Tactile Sensors

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Uriel Martinez-Hernandez (2015), Scholarpedia, 10(4):32398. doi:10.4249/scholarpedia.32398 revision #149455 [link to/cite this article]
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Curator: Uriel Martinez-Hernandez

  Tactile sensors are data acquisition devices, or transducers, that are designed to sense a diversity of properties via direct physical contact (Nicholls and Lee, 1989). Tactile sensor designs are based around a range of different technologies some of which are directly inspired by research on biological touch. The growth of robotic applications in healthcare, agriculture, social assistance, autonomous systems and unstructured environments has created a pressing need for effective tactile sensors. Their deployment plays an important role permitting the detection, measurement and conversion of information, acquired by physical interaction with objects, into an appropriate form to be processed and analysed by higher level modules within an intelligent system (Najarian et al., 2009). Although, in recent decades, tactile sensor technology has shown great advances in design and capability, tactile sensing systems are still relatively undeveloped compared to the sophisticated technology accomplished in vision (Lee and Nicholls, 1999). The relatively slow development attained thus far is possibly related to the inherent complexity of the sense of touch (Siciliano and Khatib, 2008). Another limiting factor is that, by their very nature, tactile sensors require direct contact to be made surfaces and objects, and are therefore subject to wear and risk of damage than some other sensor types.


Contents

The human sense of touch and tactile sensors

Vision is sometimes asserted to be the most important human sensory modality perhaps underestimating the role of the sense of touch. Certainly, losing the capabilities offered by touch can cause catastrophic impairments to posture, locomotion and control of limbs, extraction of object properties and in general to any physical interaction with the environment (Robles-De-La-Torre, 2006).

Psychophysical studies have shown that the human haptic touch is rich in information for interaction, exploration, manipulation and extraction of object properties such as texture, shape, hardness and temperature (Lederman and Klatzky, 1987). This information is registered by various types of receptors, e.g. mechanoreceptors (pressure and vibration), thermoreceptors (temperature), and nociceptors (pain and damage) distributed all over the body with variable density and located in the various layers of the skin (Johansson and Westling, 1984). Human hands have a particularly high density of mechanoreceptors, being one of the parts of the body most specialised to provide accurate tactile feedback (Vallbo et al., 1984).

Tactile sensing was relatively neglected in the early years of robotics, with only a handful of devices developed by the end of the 1970s and with relatively limited integration of these systems into robots. In contrast, the 1980s saw substantial advances in tactile sensor technology, accompanied by a reduction in manufacturing costs. Progress was made in sensor materials, design and fabrication technologies, and in transduction methods for integration in various robotic platforms (Lee, 2000). The main tactile sensing technologies developed by this time were capacitive, piezoresistive, piezoelectric, magnetic, inductive, optical and strain gauges, allowing the successful development of accurate devices for detection of object shape, size, texture, force and temperature (De Rossi, 1991).

Criteria for sensor design

The human hand has wide range of sensor types that support several different forms of touch. Whilst it would be desirable to create a robotic device with similar sensing capability, particularly for applications in prosthetics, to do so would involve addressing a large list of design specifications (Castelli, 2002). Artificial touch sensing has therefore largely been focused towards less ambitious targets. The first design criteria for tactile sensors were proposed by Harmon (1982) and were motivated by the design requirements for industrial robots in the 1980s. As the technology has evolved so have the criteria. A key target for contemporary systems is humanoid robotics and the capacity to emulate human in-hand manipulation. To achieve this goal, Yousef et al. (2011) proposed the following list of functional requirements:

  • Contact detection and release of an object.
  • Lifting and replacement of an object.
  • Detection of shape and force distribution for object recognition.
  • Detection of dynamic and static forces.
  • Tracking of contact points during manipulation.
  • Estimation and detection of grip forces for manipulation.
  • Detection of motion and direction during manipulation.
  • Detection of tangential forces to prevent slip.

Beginning with the desirable features for in-hand manipulation, a set of general design guidelines for tactile sensors was presented by (Dargahi and Najarian, 2004), considering also the limitations and possibilities of sensors. The suggested guidelines, shown in Table1, are draw inspiration from the sensing capacities of the human hand (Dahiya et al., 2010).

Table 1: Design guidelines for tactile sensors in robotics
Parameter Guidelines
Force direction Normal and tangential
Temporal variation Dynamic and static
Spatial resolution 1 mm in fingertips to 5 mm in palm of hand
Time response 1 ms
Force sensitivity 0.01 - 10 N
Linearity/Hysteresis Stable, repeatable and monotonic with low hysteresis
Robustness Resistant to the application and the environment
Tactile cross-talk Minimal cross-talk
Shielding Electronic and/or magnetic shielding
Integration and fabrication Simple mechanical integration. Minimal wiring with low power consumption and cost


Beyond the guidelines presented in Table 1, temperature tolerance, size, weight, power consumption and durability are some additional important criteria (Najarian et al., 2009).

Multi-purpose sensors that address all of the above criteria remain a significant technological challenge. For this reason, a more limited set of constraints will be identified when designing sensors for specific applications, reducing cost and complexity.

Tactile sensor technologies

Human tactile sensory system is composed by sensors classified by the type of measurements registered and the way they are obtained. For example, tactile sensors can be sensitive to either static or dynamic forces, and can be employed for proprioception or for exteroception (Fraden, 2004). Proprioceptive sensors are responsible for measuring the internal state of the system, e.g. joint angles, limbs positions, velocity and motor torque. Exteroceptive sensors measuring the characteristics of the physical contact when touching objects in the environment, e.g. sensor surface deformation, contact area, contact pattern and pressure measurements (Siciliano and Khatib, 2008). The remainder of this review focuses on artificial touch sensors that are appropriate for applications in exteroception.

Tactile sensor technologies are classified by the transduction method employed to convert stimuli from external environment in a proper form for an intelligent system (Martinez-Hernandez, 2014). The most widely used tactile sensor technologies in robotics are based on capacitive, piezoresistive, optical, magnetic, binary and piezoelectric transduction methods are described in the following sections.

Capacitive sensors

Tactile sensors based on capacitive transduction operate by measuring the variations of capacitance from an applied load over a parallel plate capacitor. The capacitance is related to the separation and area of the parallel plate capacitor, which uses an elastomeric separator to provide compliance. Although Harmon has noted that capacitive sensors are susceptible to external fields (Harmon, 1982), this sensor technology has become popular in robotics for the development of “taxels” that mimic aspects of mechanoreception in human fingers (Schmidt et al., 2006; Muhammad et al., 2011). Capacitive sensors can be fabricated in very small sizes, permitting their construction and integration into dense arrays in reduced spaces, e.g. palms and fingertips (Schmitz et al., 2011). This technology also presents various advantages in terms of high sensitivity, long-term drift stability, low temperature sensitivity, low power consumption and sensing of normal or tangential forces (Lee and Wise, 1982). Limitations include significant hysteresis.

Piezoresistive sensors

This transduction method measures changes in the resistance of a contact when force is applied. Piezoresistive sensors are generally fabricated in conductive rubber or made with piezoresistive ink and stamped with a pattern. A maximum resistance value is generated when no contact or stress is applied to the sensor. Conversely, the resistance decreases with increasing pressure or stress to the contact (Webster, 1988). The benefits of this transduction method for integration into sensor arrays was initially demonstrated by Snyder and St Clair (1978), Briot et al. (1979), and Russell (1987). The advantages offered by this technology include its wide dynamic range, durability, good overload tolerance, low cost and ability for fabrication in very small sizes. Disadvantages include limited spatial resolution, the challenge of individually wiring multiple sensor elements, susceptibility to drift and hysteresis. The investigation and development of fabric-based piezoresistive sensors has offered an alternative material to improve the durability and reduce hysteresis (Siciliano and Khatib, 2008). Piezoresistive tactile sensors have been used in many robotic applications, particularly where high accuracy is not a design criteria (Beebe et al., 1995; Kerpa et al., 2003; Weiss and Woern, 2004).

Optical sensors

Optical sensors operate by transducing mechanical contact, pressure, or directional movement, into changes in light intensity or refractive index, which are then detected using state-of-the-art vision sensors. A drawback is that the need to include light emitters and detectors (e.g. CCD arrays), leading to increased bulk. However, optical sensors are attractive due to their potential for high-spatial resolution, robustness to electrical interference, light weight, and their potential to resolve the wiring complexity problem presented by other sensor types such as capacitive and piezoresistive (Nicholls and Lee, 1989; Yousef et al., 2011). This has led to the integration of optical tactile sensors into various robotic systems. Begej, (1988) described a robotic system for investigating dexterous object manipulation that integrated two 32x32 planar sensor arrays built with optical fibres. In Yamada et al. (2005) sub-millimetre resolution for object contact and location detection is described using an optical fingertip that operated by measuring the intensity and direction of reflected light. Fabrication of optical taxels that are capable of measuring normal forces has been described in Heo et al. (2006). This optical system used an LED emitter together with a CCD array to measure force transduced into changes in light intensity. As a final example, Hsiao et al. (2009) describe an optical device for robust detection of object contact and grasping in a three-fingered robot hand.

Magnetic sensors

This technology operates by detecting changes in magnetic flux, induced by an applied force, through the use of Hall effect, magnetoresistive or magnetoelastic sensors. Hall effect sensors operate by measuring variations in the voltage that is generated by an electric current passing through a conductive material immersed in a magnetic field (Najarian et al., 2009). In Kinoshita et al. (1983) robot gripper using this sensing technology was integrated with twenty Hall effect sensors permitting the robot to perform an object tracking experiment. Contact detection and fingertip deformation were investigated with a 4x4 array of Hall effect sensors mounted on a rigid base by Nowlin (1991). Hall effect sensors have also proved to be an effective way of detecting multi-directional deflections of an artificial whisker (Pearson et al., 2007, Sullivan et al., 2009). Magnetoresistive and magnetoelastic sensors detect variations in magnetic fields generated by the application of mechanical stress. In the 1970s a robot tactile sensor using this magnetic approach was developed for object classification based on their contours (Pugh, 1986). Despite the relatively large size of magnetic sensing elements, Jayawant (1989) achieved recognition of 2D images using a sensor array fabricated with 256 magnetic elements. Advantages of magnetic sensor technologies include high sensitivity, wide dynamic range, very low hysteresis, linear response and general robustness. However, they are susceptible to magnetic interference and noise. Applications are limited by the physical size of the sensing device, and by the need to operate in nonmagnetic environments (Dahiya et al., 2010).

Binary sensors

Contact switches permit the detection of discrete on/off events brought about by mechanical contact (Webster, 1988). The ease of designing and building this type of sensor has permitted its integration into a very wide of robotic systems. A five-fingered prosthetic robotic hand developed by Edin et al. (2006) used on/off sensors embedded in the fingertips to support a grasping procedure. It is possible to design contact devices that go beyond a simple binary code. For example, Tajima et al. (2002) describe a sensor capable of encoding variations in pressure in a discrete multi-state code. Lack of resolution is the primary disadvantage to this sensor technology limiting applications to problems such as contact or collision detection.

Piezoelectric sensors

Piezoelectric sensors produce an electric charge proportional to an applied force, pressure or deformation. Limitation to dynamic measurements and susceptibility to temperature are the main drawbacks of this sensing technology. However, they are suitable for measurement of vibrations and widely used due to their sensitivity, high frequency response and availability in various forms, e.g. plastics, crystals, ceramics and polyvinylidene fluoride (PVDF) (Lee and Nicholls, 1999; Schmidt et al., 2006). Grahn and Astle (1986) achieved robust object detection using a tactile sensor covered with a piezoelectric material based on a layer of silicon rubber. Here, the electric charge used for object detection was generated by contacting and deforming the silicon layer. Yamada and Cutkosky (1994) used piezoelectric technology in an artificial skin technology designed to be sensitive to force, vibration and slip. From the repertoire of piezoelectric materials, PVDF is the most commonly preferred for fabrication of tactile sensors given its flexibility, workability and chemical stability (Dahiya et al., 2010). Dario and De Rossi (1985) have described the use of PVDF for building and integration of tactile sensors in a robotic gripper.

Hydraulic sensors

Hydraulic technology uses a type of actuator that converts fluid pressure into mechanical motion. Recent industrial and medical applications require microscopic servomechanisms, known as microactuators, to detect pressure and measure force based on hydraulics (De Volder and Reynaerts, 2010). Micro-hydraulics structures developed in (Sadeghi et al., 2011) allowed the fabrication of a low-power, accurate and robust flow sensor. This sensor, composed of a biomimetic hair-like structure, allows to translate flow into hydraulic pressure offering a large measurement range and high sensitivity. Force sensor arrays, similar to the human fingertip size, were able to achieve high sensitivity based on the micro-hydraulics sensing technology (Sadeghi et al., 2013). These low cost force sensors, fabricated with a stereo-lithography technique, provided robust tactile data and high spatial resolution, making them suitable for skin-like sensing applications.


Applications

Since the 1980s, when robotics was defined as the science for studying perception, action and their intelligent interconnection (Siciliano and Khatib, 2008), the integration of tactile sensors has played an important role in the development of robust, flexible and adaptable robots capable of exploring their environments and interacting safely with humans.

A variety of open robotic platforms that employ different tactile sensor technologies in their hands, fingertips, arms, forearms and torso have been developed for the study of embodied cognition, exploration, perception, recognition, learning and interaction (Schmitz et al., 2011; Weiss and Woern, 2004; Edin et al., 2006; Metta et al., 2008; Brooks et al., 1999). For example, Figure 1 shows the iCub humanoid robot (Metta et al. 2008) equipped tactile sensors in its torso, arms, forearms, palms and fingertips.

Figure 1: Contemporary humanoids such as the iCub are beginning to be equipped with tactile sensors on body surfaces suh as the hands/fingers, arms, and torso.

Robotic fingertips equipped with piezoelectric sensing elements have been used to recognise various object properties such as texture, hardness and shape by performing different exploratory procedures, e.g. sliding, squeezing, pushing and tapping over various objects (Hosoda et al., 2006; Takamuku et al., 2007). Various robotics hands integrated with capacitive tactile sensors have also been developed for exploration and recognition (see Son et al., 1996; Schneider et al., 2009). Pressure and force sensors in robotic fingertips have allowed the achievement of reliable control during object detection and manipulation (Dang et al., 2011; Chen et al., 1995b). Design and integration of tactile sensors in prosthetic hands permitted to mimic the natural motion and contact detection observed in humans (Carrozza et al., 2003). Investigations of tactile exploration, perception and interaction have been successfully achieved using the tactile sensory system of the iCub humanoid robot (Martinez-Hernandez, 2014; Lepora et al., 2013).

Object shape exploration has been investigated using different perception and control approaches implemented in a variety of tactile robotic platforms. For instance, a PUMA robot integrated with a planar tactile sensor array was used to extract object edge and orientation based on tactile images and geometrical moments (Muthukrishnan et al., 1987; Chen et al., 1995a). A similar approach based on geometrical moments, but using a KUKA arm with a planar tactile sensors, was able to explore and recognise the shape of various objects (Li et al., 2013). An adaptive threshold approach applied to tactile images obtained from a CMU DD Arm II allowed the recognition of object orientation (Berger and Khosla, 1988). The use of the iCub fingertip sensor with a probabilistic approach presented an intelligent and robust object shape exploration and extraction (Martinez-Hernandez et al., 2013). Figure 2 shows a robotic hand integrated with tactile fingertip sensors that has been programmed perform robust exploratory procedures.

Figure 2: Schunk hand mounted on a KUKA arm for manipulations tasks.

A rolling and enclosing exploration procedure was implemented for robust object recognition based on the kurtosis observed on each tactile fingertip of a five-fingered robotic hand (Nakamoto et al., 2008). A shadow robotic platform performing an enclosing procedure was able to classify a variety of objects using tactile information and a Self-Organising Map (SOM) (Ratnasingam and McGinnity, 2011). The iCub humanoid robot using tactile information from its hands and fingers during an enclosure procedure was able to achieve high accuracy for an object recognition task. These results were obtained by the implementation of a biologically inspired method based on a probabilistic and temporal approach (Soh et al., 2012). The robotic hand from Barrett Hand Inc. covered with tactile sensors in its palm and fingertips has been widely used for dexterous manipulation and perception with the sense of touch (Figure 3).

Figure 3: Robotic hand from Barrett Hand Inc. used for dexterous manipulation and exploration based on active tactile perception.

Tactile sensors have been integrated into a number of biomimetic robots both as a means to understand tactile sensing in animals and as a path towards the development of useful robotic technologies. Perception of stimulus attributes, such as texture, distance to contact, and speed and direction of moving stimuli, has been demonstrated in a number of whiskered robots equipped with artificial vibrissae (Pearson et al., 2007; Prescott et al., 2009; Sullivan et al., 2012; Lungarella et al., 2002). Tactile sensing used artificial whiskers has also be demonstrated as being able to support complex surface detection and reconstruction (Solomon and Hartmann, 2006), tactile simultaneous localisation and mapping (Pearson et al., 2013), and moving object detection and tracking (Mitchinson et al., 2014). The development of artificial antennae fabricated with pressure and force sensors has allowed modelling of the contact detection and exploration behaviour of insects such as ants and cockroaches (Kaneko, 1994; Ueno and Kaneko, 1994; Kaneko et al., 1998). Research and integration of tactile sensors has also reached the field of underwater robotics in the form of artificial whiskers, modelled on the remarkable perceptual capabilities of seals, that are able to measure speed and direction of fluid motion, angle and wake detection (Eberhardt et al., 2011; Beem et al., 2013).


Conclusions

Inspired by the repertoire of capabilities and benefits that the sense of touch offers to the animal kingdom, different tactile sensor technologies have been developed, since the early days of robotics, that can enhance the performance and functionality of robotic systems allowing them to touch, feel and explore their environments. A range of different sensor technologies have been developed each with their own advantages and limitations, though we have yet to come close to emulating the versatility and richness of the human sense of touch.


Internal references

Cockroach antennae

Haptic exploration

Mammalian mechanoreception

Whiskered robots

Vibrissal touch in pinnipeds

References

  • Beebe, D. J., Hsieh, A. S., Denton, D. D., and Radwin, R. G. (1995). A silicon force sensor for robotics and medicine. Sensors and Actuators A: Physical, 50(1):55–65.
  • Beem, H., Hildner, M., and Triantafyllou, M. (2013). Calibration and validation of a harbor seal whisker-inspired flow sensor. Smart Materials and Structures, 22(1):014012.
  • Begej, S. (1988). Planar and finger-shaped optical tactile sensors for robotic applications. Robotics and Automation, IEEE Journal of, 4(5):472–484.
  • Berger, A. D. and Khosla, P. K. (1988). Edge detection for tactile sensing. In Proc. SPIEs Cambridge Symposium on Optical and Optoelectronic Engineering: Advances in Intelligent Robotics Systems.
  • Briot, M. et al. (1979). The utilization of an artificial skin sensor for the identification of solid objects. In 9th Int. Symp. on Industrial Robots, pages 13–15.
  • Brooks, R. A., Breazeal, C., M Marjanović, M., Scassellati, B., and Williamson, M. M. (1999). The cog project: Building a humanoid robot. In Computation for metaphors, analogy, and agents, pages 52–87. Springer.
  • Carrozza, M. C., Dario, P., Vecchi, F., Roccella, S., Zecca, M., and Sebastiani, F. (2003). The cyberhand: on the design of a cybernetic prosthetic hand intended to be interfaced to the peripheral nervous system. In Intelligent Robots and Systems, 2003.(IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on, volume 3, pages 2642–2647. IEEE.
  • Castelli, F. (2002). An integrated tactile-thermal robot sensor with capacitive tactile array. Industry Applications, IEEE Transactions on, 38(1):85–90.
  • Chen, N., Rink, R., and Zhang, H. (1995a). Efficient edge detection from tactile data. In Intelligent Robots and Systems 95.’Human Robot Interaction and Cooperative Robots’, Proceedings. 1995 IEEE/RSJ International Conference on, volume 3, pages 386–391. IEEE.
  • Chen, N., Zhang, H., and Rink, R. (1995b). Edge tracking using tactile servo. In Intelligent Robots and Systems 95.’Human Robot Interaction and Cooperative Robots’, Proceedings. 1995 IEEE/RSJ International Conference on, volume 2, pages 84–89. IEEE.
  • Dahiya, R. S., Metta, G., Valle, M., and Sandini, G. (2010). Tactile sensing from humans to humanoids. Robotics, IEEE Transactions on, 26(1):1–20.
  • Dang, H., Weisz, J., and Allen, P. K. (2011). Blind grasping: Stable robotic grasping using tactile feedback and hand kinematics. In Robotics and Automation (ICRA), 2011 IEEE International Conference on, pages 5917–5922. IEEE.
  • Dargahi, J. and Najarian, S. (2004). Human tactile perception as a standard for artificial tactile sensing a review. The International Journal of Medical Robotics and Computer Assisted Surgery, 1(1):23–35.
  • Dario, P. (1991). Tactile sensing: Technology and applications. Sensors and Actuators A: Physical, 26(1):251–256.
  • Dario, P. and De Rossi, D. (1985). Tactile sensors and the gripping challenge: Increasing the performance of sensors over a wide range of force is a first step toward robotry that can hold and manipulate objects as humans do. Spectrum, IEEE, 22(8):46–53.
  • De Rossi, D. (1991). Artificial tactile sensing and haptic perception. Measurement Science and Technology, 2(11):1003. Eberhardt, W., Shakhsheer, Y., Calhoun, B., Paulus, J., and Appleby, M. (2011). A bio-inspired artificial whisker for fluid motion sensing with increased sensitivity and reliability. In Sensors, 2011 IEEE, pages 982–985. IEEE.
  • De Volder, M. and Reynaerts, D. (2010). Pneumatic and hydraulic microactuators: a review. Journal of Micromechanics and microengineering, 20(4):043001.
  • Edin, B. B., Beccai, L., Ascari, L., Roccella, S., Cabibihan, J.-J., and Carrozza, M. C. (2006). Bio-inspired approach for the design and characterization of a tactile sensory system for a cybernetic prosthetic hand. In Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on, pages 1354–1358. IEEE.
  • Fend, M., Bovet, S., and Hafner, V. (2005). The artificial mouse-a robot with whiskers and vision. Signal [V], 5(6).
  • Fraden, J. (2004). Handbook of modern sensors: physics, designs, and applications. Springer.
  • Grahn, A. R. and Astle, L. (1986). Robotic ultrasonic force sensor arrays. Robot sensors, 2:297–315.
  • Harmon, L. D. (1982). Automated tactile sensing. The International Journal of Robotics Research, 1(2):3–32.
  • Heo, J.-S., Chung, J.-H., and Lee, J.-J. (2006). Tactile sensor arrays using fiber bragg grating sensors. Sensors and Actuators A: Physical, 126(2):312–327.
  • Hosoda, K., Tada, Y., and Asada, M. (2006). Anthropomorphic robotic soft fingertip with randomly distributed receptors. Robotics and Autonomous Systems, 54(2):104–109.
  • Hsiao, K., Nangeroni, P., Huber, M., Saxena, A., and Ng, A. Y. (2009). Reactive grasping using optical proximity sensors. In Robotics and Automation, 2009. ICRA’09. IEEE International Conference on, pages 2098–2105. IEEE.
  • Jayawant, B. (1989). Tactile sensing in robotics. Journal of Physics E: Scientific Instruments, 22(9):684.
  • Johansson, R. and Westling, G. (1984). Roles of glabrous skin receptors and sensorimotor memory in automatic control of precision grip when lifting rougher or more slippery objects. Experimental Brain Research, 56(3):550–564.
  • Kaneko, M. (1994). Active antenna. In Robotics and Automation, 1994. Proceedings., 1994 IEEE International Conference on, pages 2665–2671. IEEE.
  • Kaneko, M., Kanayama, N., and Tsuji, T. (1998). Active antenna for contact sensing. Robotics and Automation, IEEE Transactions on, 14(2):278–291.
  • Kerpa, O., Weiss, K., and Worn, H. (2003). Development of a flexible tactile sensor system for a humanoid robot. In Intelligent Robots and Systems, 2003.(IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on, volume 1, pages 1–6. IEEE.
  • Kinoshita, G., Hajika, T., and Hattori, K. (1983). Multifunctional tactile sensors with multi-elements for fingers. In Proc. Int. Conf. on Advanced Robotics, pages 195–202.
  • Lederman, S. J. and Klatzky, R. L. (1987). Hand movements: A window into haptic object recognition. Cognitive psychology, 19(3):342–368.
  • Lee, M. H. (2000). Tactile sensing: new directions, new challenges. The International Journal of Robotics Research, 19(7):636–643.
  • Lee, M. H. and Nicholls, H. R. (1999). Review article tactile sensing for mechatronics a state of the art survey. Mechatronics, 9(1):1–31.
  • Lee, Y. S. and Wise, K. D. (1982). A batch-fabricated silicon capacitive pressure transducer with low temperature sensitivity. Electron Devices, IEEE Transactions on, 29(1):42–48.
  • Lepora, N. F., Martinez-Hernandez, U., and Prescott, T. J. (2013). A solid case for active bayesian perception in robot touch. In Biomimetic and Biohybrid Systems, pages 154–166. Springer.
  • Li, Q., Schurmann, C., Haschke, R., and Ritter, H. (2013). A control framework for tactile servoing.
  • Lungarella, M., Hafner, V. V., Pfeifer, R., and Yokoi, H. (2002). An artificial whisker sensor for robotics. In Intelligent Robots and Systems, 2002. IEEE/RSJ International Conference on, volume 3, pages 2931–2936. IEEE.
  • Martinez-Hernandez, U. (2014). Autonomous active exploration for tactile sensing in robotics. PhD thesis, The University of Sheffield, Sheffield, UK.
  • Martinez-Hernandez, U., Dodd, T., Prescott, T. J., and Lepora, N. F. (2013). Active bayesian perception for angle and position discrimination with a biomimetic fingertip. In Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on, pages 5968–5973. IEEE.
  • Metta, G., Sandini, G., Vernon, D., Natale, L., and Nori, F. (2008). The icub humanoid robot: an open platform for research in embodied cognition. In Proceedings of the 8th workshop on performance metrics for intelligent systems, pages 50–56. ACM.
  • Mitchinson, B., Pearson, M. J., Pipe, A. G., Prescott, T. J. (2014). Biomimetic tactile target acquisition, tracking and capture, Robotics and Autonomous Systems, 62(3), 366-375.
  • Muhammad, H., Oddo, C., Beccai, L., Recchiuto, C., Anthony, C., Adams, M., Carrozza, M., Hukins, D., and Ward, M. (2011). Development of a bioinspired MEMS based capacitive tactile sensor for a robotic finger. Sensors and Actuators A: Physical, 165(2):221–229.
  • Muthukrishnan, C., Smith, D., Myers, D., Rebman, J., and Koivo, A. (1987). Edge detection in tactile images. In Robotics and Automation. Proceedings. 1987 IEEE International Conference on, volume 4, pages 1500–1505. IEEE.
  • Najarian, S., Dargahi, J., and Mehrizi, A. (2009). Artificial tactile sensing in biomedical engineering. McGraw Hill Professional.
  • Nakamoto, H., Kobayashi, F., Imamura, N., Shirasawa, H., and Kojima, F. (2008). Shape classification in rotation manipulation by universal robot hand. In Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on, pages 53–58. IEEE.
  • Nicholls, H. R. and Lee, M. H. (1989). A survey of robot tactile sensing technology. The International Journal of Robotics Research, 8(3):3–30.
  • Nowlin, W. C. (1991). Experimental results on bayesian algorithms for interpreting compliant tactile sensing data. In Robotics and Automation, 1991. Proceedings., 1991 IEEE International Conference on, pages 378–383. IEEE.
  • Pearson, M. J., Pipe, A. G., Melhuish, C., Mitchinson, B., and Prescott, T. J. (2007). Whiskerbot: a robotic active touch system modeled on the rat whisker sensory system. Adaptive Behavior, 15(3):223–240.
  • Pearson, M., Fox, C. J., Sullivan, C. W., Prescott, T. J., Pipe, A. G., Mitchinson, B. (2013). Simultaneous localisation and mapping on a multi-degree of freedom biomimetic whiskered robot. IEEE International Conference on Robotics and Automation (ICRA).
  • Prescott, T. J., Pearson, M., Mitchinson, B., Sullivan, J. C. W., Pipe, A. G. (2009). Whisking with robots: From rat vibrissae to biomimetic technology for active touch. IEEE Robotics and Automation Magazine, 16(3), 42-50.
  • Pugh, A. (1986). Robot Sensors: Tactile and non-vision, volume 2. Springer.
  • Ratnasingam, S. and McGinnity, T. (2011). Object recognition based on tactile form perception. In Robotic Intelligence In Informationally Structured Space (RiiSS), 2011 IEEE Workshop on, pages 26–31. IEEE.
  • Robles-De-La-Torre, G. (2006). The importance of the sense of touch in virtual and real environments. Ieee Multimedia, 13(3):24–30.
  • Russell, R. A. (1987). Compliant-skin tactile sensor. In Robotics and Automation. Proceedings. 1987 IEEE International Conference on, volume 4, pages 1645–1648. IEEE.
  • Sadeghi, M. M., Peterson, R. L., and Najafi, K. (2011). Micro-hydraulic structure for high performance bio-mimetic air flow sensor arrays. In Electron Devices Meeting (IEDM), 2011 IEEE International, pages 29–4. IEEE.
  • Sadeghi, M. M., Peterson, R. L., and Najafi, K. (2013). High-speed electrostatic micro-hydraulics for sensing and actuation. In Micro Electro Mechanical Systems (MEMS), 2013 IEEE 26th International Conference on, pages 1191–1194. IEEE.
  • Schmidt, P. A., Ma ̈el, E., and Wurtz, R. P. (2006). A sensor for dynamic tactile information with applications in human–robot interaction and object exploration. Robotics and Autonomous Systems, 54(12):1005–1014.
  • Schmitz, A., Maiolino, P., Maggiali, M., Natale, L., Cannata, G., and Metta, G. (2011). Methods and technologies for the implementation of large-scale robot tactile sensors. Robotics, IEEE Transactions on, 27(3):389–400.
  • Schneider, A., Sturm, J., Stachniss, C., Reisert, M., Burkhardt, H., and Burgard, W. (2009). Object identification with tactile sensors using bag-of-features. In Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on, pages 243–248. IEEE.
  • Siciliano, B. and Khatib, O. (2008). Springer handbook of robotics. Springer.
  • Snyder, W. E. and St Clair, J. (1978). Conductive elastomers as sensor for industrial parts handling equipment. Instrumentation and Measurement, IEEE Transactions on, 27(1):94–99.
  • Soh, H., Su, Y., and Demiris, Y. (2012). Online spatio-temporal gaussian process experts with application to tactile classification. In Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, pages 4489–4496. IEEE.
  • Solomon, J. H. and M. J. Hartmann (2006). "Biomechanics: robotic whiskers used to sense features." Nature 443(7111): 525.
  • Son, J. S., Howe, R. D., Wang, J., and Hager, G. D. (1996). Preliminary results on grasping with vision and touch. In Intelligent Robots and Systems’ 96, IROS 96, Proceedings of the 1996 IEEE/RSJ International Conference on, volume 3, pages 1068–1075. IEEE.
  • Sullivan, J., Mitchinson, B., Pearson, M. J., Evans, M., Lepora, N. F., Fox, C. W., Melhuish, C., and Prescott, T. J. (2012). Tactile discrimination using active whisker sensors. Sensors Journal, IEEE, 12(2):350–362.
  • Tajima, R., Kagami, S., Inaba, M., and Inoue, H. (2002). Development of soft and distributed tactile sensors and the application to a humanoid robot. Advanced Robotics, 16(4):381–397.
  • Takamuku, S., Gomez, G., Hosoda, K., and Pfeifer, R. (2007). Haptic discrimination of material properties by a robotic hand. In Development and Learning, 2007. ICDL 2007. IEEE 6th International Conference on, pages 1–6. IEEE.
  • Ueno, N. and Kaneko, M. (1994). Dynamic active antenna-a principle of dynamic sensing. In Robotics and Automation, 1994. Proceedings., 1994 IEEE International Conference on, pages 1784–1790. IEEE.
  • Vallbo, A. B., Johansson, R., et al. (1984). Properties of cutaneous mechanoreceptors in the human hand related to touch sensation. Hum Neurobiol, 3(1):3–14.
  • Webster, J. G. (1988). Tactile sensors for robotics and medicine. John Wiley & Sons, Inc.
  • Weiss, K. and Woern, H. (2004). Tactile sensor system for an anthropomorphic robotic hand. In IEEE International conference on manipulation and grasping IMG.
  • Yamada, Y. and Cutkosky, M. R. (1994). Tactile sensor with 3-axis force and vibration sensing functions and its application to detect rotational slip. In Robotics and Automation, 1994. Proceedings., 1994 IEEE International Conference on, pages 3550–3557. IEEE.
  • Yamada, Y., Morizono, T., Umetani, Y., and Takahashi, H. (2005). Highly soft viscoelastic robot skin with a contact object-location-sensing capability. Industrial Electronics, IEEE Transactions on, 52(4):960–968.
  • Yousef, H., Boukallel, M., and Althoefer, K. (2011). Tactile sensing for dexterous in-hand manipulation in robotics a review. Sensors and Actuators A: physical, 167(2):171–187.
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