Tactile Sensors

• Dr. Uriel Martinez-Hernandez, University of Sheffield, Sheffield, United Kingdom

  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.

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.

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Internal references

• Hanke, W and Dehnhardt, G (2015). Vibrissal touch in pinnipeds. Scholarpedia 10(3): 6828. http://www.scholarpedia.org/article/Vibrissal_touch_in_pinnipeds. (see also pages mmm-nnn of this book)

• Klatzky, R and Reed, C L (2009). Haptic exploration. Scholarpedia 4(8): 7941. http://www.scholarpedia.org/article/Haptic_exploration. (see also pages mmm-nnn of this book)

• Moayedi, Y et al. (2015). Mammalian mechanoreception. Scholarpedia 10(3): 7265. http://www.scholarpedia.org/article/Mammalian_mechanoreception. (see also pages mmm-nnn of this book)

• Okada, J (2009). Cockroach antennae. Scholarpedia 4(10): 6842. http://www.scholarpedia.org/article/Cockroach_antennae. (see also pages mmm-nnn of this book)

• Pipe, T and Pearson, M J (2015). Whiskered robots. Scholarpedia 10(3): 6641. http://www.scholarpedia.org/article/Whiskered_robots. (see also pages mmm-nnn of this book)

Sponsored by: Prof. Tony J. Prescott, Dept Psychology, Univ of Sheffield, UK

Reviewed by: Prof. Tony J. Prescott, Dept Psychology, Univ of Sheffield, UK

Reviewed by: Dr. Martin J. Pearson, Bristol Robotics Laboratory

Accepted on: 2015-04-06 23:39:55 GMT