon Somlor
Tito Pradhono Tomo
Lorenzo Jamone
Richard Sahala Hartanto
Harris Kristanto
Wai Keat Wong
Jinsun Hwang
Alexandre Sarazin
Shuji Hashimoto
Shigeki Sugano
6.1 Introduction
125(2)
6.2 Related work
127(7)
6.2.1 Piezoelectric-based sensors
127(1)
6.2.2 Optical-based sensors
128(1)
6.2.3 Hall-effect-based sensors
128(2)
6.2.4 PSECR-based sensors
130(1)
6.2.5 Piezoresistive-based sensors
130(2)
6.2.6 Capacitive-based sensors
132(2)
6.2.7 MEMS-based sensors
134(1)
6.2.8 Proximity detection
134(1)
6.2.9 Summary of related work
134(1)
6.3 Three-axis capacitive soft skin sensor
134(20)
6.3.1 Concept
134(1)
6.3.2 Implementation
135(5)
6.3.3 Experiments
140(12)
6.3.4 Summary
152(2)
6.4 Three-axis Hall-effect sensors
154(12)
6.4.1 Concept
154(1)
6.4.2 Implementation
154(3)
6.4.3 Experiment
157(9)
6.5 Conclusion
166(7)
References
167(6)
7 A review of tactile sensing in e-skin, wearable device, robotic, and medical service
173(28)
Jian Hu
Junghwan Back
Hongbin Liu
7.1 Introduction
174(1)
7.2 Hardware of various tactile sensing technologies
175(7)
7.2.1 Resistive
178(1)
7.2.2 Piezoelectric
178(1)
7.2.3 Capacitive
178(1)
7.2.4 Optical
179(1)
7.2.5 Magnetic field
180(1)
7.2.6 Quantum tunneling composite
180(1)
7.2.7 Triboelectric effect
181(1)
7.2.8 Field-effect transistor
181(1)
7.3 Design criterion and performance index of a tactile sensing system
182(1)
7.4 Applications of tactile sensing technologies
183(4)
7.4.1 Development trend of tactile sensing technologies in e-skin
183(1)
7.4.2 Development trend of tactile sensing technologies in a wearable device
184(1)
7.4.3 Development trend of tactile sensing technologies in robotic
185(1)
7.4.4 Development trend of tactile sensing technologies in medical service
186(1)
7.5 Challenges and discussion
187(14)
7.5.1 Standardization of fabrication process
187(1)
7.5.2 Data transmission of high-density tactile sensing elements
188(1)
7.5.3 Fault tolerance and autocalibration
188(1)
7.5.4 Layout of sensing elements on an irregular 3D
189(1)
References
189(12)
8 Neuroengineering approaches for cognitive hearing technology
201(12)
Tobias Reichenbach
8.1 Introduction
201(1)
8.2 General aspects of neurofeedback in a hearing aid
202(2)
8.3 Decoding selective attention to speech from the auditory brainstem response to the temporal fine structure
204(2)
8.4 Decoding speech comprehension from cortical tracking of speech features
206(1)
8.5 Enhancing speech comprehension through transcranial electric stimulation
207(2)
8.6 Summary
209(4)
References
209(4)
9 Mobile robot olfaction state-of-the-art and research challenges
213(36)
Lino Marque
Hugo Magalnaes
Rui Baptista
Joao Macedo
9.1 Introduction
213(1)
9.2 Odour dispersion
214(2)
9.3 Artificial olfaction
216(9)
9.3.1 Gas sensing
217(6)
9.3.2 Flow sensing
223(2)
9.4 Odour source localisation
225(6)
9.4.1 Searching odours
225(2)
9.4.2 Tracking odour plumes
227(3)
9.4.3 Source declaration
230(1)
9.5 Learning in mobile robot olfaction
231(5)
9.5.1 Source-term estimation
231(1)
9.5.2 Policy search
232(4)
9.6 Open challenges
236(13)
9.6.1 Artificial olfaction
236(1)
9.6.2 Odour source localisation
237(1)
9.6.3 Learning to locate odour sources
238(1)
References
239(10)
10 Vision sensors for robotic perception
249(16)
Shan Luo
Daniel Fernandes Gomes
Jiaqi Jiang
Guanqun Cao
10.1 Introduction
249(3)
10.2 RGB cameras for robotic perception
252(1)
10.3 Stereo cameras
253(1)
10.4 Event cameras
253(1)
10.4.1 Hardware
253(1)
10.4.2 Applications in robotics
254(1)
10.5 Depth cameras
254(1)
10.6 Vision sensors for other modalities
255(5)
10.6.1 Marker-based sensors
256(1)
10.6.2 Image-based sensors
256(4)
10.7 Conclusions
260(5)
Acknowledgements
261(1)
References
261(4)
11 Audio sensors
265(34)
Kazuhiro Nakadai
Hirofumi Nakajima
Hiroshi G. Okuno
11.1 Audio sensors
265(3)
11.1.1 Airborne microphones
266(1)
11.1.2 Microphones for underwater
267(1)
11.1.3 Microphones for underground and structures
267(1)
11.1.4 Microphones for biological bodies
267(1)
11.2 Microphones for audible sounds
268(6)
11.2.1 Indicators for microphone characteristics
271(3)
11.3 Microphone array
274(4)
11.4 Robot audition
278(1)
11.5 Acoustic signal processing
279(4)
11.6 OSS for robot audition
283(2)
11.7 Applications of robot audition
285(6)
11.7.1 Prince Shotoku robot
285(1)
11.7.2 Drone audition system
286(3)
11.7.3 VR system based on bird song scene analysis
289(2)
11.8 Summary
291(8)
References
291(8)
12 Audio and gas sensors
299(12)
Caleb Rascon
12.1 Audio sensors
299(12)
12.1.1 Hardware
299(4)
12.1.2 Software
303(3)
References
306(5)
Index 311
Ravinder Dahiya is Professor in Electrical and Computer Engineering Department at Northeastern University, Boston, USA. His group (Bendable Electronics and Sustainable Technologies (BEST)) conducts fundamental research in electronic skin, flexible printed electronics and their applications in robotics, prosthetics, wearables, augmented/virtual reality and similar interactive systems. He has authored or co-authored more than 500 publications, books and submitted/granted patents and disclosures. He has led or contributed to many international projects. Prof. Dahiya is President of IEEE Sensors Council. He is the Founding Editor-in-Chief of IEEE Journal on Flexible Electronics (J-FLEX). He has been recipient of EPSRC Fellowship, Marie Curie Fellowship and Japanese Monbusho Fellowship. He has received several awards, including Technical Achievement award from IEEE Sensors Council, Young Investigator Award from Elsevier, and 12 best journal/conference paper awards as author/co-author. He is Fellow of IEEE and the Royal Society of Edinburgh.



Oliver Ozioko is a lecturer in electrical and electronic engineering at the University of Derby, UK. Prior to joining the University of Derby, he worked as a postdoctoral researcher at the University of Glasgow. He holds a PhD Degree in Electrical and Electronic Engineering from the University of Glasgow. His research focuses on sensors and intelligent systems, electronic skin, haptics, assistive technologies, smart systems, as well as self-powered wearable and portable systems. He has authored or co-authored over 29 technical publications. He is the 2023 YP chair for IEEE Sensors council.



Gordon Cheng is chair professor and director of the Institute for Cognitive Systems and is the coordinator of the Center of Competence Neuro-Engineering, Technical University of Munich, Germany. He is also the founding director of the Elite Master of Science program in Neuroengineering (MSNE) of the Elite Network of Bavaria, Germany. For more than 20 years, he has made pioneering contributions in humanoid robotics, neuroengineering and artificial intelligence. He founded the department of humanoid robotics and computational neuroscience at the Institute for Advanced Telecommunications Research in Kyoto, Japan, where he was department head from 2003 to 2008. In addition, from 2007 to 2008 he was a project manager at the National Institute of Information and Communications Technology, Japan, and the Japanese Science and Technology Agency, where he was responsible for the Computational Brain project (2004-2008). He is the co-inventor of 20 patents and co-authored over 350 technical publications. He was acknowledged as an IEEE Fellow in 2017 for his "contributions in humanoid robotic systems and neurorobotics". He holds a Doctorate Degree in Systems Engineering from The Australian National University.
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