Sensory Systems for Robotic Applications

Description

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. Contents About the Editors 1 Development of tactile sensors for intelligent robotics research 1.1 Introduction 1.2 Developed tactile sensors and implementations 1.2.1 Conformable and scalable tactile sensor for 3D-curved surfaces 1.2.1.1 Ability to fit on 3D-curved surfaces due to branch-shaped sheet 1.2.1.2 Customizability by free cutting 1.2.1.3 Communication networks and modularization 1.2.1.4 Pressure sensing by photo-reflector 1.2.1.5 Examples of implementation 1.2.2 High-density tactile sensor for bare-hand-like sensor gloves 1.2.2.1 Design and implementation 1.2.2.2 Consistency with mass production 1.2.2.3 Contact resistance method 1.2.2.4 Toward new applications using tactile sensing 1.2.3 Stretchable tactile sensor based on inverse problem analysis 1.2.3.1 Electrical impedance tomography 1.2.3.2 Conductive rubber sheet 1.2.3.3 Conductive knit fabric 1.2.3.4 Pressure-sensitive but stretch-insensitive conductor 1.2.3.5 Issues and future prospects 1.3 Tactile sensing and robotics: future direction 1.3.1 Automaton model 1.3.2 State-action model 1.3.3 Future direction 1.4 Conclusion References 2 Developmental soft robotics 2.1 Introduction 2.2 Bio-inspired soft robotics 2.2.1 Soft materials and soft actuation 2.2.2 Soft robot control, simulation and learning 2.3 Developmental soft robotics 2.3.1 Facets of development 2.3.1.1 Incremental process and self-organization 2.3.1.2 Degrees of freedoms, freezing and freeing 2.3.1.3 Self-exploration and spontaneous activity 2.3.1.4 Intrinsic motivation 2.3.1.5 Categorization 2.3.1.6 Morphology and morphological computation 2.3.1.7 Sensory-motor coordination 2.3.1.8 Body schema 2.3.2 Soft robotics and developmental time scales 2.3.3 Design principles 2.3.3.1 Functional morphology and morphological computation 2.3.3.2 Soft system–environment interactions 2.3.3.3 Sensor morphology and soft perception 2.3.4 Ontogenetics and adaptivity 2.3.4.1 Adaptation and growth 2.3.4.2 Tool use and extended phenotype 2.4 Challenges and perspectives 2.4.1 Evolutionary robotics 2.4.2 Complexity and scalability 2.4.3 Learning through the body References 3 Three-axis tactile sensor using optical transduction mechanism 3.1 Introduction 3.2 Design concept of the optical three-axis tactile sensor 3.2.1 Basic principle 3.2.2 Conical-columnar feeler-type optical three-axis tactile sensor 3.2.3 Tracking-centroid-movement-type optical three-axis tactile sensor 3.3 Actual design of the optical three-axis tactile sensor 3.3.1 Aluminum-dome type 3.3.2 Rubber-dome type 3.3.3 Tracking-contact-area-movement type 3.4 Applications 3.4.1 Tasks achieved by three-axis tactile sensing 3.4.2 Picking-up and counting paper 3.4.3 Human-robot communication 3.5 Conclusion References 4 Strain sensors for soft robotic applications 4.1 Introduction 4.2 Mechanisms for strain sensors 4.2.1 Strain sensing based on intrinsic properties of materials and tunneling effect 4.2.2 Disconnection and microcrack propagation mechanism 4.3 Classification of strain sensors 4.3.1 Piezoresistive strain sensors 4.3.2 Capacitive-type strain sensors 4.3.3 Triboelectric-type strain sensors 4.4 Conclusion References 5 Neuromorphic principles for large-scale robot skin 5.1 Classical engineering approaches are reaching their limits 5.1.1 Motivations for robot skin 5.1.2 Robot skin 5.1.3 Challenges and limits of robot skin 5.2 Biology employs a toolbox full of optimized principles 5.2.1 Skin receptors are tuned to sense specific stimulus features 5.2.2 Skin receptors transduce stimuli features to binary action potentials 5.2.3 Skin information is encoded by different neural codes 5.2.4 Skin information ascends somatotopically ordered 5.2.5 Skin information is structured and processed hierarchically 5.2.5.1 Topographical features 5.2.5.2 Surface structural features (texture) 5.2.5.3 Geometrical and dynamical features 5.2.6 The cognitive where 5.2.7 The cognitive what 5.3 Biological principles are the key to large-scale robot skin 5.3.1 Neuromorphic event-driven sensors 5.3.2 Neuromorphic information representation in hierarchical structures 5.4 Neuromorphic systems realize biological principles 5.4.1 Neuromorphic event-driven vision has been engineered first 5.4.1.1 The dynamic vision sensor 5.4.1.2 The asynchronous time-based vision sensor (ATIS) 5.4.1.3 The dynamic and active vision pixel sensor 5.4.1.4 Applications prove the efficiency of neuromorphic vision sensors 5.4.2 The neuromorphic AER is a standard for transmitting events 5.4.2.1 The AER 5.4.2.2 AER for distributed sensors and processing 5.4.3 The send-on-delta principle allows event-driven transmission and processing in synchronous systems 5.4.3.1 The send-on-delta principle 5.4.3.2 Comparison of SoDP with AER 5.4.4 Neuromorphic event-driven skin is under development 5.4.4.1 Transduction of tactile stimuli to events in quasi-digital AER 5.4.4.2 Neuromorpic event-driven tactile sensors generate events in AER 5.4.4.3 Neuromorphic event-driven tactile sensors with AER for large-scale robot skin 5.4.4.4 Neuromorphic force sensors with high spatiotemporal resolution using SoDP 5.4.5 Neuromorphic information representations mimic the primary somatosensory cortex 5.4.5.1 Robot skin systems structure tactile information somatotopically 5.4.5.2 Real-time robot skin systems with generalized information representations 5.4.6 Neuromorphic parallel information streams of the cognitive where and what 5.4.6.1 The where pathway 5.4.6.2 The what pathway 5.4.6.3 The connection of the where and the what pathway 5.5 The realization of an event-driven large-scale robot skin system 5.5.1 Robot skin system 5.5.1.1 Robot skin 5.5.1.2 Structural self-calibration 5.5.1.3 Event-driven robot skin 5.5.1.4 Neuromorphic skin system 5.5.2 Event-driven reactive skin control 5.5.3 The benefits References 6 Soft three-axial tactile sensors with integrated electronics for robot skin 6.1 Introduction 6.2 Related work 6.2.1 Piezoelectric-based sensors 6.2.2 Optical-based sensors 6.2.3 Hall-effect-based sensors 6.2.4 PSECR-based sensors 6.2.5 Piezoresistive-based sensors 6.2.6 Capacitive-based sensors 6.2.7 MEMS-based sensors 6.2.8 Proximity detection 6.2.9 Summary of related work 6.3 Three-axis capacitive soft skin sensor 6.3.1 Concept 6.3.2 Implementation 6.3.2.1 Copper beryllium plate with bump 6.3.2.2 Tilt double-sided transducers 6.3.2.3 Temperature compensation pad 6.3.2.4 Manufacturing 6.3.3 Experiments 6.3.3.1 Linearity and hysteresis 6.3.3.2 Tri-axial force 6.3.3.2.1 Normal force calibration 6.3.3.2.2 Shear force calibration 6.3.3.4 Temperature influences compensation and minimum detectable pressure 6.3.3.3 SNR 6.3.4 Summary 6.4 Three-axis Hall-effect sensors 6.4.1 Concept 6.4.2 Implementation 6.4.3 Experiment 6.4.3.1 Test setup 6.4.3.2 Temperature compensation tests 6.4.3.3 Calibration 6.4.3.4 Response time 6.4.3.5 Minimum detectable force 6.4.3.6 SNR 6.4.3.7 Hysteresis 6.4.3.8 Spatial crosstalk test 6.4.3.9 Contact shape detection 6.4.3.10 Measurements on Allegro hand 6.5 Conclusion References 7 A review of tactile sensing in e-skin, wearable device, robotic, and medical service 7.1 Introduction 7.2 Hardware of various tactile sensing technologies 7.2.1 Resistive 7.2.2 Piezoelectric 7.2.3 Capacitive 7.2.4 Optical 7.2.5 Magnetic field 7.2.6 Quantum tunneling composite 7.2.7 Triboelectric effect 7.2.8 Field-effect transistor 7.3 Design criterion and performance index of a tactile sensing system 7.4 Applications of tactile sensing technologies 7.4.1 Development trend of tactile sensing technologies in e-skin 7.4.2 Development trend of tactile sensing technologies in a wearable device 7.4.3 Development trend of tactile sensing technologies in robotic 7.4.4 Development trend of tactile sensing technologies in medical service 7.5 Challenges and discussion 7.5.1 Standardization of fabrication process 7.5.2 Data transmission of high-density tactile sensing elements 7.5.3 Fault tolerance and autocalibration 7.5.4 Layout of sensing elements on an irregular 3D References 8 Neuroengineering approaches for cognitive hearing technology 8.1 Introduction 8.2 General aspects of neurofeedback in a hearing aid 8.3 Decoding selective attention to speech from the auditory brainstem response to the temporal fine structure 8.4 Decoding speech comprehension from cortical tracking of speech features 8.5 Enhancing speech comprehension through transcranial electric stimulation 8.6 Summary References 9 Mobile robot olfaction state-of-the-art and research challenges 9.1 Introduction 9.2 Odour dispersion 9.3 Artificial olfaction 9.3.1 Gas sensing 9.3.2 Flow sensing 9.4 Odour source localisation 9.4.1 Searching odours 9.4.2 Tracking odour plumes 9.4.2.1 Reactive strategies 9.4.2.2 Meta-heuristic search 9.4.2.3 Probabilistic methods 9.4.3 Source declaration 9.5 Learning in mobile robot olfaction 9.5.1 Source-term estimation 9.5.2 Policy search 9.5.2.1 Machine learning 9.5.2.2 Evolutionary computation 9.5.2.3 Swarm approaches 9.6 Open challenges 9.6.1 Artificial olfaction 9.6.2 Odour source localisation 9.6.3 Learning to locate odour sources References 10 Vision sensors for robotic perception 10.1 Introduction 10.2 RGB cameras for robotic perception 10.3 Stereo cameras 10.4 Event cameras 10.4.1 Hardware 10.4.2 Applications in robotics 10.5 Depth cameras 10.6 Vision sensors for other modalities 10.6.1 Marker-based sensors 10.6.2 Image-based sensors 10.7 Conclusions Acknowledgements References 11 Audio sensors 11.1 Audio sensors 11.1.1 Airborne microphones 11.1.2 Microphones for underwater 11.1.3 Microphones for underground and structures 11.1.4 Microphones for biological bodies 11.2 Microphones for audible sounds 11.2.1 Indicators for microphone characteristics 11.3 Microphone array 11.4 Robot audition 11.5 Acoustic signal processing 11.6 OSS for robot audition 11.7 Applications of robot audition 11.7.1 Prince Shotoku robot 11.7.2 Drone audition system 11.7.3 VR system based on bird song scene analysis 11.8 Summary References 12 Audio and gas sensors 12.1 Audio sensors 12.1.1 Hardware 12.1.1.1 Microphones 12.1.1.1.1 Miniature condenser microphones 12.1.1.1.2 MEMS microphones 12.1.1.2 Portable audio interfaces 12.1.1.3 Recommendations 12.1.1.4 Other acoustic sensors 12.1.2 Software 12.1.2.1 Localization 12.1.2.2 Separation 12.1.2.3 Currently implemented examples References Index