Development of an Elastic Tactile Sensor Emulating Human Fingers for Tele-Presentation Systems Yusuke HIDAKA, Yuta SHIOKAWA, Kaoru TASHIRO and Takashi MAENO
Masashi KONYO and Takahiro YAMAUCHI Graduate School of Information Sciences, Tohoku University
Graduate School of System Design and Management, Keio University Yokohama, Japan
Sendai, Japan
[email protected] Abstract—We have developed a tactile sensor for tactile telepresentation systems by focusing on four features of the human finger considered to play an important role in texture perception; the existence of nails and bone, the multilayer structure of soft tissue, the distribution of mechanoreceptors, and the deployment of epidermal ridges. As a result, the developed sensor could detect roughness, softness and friction known to constitute texture perception of humans with precision equivalent to human. In addition, our sensor can be equipped with robot hands as it satisfies the requirements such as size equivalent to human finger, durability to withstand the wearing for repeated use, correspondence for scanning two dimensions.
I.
INTRODUCTION
Tactile technology to detect and display realistic tactile information such as roughness, softness and friction senses is expected as a technology for virtual reality and tele-robotics. Especially, technology to enable human operators to perceive textures that robots in remote environment are touching is expected to create benefits such as an improvement in the reality of perception, and providing a stable grasp by preventing slippage. From these background, variety of tactile transmission system have been developed including palpation systems for searching lump in an organ [1], system to detect and display bump on a plate [2] and system to display arbitrary softness by controlling contact area between operator’s finger and tactile display [3]. However, these studies focus on transmitting single tactile factor by detecting and displaying single stimulus of multiple stimuli caused depending on physical property of objects. However, variety and reality of tactile senses could be perceived only by recreating multiple tactile factors simultaneously. One of the causes of this problem is that tactile sensors able to detect tactile information equivalent to human as well as satisfy requirements to be equipped in the system. Problems of past tactile sensors are as follows: •
Features of human finger considered to play an important role in texture perception haven’t been emulated accurately.
978-1-4244-5335-1/09/$26.00 ©2009 IEEE
•
Multiple tactile senses couldn’t be detected simultaneously only by scanning once like human finger.
•
Past tactile sensors were not correspondence in multimodal finger movement.
In the present study, we develop a tactile sensor for tactile tele-presentation system by emulating the tissue structure and perceptual mechanism of human fingers as well as satisfying the requirement stated above. II.
FEATURES OF HUMAN FINGER
Preceding studies have suggested there are many specific features of a human finger that realize perception of such a wide variety of texture. From an anatomical layered tissue composing the finger, and the perspective, the structure of the finger, the structure of the mechanoreceptors within the tissue all play an important role in texture perception. A. Finger Structure First, focusing on the structure of a human finger, a human fingertip is made up of tissue, bone, and nail. Tissue surrounds the bone, allowing the finger to take an oval-like form. The curve of the oval shape allows consistent precise grasping and manipulation. The rigid nail helps secure the tissue so that the finger can touch the object without having to deform too much to accomplish a task. Nails are also known to play an important role in texture perception, in which it helps to enlarge the stimuli on mechanoreceptors by sandwiching the tissue between the surface and nail. B. Tissue Structure Looking more closely at the tissue structure, studies have noticed that human tissue is made up of multiple layers, each with different mechanical properties. The outmost layer is the epidermis, beneath it the dermis layer, and the layer closest to the bone is the subcutanea. The epidermis is the hardest layer, with the smallest elasticity and is approx. 1mm thick. Epidermal ridges cover the surface of the epidermis. The
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IEEE SENSORS 2009 Conference
papilla
Meissner’s corpuscle Epidermal ridge Keratinous layer Epidermal Layer
Merkel’s corpuscle
Dermal Layer Subcutaneous fat
Sweat grand Pacinian corpuscle
Rufinian corpuscle
Fig. 1 Structure of human tissue effect of these ridges will be mentioned later. The dermis is a softer layer with more elasticity, usually 1 to 3mm thick. Innumerous protrusions compose the boundary between the epidermis and dermis securing the two layers. The subcutanea which fills the space between the dermis and bone is mainly composed of fat and functions as a cushion when shock load is applied to the finger. The multilayered structure enhances effective texture perception. Due to the difference in elastic coefficients, there is greater deformation of the inner layers than the outmost epidermis when the finger presses into or moves along a surface. C. Mechanoreceptors Texture perception of humans is said to be conducted through determination of a combination of certain types of information such as “roughness”, “thickness/softness”, and “friction” [6]. Four types of mechanoreceptors, Meissner’s Corpuscles, Pacinian Corpuscles, Merkel’s discs, and Ruffini Endings, are situated within a human finger and are each assigned to detect different types of information (Fig. 1). For example, the Meissner’s corpuscles, closely packed on the epidermis-dermis boundary, are said to be effective in detecting the roughness of a surface. Meissner’s corpuscles respond to the change in velocity and are most activated when stimulation with frequencies below 100 Hz is applied. Therefore, when touching motion is conducted, the roughness of the surface the finger is touching generates vibration on the surface of he finger tissue, and this ignites the Meissner’s corpuscles. Pacinian Corpuscles, on the other hand, are highly sensitive pressure receptors that are located deep in the dermis. These also respond to vibration rather than to prolonged pressure, but of higher frequencies than the Meissner’s corpuscles. Vibration with frequencies higher than 100Hz is believed to relate to friction or how slippery the surface is. D. Epidermal Ridges Epidermal ridges are approximately 0.1mm in height, 0.3 to 0.5mm in width, and cover the surface of a human finger. They are also an extremely important element in texture perception. The shape of an epidermal ridge is a combination of a trapezoid and semicircle. This shape is the most effective for even distribution of normal force at the surface. Maeno et al. have discovered through simulation that the existence of epidermal ridges affects the activity of Meissner’s corpuscles which are located directly beneath these ridges [7]. Further
Fig. 2 Human texture perception [6] evaluation on the effect epidermal ridges have to improve tactile sensitivity has also been conducted. When moving the finger along a surface, vibration of the epidermal ridges is directly transmitted to the corpuscles where as without the ridges, the finger would just slide along the surface unable to distinguish between a rough surface and a smooth one. III.
HUMAN TEXTURE PERCEPTION
Human percept tactile senses by palpating surface of an object. It is considered that multiple tactile stimuli depending on physical properties of the object affect tactile senses. Especially surface texture, elasticity, friction characteristic and heat transfer characteristic are known to have significant effect on tactile senses (see Fig. 2) [6]. In this study, we develop a sensor to detect mechanical stimulus perceived as skin sensation -- vibration patterns caused inside skins depending on surface texture, pressure distribution patterns caused at the surface of skins depending on elasticity, and friction force patterns depending on friction characteristic of objects. We have shown in our previous study that roughness, softness and friction sense are assumed to be perceived by recreating those mechanical stimuli patterns [6]. In this study, each tactile sense is defined referring to our previous study as follows; roughness sense is the tactile sense such as rough or flat perceived mainly due to surface texture of objects, softness sense is the tactile sense such as soft or hard perceived mainly due to elasticity of objects, and friction sense is the tactile sense such as slippery or non-slippery perceived mainly due to friction characteristic of objects. As a result of factor analysis, it is confirmed that roughness and softness senses are orthogonal [6]. In a strict sense, friction sense is not orthogonal to roughness and softness senses. However, it is also confirmed that friction characteristic is an important physical property for tactile sense [8]. Hence, friction sense is selected as one of the target tactile senses to be displayed in this study.
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Acrylic layer Silicon layer 5mm
2 axes force sensor
Strain gage Urethane layer 0.1mm
R=5mm R=9.75mm R=11.25mm
R=0.25mm 0.6mm
Close Tactile sensor
Strain gage
(ⅱ): Top view
Top view figure
(ⅲ): whole tactile sensor .
(ⅰ): Outline drawing.
Fig. 3.
IV.
Finger shaped elastic tactile sensor
DESIGN
In the present study, we develop sensor to detect multiple physical properties having effect on tactile sensation for tactile tele-presentation system. Mukaibo developed a sensor able to detect roughness, softness and friction senses using strain gages by emulating the tissue structure and perceptual mechanism of human fingers [9][10]. Our sensor was designed adopting the same approach as Mukaibo did, except for the following modifications. 1) Size equivalent to human finger Mukaibo’s sensor was designed three times the size of human finger. Therefore, the deformation of the sensor caused by touching an object was different from that of human finger. In addition, it was difficult to equip the sensor with robot hands. 2) Accurate emulation of epidermal ridges It was confirmed that vibration pattern caused inside of the sensor was different from that of human finger if the size of epidermal ridges were not equal. 3) Correspondance to three dimensions scanning Mukaibo’s sensor corresponds to only two dimensions. However, human move his finger in three dimensions depending on characteristics of the object he is touching. Fig.3 shows the developed sensor. Silicone rubber is used for inside layer emulating the human tissue. The surface layer of the sensor was made of urethane rubber, considering the friction property and durability. In order to effectively derive sensory information, parts that function as the bone and nail are situated at the base of the sensor. The surface of the sensor that touches the subject material is curved in order to detect the difference in contact area. The surface layer of the sensor was made of urethane rubber, considering the friction property and durability. To emulate mechanoreceptors of the human finger, a total of five strain gages were placed inside the silicone rubber, on the boundary of the two different layers, just like Meissner’s Corpuscles. This is to obtain spatial distribution of sensory outputs. Strain gages were placed crosswise so that it can be scanned from any direction.
Scanning direction was calculated using the ratio of two orthogonal sensor outputs. Epidermal ridges were placed along the surface of the sensor to achieve the same effects as human fingers do, as explained in the previous section. Two double leaf springs are positioned at the base of the sensor to measure the total normal force and tangential force that is applied to the sensor. The five strain gages are each deployed directly beneath an epidermal ridge so that both vibration information and deformation of the epidermal ridge can be obtained when in contact with an object surface. The area of a contact surface can be measured by placing multiple strain gages, and keeping track of the output of each strain gage. V.
VERIFICATION EXPERIMENT
To verify the estimation ability of the developed sensor, several experiments stated in the following section were conducted. A. Estimation of Surface Geometry First, in order to verify that the sensor can detect the minute depth of surface texture, ten acrylic resins having difference in height of bumps were scanned by the sensor. Each acrylic resin had a bump with height of 0.01 mm to 0.80 mm processed at the center of the plate. The sensor was pressed at a normal force of 1 N, and then moved along the ridges at a speed of 0.2 m/s. Relationship between the output of the strain gage in the center and height of the bump is shown in Fig. 4. As shown in Fig.4, the relationship is nearly linear, meaning that amplitude of surface geometry can be calculated using the result. Second, acryl plates with ridges of various wavelengths were scanned by the sensor to verify the ability of the sensor to detect wavelength of materials. In this experiment, the sensor was scanned at a speed of 0.02 m/s and at a normal force of 1 N. Then spectral analysis of these outputs was conducted in order to make a quantitative evaluation of the abilities of the sensor. Fig.5 shows the results when sensing ridges with a wavelength of 0.2 mm. A large power spectrum was obtained for a frequency of 5 m-1. Frequency can be calculated by dividing velocity by wavelength as can be expressed in the equation:
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0.1 0.1
8 6 4 2 0
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00
0.2 0.4 0.6 0.8 0.2 0.4 0.6 0.8 Depth of ridges [mm]
0
1
2
3
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Space frequency [m ] Fig. 5. Space frequency sensing.
Acryl
100 100
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Silicon (VP7550) Silicon (SLJ3266)
0.01 0.01 00
0.02 0.02
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Variance of strain gage outputs [‐]
Fig. 6. Softness sensing
Fig. 4. Depth sensing
(1)
where f is frequency, v is velocity, and λ wavelength. The experimental results satisfied this equation. The same could be said for the other wavelengths. As most of the material have wavelength of less than 5 m-1, we can say that the sensor can detect special frequency of material human may touch. B. Estimation of Young’s Modulus In order to verify that the sensor can detect softness of materials using variance of the output, the sensor was pressed into two silicone rubbers having difference in their Young’s modulus and an acrylic plate. From the output of the five strain gages, the variance of the output was calculated. Results are shown in Fig.6. Here, the smaller the result, the less difference there is in the output of each sensor, which suggests the smaller the result, the softer the material is. C. Estimation of coefficient of Dynamic Friction Five materials having difference in friction were scanned by the sensor. Materials used in this experiment were paper, mending tape, two silicone rubbers having difference in Young’s modulus and acrylic resin. These materials were scanned by the sensor at the normal force of 1 N. The coefficient of dynamic friction of each material was calculated using the output of the force sensor placed at the root of the sensor. The order of the measured friction coefficient corresponded with that of the friction coefficient gained by tracing the same materials using human finger. However, the sensor cannot measure the accurate difference of the friction of each material. This is because the friction property of the sensor surface layer, urethane rubber, had large difference from that of human finger. Hence, this problem can be solved by using a material having friction property similar to human finger. VI.
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f=v/λ
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Young's modulus [Mpa]
y = 0.2072x - 0.003 R2 = 0.9921
Powerspectrum [mV2]
Output voltage p-p [mV]
0.2 0.2
perceptual mechanism of human fingers. The sensor was designed to satisfy the requirement such as size equivalent to human finger, durability to withstand the wearing for repeated use, correspondence for scanning two dimensions in order to be equipped with robot hands. By conducting several experiments, it was confirmed that the developed sensor can estimate surface geometry, Young’s modulus and coefficient of dynamic friction of the object it is touching, known to have a large effect on roughness, softness and friction senses, respectively. By utilizing this sensor, we will develop tactile tele-presentation system able to transmit realistic tactile information to remote environment. REFERENCES [1]
[2] [3]
[4]
[5]
[6]
[7] [8]
[9]
CONCLUSION
We have developed a tactile sensor for tactile telepresentation system by emulating the tissue structure and
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R. D. Howe, W. J. Peine, D. A. kontarinis and J. S. Son, ”Remote Palpation Technology”, IEEE Engineering in Medicine and Biology, 1995 Akio Yamamoto et al., Tactile Telepresence System using PVDF Sensors and Electrostatic Stimulator, proc. of IROS2005 Anitonio Bicchi, Enzo P. Schilingo and Danilo De Rossi, ”Haptic Discrimination of Softness in Teleoperation: The Role of the Contact Area Spread Rate”, IEEE Trans. on Robotic and Automation, Vol.16, No.5, October 2000 Ravinder S. Dahiya, Giorgio Metta, Maurizio Valle, Giulio Sandini,Tactile Sensing - From Humans to Humanoids, IEEE Transactions on Robotics.2008 Takashi Maeno, Kazumi Kobayashi and Nobutoshi Yamazaki, Relationship between the Structure of Human Finger Tissue and the Location of Tactile Receptors, Bulletin of JSME International Journal, Vol. 41, No. 1, C, pp. 94-100,1998. H. Shirado and T. Maeno, Modeling of Texture Perception Mechanism for Tactile Display and Sensor, Journal of VR Society of Japan, Vol. 9, No. 3, pp. 235-240, 2004 T. Maeno, D. Yamada and H. Sato, Analysis on Geometry of Human Epidermal Ridges, JSME Journal C, 2005 (in preparation) (in Japanese) T. Maeno, Analysis on Texture Recognition Mechanism of Humans and its Application to Tactile Sensors and Tactile Displays, Journal of the Society of Instrument and Control Engineers (in Japanese), Vol. 47, No. 7, pp. 561- 565, 2008 Yuka Mukaibo, Hirokazu Shirado, Masashi Konyo and Takashi Maeno, Development of a Texture Sensor Emulating the Tissue Structure and Perceptual Mechanism of Human Fingers, Proc. IEEE International Conference on Robotics and Automation, 2005,pp.2576-2581
