A Piezoresistive Tactile Sensor for a Large Area Employing Neural Network

sensors

Article A Piezoresistive Tactile Sensor for a Large Area Employing Neural Network

Youzhi Zhang , Jinhua Ye, Zhengkang Lin, Shuheng Huang, Haomiao Wang and Haibin Wu * School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350116, China; [email protected] (Y.Z.); [email protected] (J.Y.); [email protected] (Z.L.); [email protected] (S.H.); [email protected] (H.W.) * Correspondence: [email protected]; Tel.: +86-189-5049-2523

Received: 6 November 2018; Accepted: 17 December 2018; Published: 21 December 2018

Abstract: Electronic skin is an important means through which robots can obtain external information. A novel flexible tactile sensor capable of simultaneously detecting the contact position and force was proposed in this paper. The tactile sensor had a three-layer structure. The upper layer was a specially designed conductive film based on indium-tin oxide polyethylene terephthalate (ITO-PET), which could be used for detecting contact position. The intermediate layer was a piezoresistive film used as the force-sensitive element. The lower layer was made of fully conductive material such as aluminum foil and was used only for signal output. In order to solve the inconsistencies and nonlinearity of the piezoresistive properties for large areas, a Radial Basis Function (RBF) neural network was used. This includes input, hidden, and output layers. The input layer has three nodes representing position coordinates, X, Y, and resistor, R. The output layer has one node representing force, F. A sensor sample was fabricated and experiments of contact position and force detection were performed on the sample. The results showed that the principal function of the tactile sensor was feasible. The sensor sample exhibited good consistency and linearity. The tactile sensor has only five lead wires and can provide the information support necessary for safe human—computer interactions.

Keywords: electronic skin; contact position detection; contact force detection; RBF neural network

1. Introduction Robotic intelligent technology is booming all over the world, and the tactile sensor is one of the prerequisites for achieving robot intelligence. The tactile sensor, one of the most important wearable devices, is directly related to the intellectualization of the next generation of robots, medical devices, and human prosthesis technologies [1–3]. It has become one of the hottest topics in academic research [4–6]. Based on a variety of transduction principles, such as piezoresistive [7], capacitive [8], piezoelectric [9], electromagnetic [10], and optoelectronic principles [11], etc., researchers have developed different types of tactile sensors capable of detecting one or more kinds of tactile information such as contact position, force, relative sliding, temperature, and roughness. Normally, the parts of the tactile sensor can be divided into those of array structure and the non-array structure. The array tactile sensor integrates a certain number of sensor units, which constructs the detection area. It is mostly designed and manufactured based on micro-electro-mechanical system (MEMS) technology and presents a small structural size, good sensitivity, and high accuracy. For instance, Takao et al. [12] designed a flexible pressure sensor matrix with organic field-effect transistors, and the size of each sensor cell was only 2 mm. The sensor had good flexibility and high spatial resolution. Ahmed et al. [13] developed a magnetic nanocomposite cilia tactile sensor. The sensor could detect pressure as low as 3.5 mN and had an ultrahigh sensitivity. However, the array tactile sensor also

Sensors 2019, 19, 27; doi:10.3390/s19010027 www.mdpi.com/journal/sensors Sensors 2019, 19, 27 2 of 17 has some defects. Normally, the detection is discontinuous. The sensing signals must be acquired by scanning each of the sensing cells, so the spatial resolution and the response time of the sensor are often difficult to balance. In addition, the amount of data needing to be processed by the sensor will be enormous for large area use. The non-array tactile sensor is capable of using a single sensing unit to detect contact information and can be flexibly attached to the surface of the robots. For instance, Dana et al. [14] proposed an artificial skin that detected the position and speed of a slipping object using a single force sensor. The sensor had an average speed sensing resolution of 10 mm/s, an average position sensing resolution of 15 mm, and was robust in its calculation of various grip conditions. Visentin et al. [15] proposed a deformable tactile sensor for continuous sensing based on electrical impedance tomography (EIT). The sensor was made of conductive fabric and can be used for multi-point contact position detection. Compared with the array tactile sensor, the non-array tactile sensor does not need to scan the sensing cells point by point to obtain sensing signals and has fewer lead wires. Thus, the non-array tactile sensor is more suitable for the “electronic skin” of robots. Tactile sensors, as the “electronic skin” of robots, usually are used to flexibly cover relatively large areas of the robot’s surface. Contact position and force are the most important kinds of tactile information for human—robot interaction. At present, researchers have developed some non-array tactile sensors for detecting contact position or force. For instance, Pan et al. [16] proposed a flexible tactile sensor fabricated with conductive fabric that could detect contact position. The sensor could be used to detect contact position over a large area. There were fewer leading wires, and the signal processing was simple. However, the position detection capability of the sensor was still discrete. Wu et al. [17] developed a non-array tactile sensor based on a distributed planar electric field. The sensor was capable of continuous position detection and had good flexibility and simple signal processing, but it could only detect single-point contact. David et al. [18] proposed a tactile sensor fabricated with conductive fabric based on electrical impedance tomography. The sensor could be made into different shapes and used for multi-point contact position detection. Zhang et al. [19] developed a low-cost touch sensing sensor using electric field tomography. The sensor could be integrated onto the surface of an object by spraying so that it could even be used to detect contact positions on the surfaces of 3D objects. However, the EIT and similar technologies require complex mathematical algorithms to obtain the detecting information. It thus needed more electrodes and better computing capacity of the hardware, and its practicability was subject to such restrictions. In addition, Markus et al. [20] proposed a tactile sensor based on piezoresistive conductive rubber. The sensor was made of piezoresistive conductive rubber and copper wire. The copper wire was sewn into the rubber to form a staggered grid, and elastic foam was attached to the surface of the sensor. The sensor was capable of detecting the contact position and contact force. However, it still contained many wires, and the wiring was complicated. Wu et al. [21] designed a tactile sensor based on the resistive and capacitive sensing method. The sensor had a five-layer structure and only six lead wires. It was capable of detecting both the collision position and force, but sensitivity to force detection would decrease with the increase of the sensor’s size. In this paper, we propose a novel non-array thin-film tactile sensor, which can be used for detecting the contact position and force. The sensor has a multi-layer structure and only five lead wires. The sensor has good flexibility, low-cost, favorable robustness, and can be used for full-body robot clothing.

2. Development of the Tactile Sensor

2.1. Structure of the Tactile Sensor

The tactile sensor is a “sandwich” structure with three layers, labeled by L1–L3, respectively, as shown in Figure1.L 1 is the conductive plane designed according to a special structure. It is composed of the strong conductive line, M1, and a weak conductive plane, M2.M1 is coated around Sensors 2019, 19, 27 3 of 17

Sensors 2018, 18, x FOR PEER REVIEW 3 of 17

M2, and the conductivity of M1 is much higher than that of M2. There are four electrodes fitted four corners of M1. When a bias excitation is applied to its two diagonal electrodes, a uniform at four corners of M1. When a bias excitation is applied to its two diagonal electrodes, a uniform electric field will be generated on the weak conductive plane. L2 is the force sensing element, that is, electric field will be generated on the weak conductive plane. L2 is the force sensing element, that is, the piezoresistive film. Its internal resistance decreases with the increase of external pressure. L3 is the piezoresistive film. Its internal resistance decreases with the increase of external pressure. L3 is the the conductive plane with good conductivity, and the whole layer of L3 is used as signal output and conductive plane with good conductivity, and the whole layer of L3 is used as signal output and is is referred to as electrode E. There is a seamless fit between L2 and L3, and a small amount of gas is referred to as electrode E. There is a seamless fit between L2 and L3, and a small amount of gas is filled filled and sealed between L1 and L2. When the electronic skin is not stressed, there is no contact and sealed between L1 and L2. When the electronic skin is not stressed, there is no contact between L1 between L1 and L2. and L2.

Figure 1. The structural model of the tactile sensor. Figure 1. The structural model of the tactile sensor. The single detecting process of the sensor consists of three stages when one point is pressed. In theThe first single stage, detecting a bias excitation process is of applied the sensor to electrodes consists of A andthree C stages of the when sensor, one and point a uniform is pressed. electric In fieldthe first will stage, be generated a bias excitation on the weak is applied conductive to elec planetrodes of LA1 .Land1 will C of be the deformed sensor, and and a will uniform have contactelectric withfield Lwill2 and be L 3generatedat the pressed on the point. weak Next, conductive the potential plane of of the L pressed1. L1 will point be deformed on the weak and conductive will have planecontact of with L1 is extractedL2 and L3 by at Lthe3. Accordingpressed point. to the Next, linear the relationship potential betweenof the pressed position point and on potential the weak in theconductive uniform plane electric of field,L1 is extracted the coordinate by L3. of According the contact to pointthe linear can berelationship easily calculated. between In position the second and stage,potential a bias in the excitation uniform is electric applied field, to electrodes the coordinate B and of D the of contact the sensor, point and can a be uniform easily calculated. electric field In orthogonalthe second to stage, the previous a bias excitation electric field is willapplied be generated to electrodes on the B weak and conductiveD of the sensor, plane ofand L1 .a Similarly, uniform itelectric is easy field to calculate orthogonal the coordinateto the previous of a pressed electric point field by will extracting be generated its potential. on the The weak positions conductive and coordinatesplane of L1 of. Similarly, any pressed it is point easy can to thereforecalculate be the detected. coordinate In the of third a pressed stage, becausepoint by L 1extractingis deformed its andpotential. in contact The withpositions L2 and and L3 coordiat thenates pressed of any point, pressed the sensor point is can able ther toefore calculate be detected. the contact In the force third by extractingstage, because the resistance L1 is deformed between and electrodes in contact C with andE L based2 and L on3 at the the piezoresistive pressed point, properties the sensor of theis able force to sensingcalculate element. the contact force by extracting the resistance between electrodes C and E based on the piezoresistiveThus, the tactileproperties sensor of onlythe force uses sensing a single element. sensing unit to detect contact position and force. Thus, the tactile sensor only uses a single sensing unit to detect contact position and force. 2.2. Contact Position Detection 2.2. ContactThe key Position to position Detection detection is to alternately construct two uniform electric fields in different directionsThe key on theto position weak conductive detection plane is to alternately of L1. In this co paper,nstruct L 1twois composed uniform electric of the strongfields conductivein different line M and the weak conductive plane M .M is coated around M , and the conductivity of M is directions1 on the weak conductive plane2 of1 L1. In this paper, 2L1 is composed of the strong1 much higher than that of M . conductive line M1 and 2the weak conductive plane M2. M1 is coated around M2, and the As shown in Figure2, a bias voltage is applied to the two diagonal points A and C of M , and since conductivity of M1 is much higher than that of M2. 1 the conductivity of M is much greater than that of M , most of the current will flow from C to A in As shown in Figure1 2, a bias voltage is applied to2 the two diagonal points A and C of M1, and M . A potential distribution of the linear increase will be generated from A to C in M . Since M has since1 the conductivity of M1 is much greater than that of M2, most of the current will1 flow from1 C to good contact with M , the potential distribution of the linear increase will also be generated from A to A in M1. A potential2 distribution of the linear increase will be generated from A to C in M1. Since M1 C on the boundary of M , that is the boundary potential distribution necessitated by constructing a has good contact with 2M2, the potential distribution of the linear increase will also be generated uniform electric field. from A to C on the boundary of M2, that is the boundary potential distribution necessitated by constructing a uniform electric field.

Sensors 2018, 18, x FOR PEER REVIEW 4 of 17

Sensors 2019, 19, 27 4 of 17 Sensors 2018, 18, x FOR PEER REVIEW 4 of 17

Figure 2. The working schematic diagram of L1.

2 ϕ Suppose M is a homogeneousFigureFigure 2.2. Theconducting working schematicschematicplane, its diagramdiagram internal ofof LpotentialL11. distribution (,x y ) satisfies the Laplasse equation: ( ) Suppose M22is a homogeneous conducting plane, its internal potential distribution ϕ x, y ϕsatisﬁes Suppose M is a homogeneous conducting22 plane, its internal potential distribution (,x y ) the Laplasse equation: ∂∂ϕϕ satisfies the Laplasse equation: +0= . (1) ∂2∂∂ϕ22∂2 ϕ +xy= 0. (1) ∂2∂∂x22ϕϕ∂2y Using the PDE toolbox of MATLAB, the model+0 of= the. rectangular homogeneous conductive(1) Using the PDE toolbox of MATLAB, the∂∂ model22xy of the rectangular homogeneous conductive plane plane with the boundary potential distribution of linear increase shown in Figure 2 is established. with the boundary potential distribution of linear increase shown in Figure2 is established. Next, Next,Using the potential the PDE distribution toolbox of ofMATLAB, the model the is solved,model ofas shownthe rectangular in Figure homogeneous3. conductive the potential distribution of the model is solved, as shown in Figure3. plane with the boundary potential distribution of linear increase shown in Figure 2 is established. Next, the potential distribution of the model is solved, as shown in Figure 3.

Figure 3. The potential distribution of the rectangular homogeneous conductive plane with the boundaryFigure 3. potentialThe potential distribution distribution of linear of increase.the rectangular homogeneous conductive plane with the boundary potential distribution of linear increase. ◦ AsFigure shown 3. The in Figure potential3, a uniformdistribution electric of the field rectangu is generatedlar homogeneous along the 45conductivedirection plane in the with rectangular the homogeneousboundaryAs shown potential conductive in Figure distribution plane.3, a uniform Therefore, of linear electricincrease. when afield bias is voltage generated is applied along to the the 45° two direction diagonal pointsin the ◦ Arectangular and C of Mhomogeneous1 of L1, a uniform conductive electric plane. field willTherefor be generatede, when a along bias voltage the 45 directionis applied in to M the2 of two L1. Similarly,diagonalAs shown whenpoints ainA bias Figureand voltage C 3,of a isMuniform applied1 of L1, electric toa uniform the two field diagonalelectric is generated field points will Balong andbe Dgeneratedthe of 45° M1 ofdirection along L1, a uniform thein the45° ◦ electricrectangulardirection field in Mhomogeneous will2 of also L1. Similarly, be generated conductive when along a plane.bias the voltage 135 Therefordirection is appliede, when in M to a2 theofbias L two1 ,voltage as diagonal shown is applied in points Figure toB4 . andthe Dtwo of diagonalM1 of L1 , pointsa uniform A and electric C of fieldM1 ofwill L1 ,also a uniform be generated electric along field thewill 135° be generateddirection inalong M2 ofthe L 1,45° as directionshown in inFigure M2 of 4. L 1. Similarly, when a bias voltage is applied to the two diagonal points B and D of M1 of L1, a uniform electric field will also be generated along the 135° direction in M2 of L1, as shown in Figure 4.

Sensors 2019, 19, 27 5 of 17 Sensors 20182018,, 1818,, xx FORFOR PEERPEER REVIEWREVIEW 55 ofof 1717

FigureFigure 4.4. TheThe distribution ofof thethe electricelecelectrictric ﬁeldfieldfield andand potentialpotential inin MM222 ofof L L11...

The potential of any point in M of L can be detected as shown in Figure5. Firstly, a constant The potential of any point in M22 ofof LL11 cancan bebe detecteddetected asas shownshown inin Figure 5. Firstly, a constant DC bias voltage is applied to the two diagonal points A and C in M of L , and a uniform electric DC bias voltage is applied to the two diagonal points A and C in M111 ofof LL11,, andand aa uniformuniform electricelectric field will be generated in M of L . If an external force is applied to the surface of the sensor, L will fieldfield willwill bebe generatedgenerated inin MM22 ofof LL11.. IfIf anan externalexternal forceforce isis appliedapplied toto thethe surfacesurface ofof thethe sensor,sensor, LL11 willwill deform, pass through the airtight layer and contact L and L at the contact point. Next, the potential deform, pass through the airtightrtight layerlayer andand contactcontact2 LL22 andand3 LL33 atat thethe contactcontact point.point. Next,Next, thethe of the whole layer of L will be equal to the potential of the contact point in M of L .L is connected potential of the whole 3layer of L33 willwill bebe equalequal toto thethe potentialpotential ofof thethe contactcontact 2pointpoint1 inin M3M22 ofof LL11.. LL33 isis connectedto an operational to an operational amplifier withamplifier infinite with input infinite impedance inputinput impedanceimpedance so that it issoso able thatthat toitit is outputis ableable toto the outputoutput potential thethe potentialof the contact of the point. contact Similarly, point. Similarly, if a constant if a DCconsta biasnt voltageDC bias is voltage applied is to applied the two to diagonal the two diagonal points B and D in M of L , the potential of the contact point can also be obtained from L . According to the points B and2 D 1in M22 ofof LL11,, thethe potentialpotential ofof thethe contactcontact pointpoint cancan alsoalso bebe 3obtainedobtained fromfrom LL33.. Accordinglinear relationship to the linear between relationship the position between and thethe potentialposition and in the the uniform potential electric in the field, uniform the X electricand Y field,coordinatesfield, thethe X ofandand any Y contactcoordinatescoordinates point ofof in any Many2 of contactcontact L1 can pointpoint be easily inin MM calculated.22 ofof LL11 cancan Thus, bebe easilyeasily the detection calculated.calculated. ofthe Thus,Thus, contact thethe detectionposition can of the be realized.contact position can be realized.

((a))

((b))

Figure 5. PositionPosition detection. detection. (( aa)) PositionPosition detectiondetection in in the the X-axis.-axis. (( bb)) PositionPosition detectiondetection inin thethe Y-axis.-axis.

2.3. Contact Contact Force Force Detection The principle functioning of of contact force detectio detectionn of the sensor is based on the piezoresistive effect of the force sensing element. The whole layer of L2 is made of piezoresistive ﬁlm and used as the effect of the force sensing element. The whole layer of L22 isis mademade ofof piezoresistivepiezoresistive filmfilm andand usedused asas theforcethe forceforce sensing sensingsensing element elementelement of the ofof the sensor.the sensor.sensor. As shownAsAs shownshown in Figure inin Figure6, when 6, when an external an external force force is applied is applied to the to surface of the sensor, L is pressed by L and L at the contact point. The resistance between L and L thethe surfacesurface ofof thethe sensor,sensor,2 LL22 isis pressedpressed1 byby LL11 andand3 LL33 atat thethe contactcontact point.point. TheThe resistanceresistance betweenbetween1 LL311 of the sensor will then decrease with the increase of the external force. Therefore, the contact force can and L33 ofof thethe sensorsensor willwill thenthen decreasedecrease withwith thethe increaincreasese ofof thethe externalexternal force.force. Therefore,Therefore, thethe contactcontact forcebeforce detected cancan bebe by detecteddetected extracting byby extractingextracting the resistance thethe resistanceresistance between electrode betweenbetween electrode Celectrode and electrode CC andand Eelectrodeelectrode of the sensor. EE ofof thethe sensor.sensor.

Sensors 2018, 18, x FOR PEER REVIEW 6 of 17

Sensors 2019, 19, 27 6 of 17 Sensors 2018, 18, x FOR PEER REVIEW 6 of 17

Figure 6. The schematic diagram of contact force detection.

It is worth mentioning that there is an airtight layer between L1 and L2 of the sensor. This makes the resistance betweenFigure 6. L The1 and schematic L3 very diagram high when of contact the forcesensor detection. is not pressed by an external force. Thus, the power consumption of the sensor can be effectively reduced. ItThe isis worthresistanceworth mentioning mentioning model for that that detecting there there is aniscontact airtightan airtight force layer islayer betweenestablished between L1 andas L shown1 Land2 of theLin2 ofFigure sensor. the sensor.7. This When makes This the themakes resistance the resistance between between L and L L1very and highL3 very when high the when sensor the isnot sensor pressed is not by pressed an external by force.an external Thus, sensor is pressed by an external1 3 force, the total resistance R between electrodes C and E of the theforce. power Thus, consumption the power consumption of the sensor of can the be sensor effectively can be reduced. effectivelysensor reduced. sensorThe can resistance be expressed model as forfollows: detecting contact force is established as shown in FigureFigure7 7.. WhenWhen thethe sensor is pressed by an external force, the total=++ resistance Rsensor between electrodes C and E of the sensor is pressed by an external force, theRRRR totalsensor resistance 1 2 Rsensor 3 between electrodes C and E of the(2) sensor can be expressed as follows: sensor can be expressed as follows: where R1 is the resistance between the contact point in M2 of L1 and electrode C, which is related Rsensor ==++R1 + R2 + R3 (2) RRRRsensor 1 2 3 (2) to the position of the contact point. R2 is the resistance between L1 and L3 at the contact point. where R1 is the resistance between the contact point in M2 of L1 and electrode C, which is related to whereIgnoring R 1the is contactthe resistance resistance, between R2 the can contactbe regarded point asin theM2 ofresistance L1 and electrode of the piezoresistive C, which is relatedfilm at the position of the contact point. R2 is the resistance between L1 and L3 at the contact point. Ignoring tothe the contact position point, of thewhich contact decreases point. withR2 isthe the increase resistance of thebetween external L1 andforce. L3 atR the is contactthe resistance point. the contact resistance, R2 can be regarded as the resistance of the piezoresistive3 ﬁlm at the contact point,Ignoringbetween which the decreasescontactcontact pointresistance, with in the L3 increase andR2 electrode can of be the regarded externalE. Since as force.L 3the has Rresistance good3 is the conductivity, resistance of the piezoresistive between R3 is thevery contactfilm small at point in L3 and electrode E. Since L3 has good conductivity, R3 is very small and negligible. theand contactnegligible. point, which decreases with the increase of the external force. R3 is the resistance between the contact point in L3 and electrode E. Since L3 has good conductivity, R3 is very small and negligible.

Figure 7.7. The resistance model forfor detectingdetecting contactcontact force.force.

TheThe detecting circuit of of the the contact contact force force can can be be re realizedalized by by a simple a simple resistor resistor divider divider circuit, circuit, as as shown in Figure8. TheFigure sensor 7. The (represented resistance model as Rsensor for detectingin Figure contact8) is force. connected in series with the shown in Figure 8. The sensor (represented as Rsensor in Figure 8) is connected in series with the ﬁxed resistance R0, and a constant voltage Uconst is applied at both ends of the circuit; then the output fixed resistance R , and a constant voltage U is applied at both ends of the circuit; then the voltageTheU detectingsensor is: 0circuit of the contact force canconst be realized by a simple resistor divider circuit, as Uconst · Rsensor shownoutput involtage Figure U 8.sensor The is:sensor (representedU =as Rsensor in Figure. 8) is connected in series with the (3) sensor R + R R U 0 sensor fixed resistance 0 , and a constant voltage constUR is ⋅applied at both ends of the circuit; then the Finally, Usensor is transmitted to the microcontroller= const by sensor the A/D conversion so that the contact Usensor . (3) forceoutput can voltage be detected. Usensor is: + RR0sensor UR⋅ U = const sensor sensor + . (3) RR0sensor

Sensors 2018, 18, x FOR PEER REVIEW 7 of 17

Finally, Usensor is transmitted to the microcontroller by the A/D conversion so that the contact Sensors 2019, 19, 27 7 of 17 force can be detected.

FigureFigure 8.8. The schematic diagram ofof thethe contactcontact forceforce detectiondetection circuit.circuit. 2.4. Sample Fabrication 2.4. Sample Fabrication In order to further verify the feasibility of the tactile sensor, a sample of the sensor was fabricated. In order to further verify the feasibility of the tactile sensor, a sample of the sensor was Conductive silver paste was used as the strong conductive line, M1 of L1, and the conductive indium-tin fabricated. Conductive silver paste was used as the strong conductive line, M1 of L1, and the oxide polyethylene terephthalate (ITO-PET) film was used as the weak conductive plane, M of L . conductive indium-tin oxide polyethylene terephthalate (ITO-PET) film was used as the2 weak1 Velostat is a conductive film made of polyolefins foil impregnated with carbon black, produced by the conductive plane, M2 of L1. Velostat is a conductive film made of polyolefins foil impregnated with 3M company. It has favorable piezoresistive properties. Therefore, the Velostat film was used as the carbon black, produced by the 3M company. It has favorable piezoresistive properties. Therefore, force sensing element of the sensor. The detailed fabrication process is as follows: the Velostat film was used as the force sensing element of the sensor. The detailed fabrication process is as follows: 2 (1) Upper layer L1: the ITO-PET film with sheet a resistance of 100 Ω/cm and the thickness of × (1) Upper0.175 mm layer (where L1: the the ITO-PET thickness film of the with ITO sheet film isa 70resistance nm) was of cut 100 to aΩ square/cm2 and with the 100 thickness100 mm. of 7 0.175The conductive mm (where silver the thickness paste with of a the conductivity ITO film is of 70 about nm) 2.4was× cut10 toS/m a square was printed with 100 with × 100 screen mm. Theprinting conductive on the ITO-PETsilver paste film. with Four a aluminumconductivity electrodes of about were2.4 × fitted107 S/m onto was the printed four corners with screen of the printingrectangular on circuitthe ITO-PET printed film. with Four conductive aluminum silver electrodes paste. were fitted onto the four corners of (2) theMiddle rectangular layer L circuit2: The printed Velostat with film conductive with a thickness silver paste. of 0.1 mm was cut into a square with (2) 100Middle× 100 layer mm L.2 A: The double-sided Velostat film glued with grid a thickness was used of to 0.1 bond mm layers was cut L1 andinto La 2squaretogether with to form100 × 100an airtight mm. A isolation double-sided layer. Theglued grid grid had was a thickness used to ofbond 0.3 mmlayers and L1 mesh and sizeL2 together of 25 × 25to mm.form an (3) airtightLower layer isolation L3: The layer. aluminum The grid foil had with a thickness good conductivity of 0.3 mm was and cut mesh into size a square of 25 of× 10025 mm.× 100 mm (3) Lowerdimensions. layer AL3 PET: The film aluminum was attached foil with to one good side conductivity of the aluminum was cut foil into as an a insulating square of protective100 × 100 mmlayer. dimensions. An electrode A wasPET fitted film ontowas theattached edge ofto theone aluminum side of the foil aluminum for signal foil output. as an insulating (4) protectiveAssembling: layer. The An 3M-9495# electrode double-sided was fitted onto adhesive the edge was of attached the aluminum to the edgefoil for of signal the lower output. layer, (4) Assembling:and the upper, The middle, 3M-9495# and lowerdouble-sided layers were adhesive aligned was and attached closely to bonded. the edge The of sensorthe lower sample layer, is andshown the in upper, Figures middle,9 and 10 and. lower layers were aligned and closely bonded. The sensor sample Sensorsis shown 2018, 18in, xFigures FOR PEER 9 REVIEW and 10. 8 of 17

FigureFigure 9.9. TheThe sensor sample. sample.

Figure 10. The thickness of the sensor sample.

2.5. Piezoresistive Properties of the Velostat Film The sensor detects the contact force based on the piezoresistive properties of the force sensing element. As shown in Figure 9, the sensor used a large piece of Velostat film as the force sensing element. Ideally, the piezoresistive properties of all regions of the Velostat film should be consistent. Then, the mapping relationship between force and resistance can be tested. According to the mapping relationship, the contact force can be easily calculated. Therefore, it is necessary to further study the consistency of the piezoresistive properties of the Velostat film. A simple test device was designed to test the consistency of the piezoresistive properties of the Velostat film. The test device is shown as Figure 11. First, a piece of the Velostat film with 100 × 100 mm dimensions was divided into sixteen small squares of 25 × 25 mm size, and two pieces of aluminum foil were used as electrodes to clamp the Velostat film. Then, a force of 1–15 N was applied to them, respectively, and the resistances between the two electrodes were recorded by a multimeter. Two rubber blocks were used to insulate the aluminum foils from the outside. The size of the aluminum foil electrode A was 15 × 15 mm, and the size of the aluminum foil electrode B was 100 × 100 mm. The mapping relationship between the force and resistance of the sixteen small squares of the Velostat film is shown in Figure 12.

Sensors 2018, 18, x FOR PEER REVIEW 8 of 17

Sensors 2019, 19, 27 8 of 17 Figure 9. The sensor sample.

Figure 10. The thickness of the sensor sample.

2.5. Piezoresistive Properties of the Velostat Film The sensor detects the contact force based on the piezoresistive properties of the force sensing element. As As shown shown in in Figure Figure 99,, thethe sensorsensor usedused aa largelarge piecepiece ofof VelostatVelostat filmfilm asas thethe forceforce sensingsensing element. Ideally, the piezoresistive properties of all regions of the Velostat filmfilm shouldshould bebe consistent.consistent. Then, thethe mapping mapping relationship relationship between between force force and resistanceand resistance can be can tested. be Accordingtested. According to the mapping to the mappingrelationship, relationship, the contact the force contact can beforce easily can calculated.be easily calculated. Therefore, Therefore, it is necessary it is necessary to further to study further the studyconsistency the consistency of the piezoresistive of the piezoresistive properties properties of the Velostat of the film. Velostat film. A simple simple test test device device was was designed designed to to test test the the consistency consistency of the of the piezoresistive piezoresistive properties properties of the of Velostatthe Velostat film. film. The test The device test device is shown is shown as Figure as Figure11. First, 11 a. piece First, of a the piece Velostat of the film Velostat with film100 × with 100 100mm× dimensions100 mm dimensions was divided was dividedinto sixteen into sixteensmall squares small squares of 25 of× 25 25 ×mm25 size, mm size,and andtwo two pieces pieces of aluminumof aluminum foil foil were were used used as aselectrodes electrodes to toclamp clamp the the Velostat Velostat film. film. Then, Then, a aforce force of of 1–15 1–15 N was applied to them, respectively, and the resistances between the two electrodes were recorded by a multimeter. TwoTwo rubberrubber blocksblocks were were used used to to insulate insulate the the aluminum aluminum foils foils from from the the outside. outside. The The size size of ofthe the aluminum aluminum foil foil electrode electrode A A was was 15 15× ×15 15 mm, mm, and and thethe sizesize ofof thethe aluminumaluminum foilfoil electrodeelectrode B was 100 × 100 mm. The mapping relationship between the force and resistance of the sixteen small squares 100Sensors × 2018100 , mm.18, x FOR The PEER mapping REVIEW relationship between the force and resistance of the sixteen 9small of 17 squaresof the Velostat of the Velostat film is shown film is in shown Figure in 12 Figure. 12.

Figure 11. Test device for testing the consistency of the piezoresistive properties of the Velostat film. Figure 11. Test device for testing the consistency of the piezoresistive properties of the Velostat film. As shown in Figure 12, there are sixteen piezoresistive properties curves, which are obviously different. Therefore, the piezoresistive properties of the sixteen small squares of the Velostat film are inconsistent. The main reasons for the differences are that the distribution of conductive particles in the interior of the Velostat film is uneven and the contact resistance between the Velostat and aluminum foil of different small squares are different.

Figure 12. The mapping relationship between the force and resistance of the sixteen small squares of the Velostat film.

As shown in Figure 12, there are sixteen piezoresistive properties curves, which are obviously different. Therefore, the piezoresistive properties of the sixteen small squares of the Velostat film are inconsistent. The main reasons for the differences are that the distribution of conductive particles in the interior of the Velostat film is uneven and the contact resistance between the Velostat and aluminum foil of different small squares are different. Therefore, if the average of the sixteen piezoresistive properties curves is used to calibrate the sensor for detecting the contact force, it will generate a large error of the contact force detection. In addition, the resistance R1 , shown in Figure 7, is also related to the position of the contact point. In summary, the contact force detection of the sensor is closely related to the position of the contact point. In order to solve this problem, on the premise of obtaining the position of the contact point, the Radial Basis Function (RBF) neural network algorithm was used to train and calibrate the tactile sensor for detecting the contact force.

3. Detection Algorithm Using RBF Neural Network

3.1. RBF Neural Network Artificial Neural Networks (ANN) have been a hotspot in the field of artificial intelligence and have been widely applied for pattern recognition, intelligent robots, automatic controls, biology, medicine, economics, etc. Some papers on tactile sensors using a neural network for texture recognition [22], contact shape recognition [23], three-dimensional force decoupling [24,25], etc., have been reported. For instance, Ken et al. [26] developed a neurorobotic texture classifier with a recurrent spiking neural network which could classify surface textures through touch. Anany et al. [27] designed a soft magnetic 3-D force sensor. The sensor was a pyramid-shaped tactile unit featuring a tri-axis Hall element and a magnet embedded in a silicone rubber substrate. The

Sensors 2018, 18, x FOR PEER REVIEW 9 of 17

Sensors 2019, 19, 27 9 of 17

Therefore, if the average of the sixteen piezoresistive properties curves is used to calibrate the sensor for detecting the contact force, it will generate a large error of the contact force detection. In addition, the resistance R1, shown in Figure7, is also related to the position of the contact point. In summary, the contact force detection of the sensor is closely related to the position of the contact point. In order to solve this problem, on the premise of obtaining the position of the contact point, the Radial Basis Function (RBF) neural network algorithm was used to train and calibrate the tactile sensorFigure for detecting 11. Test device the contact for testing force. the consistency of the piezoresistive properties of the Velostat film.

Figure 12. The mapping relationship between the force andand resistance of thethe sixteensixteen smallsmall squaressquares of the VelostatVelostat film.film. 3. Detection Algorithm Using RBF Neural Network As shown in Figure 12, there are sixteen piezoresistive properties curves, which are obviously 3.1.different. RBF Neural Therefore, Network the piezoresistive properties of the sixteen small squares of the Velostat film are inconsistent. The main reasons for the differences are that the distribution of conductive particlesArtificial in the Neural interior Networks of the (ANN)Velostat have film been is uneven a hotspot and in thethe fieldcontact of artificial resistance intelligence between andthe haveVelostat been and widely aluminum applied foil forof different pattern recognition,small squares intelligent are different. robots, automatic controls, biology, medicine,Therefore, economics, if the average etc. Some of the papers sixteen on piezoresis tactile sensorstive properties using acurves neural is networkused to calibrate for texture the recognitionsensor for detecting [22], contact the contact shape force, recognition it will [genera23], three-dimensionalte a large error of forcethe contact decoupling force detection. [24,25], etc., In have been reported. For instance, Ken et al. [26] developed a neurorobotic texture classifier with a addition, the resistance R , shown in Figure 7, is also related to the position of the contact point. In recurrent spiking neural network1 which could classify surface textures through touch. Anany et al. [27] designedsummary, a the soft contact magnetic force 3-D detection force sensor. of the The sensor sensor iswas closely a pyramid-shaped related to the position tactile unit of the featuring contact a tri-axispoint. In Hall order element to solve and this a magnet problem, embedded on the premise in a silicone of obtaining rubber substrate. the position The of non-linear the contact mapping point, betweenthe Radial the Basis 3-D Function force vector (RBF) and neural the Hallnetwork effect algo voltagesrithm was was used characterized to train and by calibrate training the a neuraltactile network.sensor for Indetecting this paper, the contact the RBF force. neural network was used as a contact force detection algorithm. The mapping between the output resistance, contact position, and the applied force was established by 3. Detection Algorithm Using RBF Neural Network training the RBF neural network. The structure of the Radial Basis Function (RBF) neural network is shown in Figure 13. The RBF 3.1. RBF Neural Network neural network is a three-layer feedforward network with a single hidden layer. It consists of an input layer,Artificial a hidden Neural layer, andNetworks an output (ANN) layer. have The been hidden a hotspot layer in can the transform field of artificial the input intelligence vectors of and the inputhave been layer widely and map applied the input for samplepattern datarecognition, of the low-dimensional intelligent robots, mode automatic into the high-dimensionalcontrols, biology, space.medicine, The economics, nodes of the etc. hidden Some layer papers are then on weighted.tactile sensors Finally, using the sumsa neural of the network weighted for nodes texture are outputrecognition by the [22], output contact layer. shape Therefore, recognition the RBF [23], neural three-dimensional network has the abilitiesforce decoupling necessary for[24,25], mapping etc., ahave nonlinear been reported. separable For space instance, into a linearly-separableKen et al. [26] developed space. a neurorobotic texture classifier with a recurrent spiking neural network which could classify surface textures through touch. Anany et al. [27] designed a soft magnetic 3-D force sensor. The sensor was a pyramid-shaped tactile unit featuring a tri-axis Hall element and a magnet embedded in a silicone rubber substrate. The

Sensors 2018, 18, x FOR PEER REVIEW 10 of 17

non-linear mapping between the 3-D force vector and the Hall effect voltages was characterized by training a neural network. In this paper, the RBF neural network was used as a contact force detection algorithm. The mapping between the output resistance, contact position, and the applied force was established by training the RBF neural network. The structure of the Radial Basis Function (RBF) neural network is shown in Figure 13. The RBF neural network is a three-layer feedforward network with a single hidden layer. It consists of an input layer, a hidden layer, and an output layer. The hidden layer can transform the input vectors of the input layer and map the input sample data of the low-dimensional mode into the high-dimensional space. The nodes of the hidden layer are then weighted. Finally, the sums of the weighted nodes are output by the output layer. Therefore, the RBF neural network has the abilities Sensors 2019, 19, 27 10 of 17 necessary for mapping a nonlinear separable space into a linearly-separable space.

Figure 13.13. The schematic diagram of the RadialRadial BasisBasis FunctionFunction (RBF)(RBF) neuralneural network.network.

TheThe hiddenhidden layerlayer is is a a set set of of radial radial basis basis functions. functions. The The commonly commonly used used radial radial basis basis function function is the is Gaussthe Gauss function: function: " # kx − c k h = − t i (i = ··· n) i exp 2− 1, 2, , (4)  2xσtic  h =−exp i (1,2,,)in=  (4) i σ 2 T 2 i where xt = (xt1, xt2, ··· , xtm) , t = 1, 2, ···, n, is the tth input vector of the input layer, and n is the total number of the input vectors,T c is the center of the ith radial basis function, σ is the extended where x ==(,xx ,, x ),1,2,, ti n, is the tth input vector of the input layer,i and n is the constant ofttttm the center12 of the ith radial basis function, which determines the width of the basis function σ aroundtotal number the center of point,the inputci, k xvectors,t − cik is theci normis the of center the vector of thext −ithci ,radial representing basis thefunction, distance betweeni is the xextendedt and ci. constant of the center of the ith radial basis function, which determines the width of the Thus, the mapping function from the input layer− nodes to the output layer nodes can− be basis function around the center point, ci , xtic is the norm of the vector xtic , expressed as: n representing the distance between xt and ci . 1 = (− k − k) yt ∑ wi exp 2 xt ci (5) Thus, the mapping function fromi= the1 input 2layerσ nodes to the output layer nodes can be expressed as: where wi is the weight of the hidden layer mapping to the output layer, and yt is the output vector of n the output layer corresponding to the input vector, xt. 1 yw=−−exp( xc ) (5) Let Yt is the target vector of the RBFti neural network correspondingσ 2 ti to the input vector xt, and mean i=1 2 square error γ of the RBF neural network can be expressed as: w y where i is the weight of the hidden layer mapping to2 the output layer, and t is the output 1 n γ = kY − y k . (6) vector of the output layer corresponding to the∑ inputt vector,t xt . n t=1 Let Yt is the target vector of the RBF neural network corresponding to the input vector xt , Therefore, it is only necessary to learn and train three parameters of the RBF neural network so and mean square error γ of the RBF neural network can be expressed as: that the RBF neural network is able to establish the mapping relationship between the input vectors and the target vectors. In other words, the input sample set and the target sample set are used to train the RBF neural network, and the three parameters of the RBF neural network are constantly revised so that the output vectors of the RBF neural network can keep approaching the target vectors until the error expectation is reached. The three parameters are the center of the radial basis function ci, the extended constant of the center of the radial basis function σi, and the weight of the hidden layer to the output layer wi. The self-organizing selection center method was used for training the RBF neural network in this paper. The method is detailed as follows:

(1) the k-means clustering algorithm was used to obtain cluster centers ci, which were used as the center of the radial basis function of the RBF neural network; Sensors 2019, 19, 27 11 of 17

(2) the Gauss function was used as the radial basis function, and the extended constant σi can be calculated from the following relation:

dmax σi = √ (i = 1, 2, ··· , N) (7) 2N

where N is the number of the hidden nodes and dmax is the maximum distance between the selected cluster centers;

(3) the weights of the hidden layer to the output layer wi were calculated by using the least square method.

3.2. Simulation The RBF neural network model was established in MATLAB, where the input layer had three nodes, and the output layer had one node. The number of the nodes of the hidden layer was determined by the mean square error γ of the RBF neural network. The smaller the γ is, the higher the precision of the model is, and the more nodes of the hidden layer are required. That is, if the error expectation is not reached, the number of the nodes of the hidden layer should be increased in the program. The radial basis function of the hidden layer was Gaussian function, and the training algorithm was the K-means clustering algorithm. The sample data (shown in Figure 12) were used for training the RBF neural network. The resistances of the sixteen piezoresistive properties curves and the position coordinates X and Y of the sixteen small squares of the Velostat film were used as the three input vectors of the RBF neural network. The forces applied to the sixteen small squares of the Velostat film were used as the target vector of the RBF neural network. The input vectors and target vectors were used for training the RBF neural network so that the trained network was able to establish the mapping relationship between the input vectors (including the resistances and the position coordinates X and Y) and the target vector (including the force applied). A rectangular coordinate system was established in the Velostat film, as shown in Figure 11. The position coordinates of the center of the sixteen small squares, {xi, yi}, where i = 1, ··· , 16, were used as the two input vectors. The resistance of the sixteen piezoresistive properties curves Rij, where i = 1, ··· , 16; j = 1, ··· , 8, were used as the third input vector. The force applied to the Velostat film Fi,j, where i = 1, ··· , 16; j = 1, ··· , 8, were used as the target vector. In order to make the neural network converge better, the input vectors and target vector should be normalized first by the following formula:

 R −R  R = ij i,min ij Ri,max−Ri,min F −F (i = 1, 2, ··· , 16; j = 1, 2, ··· , 8) (8)  F = max ij ij Fmax−Fmin where Rij, Fi are the normalized vectors, Ri,max, Ri,min are the maximum and the minimum sample data of the resistance of the ith small square, and Fmax, Fmin are the maximum and the minimum force applied to the Velostat ﬁlm. The normalized sample data is shown in Figure 14. Finally, the normalized input vectors and target vector were used for training the RBF neural network. Let the mean square error γ = 0.001. Then, the parameters of the RBF neural network were constantly revised by training so that the output vectors of the trained RBF neural network could keep approaching the target vector until the error expectation was reached. The output of the trained RBF neural network was shown in Figure 15. Sensors 2018, 18, x FOR PEER REVIEW 12 of 17

In order to make the neural network converge better, the input vectors and target vector should be normalized first by the following formula: −  RRij i,min Rij =  −  RRii,max ,min Sensors 2018, 18, x FOR PEER REVIEW (ij== 1,2, 16; 1,2, 8) 12 of 17  − (8)  FFmax ij F ij = In order to make the neuralFF network− converge better, the input vectors and target vector should be normalized first by the followingmax min formula:

Rij F i − R R where , are the normalized RRij vectors, i,min i,max , i,min are the maximum and the minimum Rij =  − sample data of the resistance of RRtheii,max ith small ,min square, and Fmax , Fmin are the maximum and the (ij== 1,2, 16; 1,2, 8) (8) minimum force applied to the Velostat film.−  FFmax ij F ij = The normalized sample data is shown− in Figure 14.  FFmax min where Rij , F i are the normalized vectors, Ri,max , Ri,min are the maximum and the minimum sample data of the resistance of the ith small square, and Fmax , Fmin are the maximum and the minimum force applied to the Velostat film. Sensors 2019, 19, 27 12 of 17 The normalized sample data is shown in Figure 14.

Figure 14. The normalized sample data.

Finally, the normalized input vectors and target vector were used for training the RBF neural network. Let the mean square error γ =0.001 . Then, the parameters of the RBF neural network were constantly revised by training so that the output vectors of the trained RBF neural network could keep approaching the target vector until the error expectation was reached. The output of the trained RBF neural network wasFigure shown 14.14. in The Figure normalized 15. sample data.data.

Figure 15. The output of the trained RBF neural network.

Comparing Figures 14 and 15, the relationship between the resistance and the force were linearized effectively, and the consistency of the piezoresistive properties curves of the sixteen small squares was improved. The maximum nonlinear error was reduced from 58.3% to 4.5%. The maximum relative standard deviation was reduced from 29.1% to 8.6%. It is worth noting that the consistency of the piezoresistive properties curves can be further improved by reducing the mean square error. Therefore, on the premise of obtaining the contact point position, the RBF neural network algorithm is suitable to be used for training and calibrating the tactile sensor for the purpose of detecting the contact force.

4. System Application Based on the PIC18F25K80 chip, we built the signal processing circuit of the tactile sensor. The signal processing circuit used a 6 V battery as an external power supply that made the use of the sensor more convenient. The display interface of the sensor was developed by LABVIEW. The tactile sensor system could display the position and force of the contact point in real time by directly calling the trained RBF neural network. It is worth mentioning that the sensor used a DC constant current, and the input current was only 10 mA. Therefore, the power consumption of the sensor system was very small. The signal processing system of the tactile sensor is shown in Figure 16. Sensors 2018, 18, x FOR PEER REVIEW 13 of 17

Figure 15. The output of the trained RBF neural network.

4. System Application Based on the PIC18F25K80 chip, we built the signal processing circuit of the tactile sensor. The signal processing circuit used a 6 V battery as an external power supply that made the use of the sensor more convenient. The display interface of the sensor was developed by LABVIEW. The tactile sensor system could display the position and force of the contact point in real time by directly calling the trained RBF neural network. It is worth mentioning that the sensor used a DC constant current, and the input current was only 10 mA. Therefore, the power consumption of the sensorSensors system2019, 19 was, 27 very small. The signal processing system of the tactile sensor is shown13 in of Figure 17 16.

FigureFigure 16. 16. StructureStructure diagram diagram of the signalsignal processing processing system. system.

It shouldIt should be benoted noted out out that thethe contact contact area area of the of tactilethe tactile sensor sensor has a great has influence a great oninfluence the contact on the force detection. Considering that people use finger touch sensors for human–computer interaction, contact force detection. Considering that people use finger touch sensors for human–computer the contact area is about 15 × 15 mm, so we used a rubber block with 15 × 15 mm dimensions to interaction, the contact area is about 15 × 15 mm, so we used a rubber block with 15 × 15 mm simulate a human finger to calibrate the tactile sensor for detecting contact force. dimensionsFirst, to thesimulate rubber a block human was fi placednger to at thecalibrate center ofthe the tactile sixteen sensor small for squares detecting of the contact sensor sample, force. andFirst, a forcethe rubber of 3 to 15block N was was applied placed to theat the rubber cent blocker of bythe the sixteen ZQ-20B-1 small spring squares testing of machine. the sensor sample,The outputand a resistancesforce of 3 betweento 15 N electrodes was applied C and to E ofthe the rubber tactile sensorblock andby the positionZQ-20B-1 coordinates spring testing of machine.the contact The output point were resistances used as the between three input electrodes vectors of C the and RBF E neuralof the network.tactile sensor The force and applied the position to coordinatesthe rubber of blockthe contact was used point as the were target used vector as of th thee RBFthree neural input network. vectors Next, of the the RBF input neural vectors network. and The forcetarget applied vector were to the normalized rubber block and used was for used training as th thee target RBF neural vector network. of the RBF Finally, neural the trainednetwork. RBF Next, the inputneural vectors network and was target stored vector in a MATLAB were normalized file for the tactile and sensorused for system training to call. the Thus, RBF the neural calibration network. Finally,of the the contact trained force RBF detection neural of network the tactile was sensor stored was in completed. a MATLAB file for the tactile sensor system to call. Thus,The experimentsthe calibration detecting of the position contact and force force detection were carried of the out tactile using sensor the calibrated was completed. tactile sensor system, and the test device is shown in Figure 17a. First, contact position detection was carried out. The experiments detecting position and force were carried out using the calibrated tactile The rubber block was placed at the center of the sixteen small squares of the sensor, and a 7 N force sensorwas system, applied and to the the rubber test blockdevice by theis shown ZQ-20B-1 in springFigure testing 17a. machine.First, contact The contact position position detection of the was carriedtactile out. sensor The rubber system block output was was placed shown at in Figurethe cent 18era. of Contact the sixteen force detection small squares was then of the carried sensor, out. and a 7 NFour force dispersed was applied small squares to the (shownrubber in block Figure by 17 a)the were ZQ-20B-1 selected spring to be subjected testing tomachine. be applied The forces contact positionof 3–15 of N.the The tactile contact sensor force system of the tactile output sensor was system shown output in Figure was shown 18a. Contact in Figure force 18b. detection was then carriedIn addition, out. Four by dispersed the means ofsmall the polynomialsquares (shown curve fit,in Figure the average 17a) ofwere the sixteenselected piezoresistive to be subjected to beproperties applied curvesforces (shownof 3–15 in N. Figure The 12contact) was usedforce to of calibrate the tactile the sensor sensor for system detecting output the contact was shown force. in FigureThe 18b. fourth position (shown in Figure 17a) of the tactile sensor was tested by the two algorithms. The sensor outputs were shown in Figure 18c. Furthermore, the sensor was pasted onto the curved panel with a radius of curvature of 200 mm,

as shown in Figure 17b and video S1 (Supplementary Materials). When we used the finger to touch the sensor, the tactile sensor system could display the contact position and force in real time. The experiment showed that the maximum measurement error of the contact force detection was not more than 5 mm. The main reasons for the error were that the silver paste circuit featured a degree of unevenness, and the voltage drop distribution of the circuit was not uniform, resulting in non-uniform gradient distribution of the potential in the conductive plane. The maximum measurement error of the contact force detection was not more than 1 N. The main reasons for the error were: (1) the force applied by the spring testing machine was not accurate enough. (2) The Velostat film is a viscoelastic material with creep properties, and its internal resistance will change with time when it is applied with a constant force. We used the resistance of the Velostat film after it was applied the force for about 1 s as the sample data. However the sampling time was not accurate enough. (3) The RBF neural network model itself has errors. In comparison of the RBF neural network algorithm with the polynomial fitting algorithm, the maximum error of the contact force detection was reduced from 2.7 N to 0.5 N. SensorsSensors 2018 2018, ,18 18, ,x x FOR FOR PEER PEER REVIEW REVIEW 1414 of of 17 17

InIn addition, addition, by by the the means means of of the the polynomial polynomial curv curvee fit, fit, the the average average of of the the sixteen sixteen piezoresistive piezoresistive propertiesproperties curvescurves (shown(shown inin FigureFigure 12)12) waswas usedused toto calibratecalibrate thethe sensorsensor forfor detectingdetecting thethe contactcontact force.force. TheThe fourthfourth positionposition (shown(shown inin FigureFigure 117a)7a) ofof thethe tactiletactile sensorsensor waswas testedtested byby thethe twotwo algorithms.algorithms. The The sensor sensor outputs outputs were were shown shown in in Figure Figure 18c. 18c. Furthermore,Furthermore, the the sensor sensor was was pasted pasted onto onto the the cu curvedrved panel panel with with a a radius radius of of curvature curvature of of 200 200 Sensorsmm,mm, as 2019as shown shown, 19, 27 in in Figure Figure 17b. 17b. When When we we used used the the fing fingerer to to touch touch the the sensor, sensor, the the tactile tactile sensor sensor system 14system of 17 couldcould display display the the contact contact position position and and force force in in real real time. time.

(a(a) )

(b(b) )

FigureFigure 17. 17. TheThe The test testtest device. device.device. ( a( (a)a ))The The The tactile tactile tactile sensor sensor sensor was was was used used used to to todetect detect detect the the the contact contact contact position position position and and and force; force; force; ( b(b) ) (usingbusing) using a a finger finger a ﬁnger to to touch totouch touch the the thetactile tactile tactile sensor sensor sensor wh wh whenenen it it was was it was pasted pasted pasted onto onto onto the the the curved curved curved panel. panel. panel.

(a(a) )

Figure 18. Cont. Sensors 2019, 19, 27 15 of 17 Sensors 2018, 18, x FOR PEER REVIEW 15 of 17

(b)

(c)

Figure 18. The results results of of the the experiments. experiments. (a (a) )The The result result of of contact contact position position detection; detection; (b ()b the) the result result of ofcontact contact force force detection; detection; (c) comparison (c) comparison of the of RBF the RBFneural neural network network output output with the with polynomial the polynomial fitting ﬁttingoutput. output.

5. ConclusionsThe experiment showed that the maximum measurement error of the contact force detection was notA novel more flexible than 5 tactilemm. The sensor main that reasons can detect for the the error contact were position that the and silver force paste was circuit proposed featured in this a paper.degree Theof unevenness, sensor was composedand the voltage of three drop thin-film distribu layers.tion of It the had circuit a simple was structure, not uniform, good resulting flexibility, in smallnon-uniform thickness gradient and only distribution five lead wires. of the In orderpotential to solve in thethe inconsistency conductive andplane. nonlinearity The maximum of the piezoresistivemeasurement propertieserror of the for contact large areas,force andetection RBF neural was networknot more was than used 1 N. as The a contact main reasons force detection for the algorithm.error were: The (1) sensorthe force sample applied was by made. the spring Experiments testing on machine the contact wasposition not accurate and force enough. detection (2) The of theVelostat sensor film sample is a viscoelastic were carried material out. The with experimental creep properties, results showed and its thatinternal the principal resistance functioning will change of thewith sensor time waswhen feasible. it is applied The tactile with sensora constant was capableforce. We of used detecting the resistance position and of forcethe Velostat for a single film pointafter andit was could applied easily the be usedforce tofor cover about the 1 surfacess as the ofsample objects. data. The However RBF neural the network sampling algorithm time was is very not effectiveaccurate forenough. correcting (3) The the inconsistencyRBF neural network and nonlinearity model itself of the has tactile errors. sensor. In comparison The maximum of the relative RBF standardneural network deviation algorithm was reduced with fromthe polynomial 29.1% to 8.6%. fitting The algorithm, maximum the nonlinear maximu errorm error was of reduced the contact from 58.3%force detection to 4.5%. Comparedwas reduced with from the 2.7 polynomial N to 0.5 N. fitting algorithm, the maximum error of the contact force detection was reduced from 2.7 N to 0.5 N. The sensor can provide the necessary information to 5. Conclusions support safe human–robot interactions. A novel flexible tactile sensor that can detect the contact position and force was proposed in Supplementary Materials: The following are available online at http://www.mdpi.com/1424-8220/19/1/27/ s1this,Video paper. S1: ExperimentalThe sensor video.was composed of three thin-film layers. It had a simple structure, good flexibility, small thickness and only five lead wires. In order to solve the inconsistency and nonlinearity of the piezoresistive properties for large areas, an RBF neural network was used as a contact force detection algorithm. The sensor sample was made. Experiments on the contact position and force detection of the sensor sample were carried out. The experimental results

Sensors 2019, 19, 27 16 of 17

Author Contributions: Conceptualization, Y.Z. and H.W. (Haibin Wu); Methodology, Y.Z.; Software, Z.L. and Y.Z.; Validation, J.Y., Z.L., and H.W. (Haomiao Wang) and S.H.; Formal analysis, Y.Z.; Investigation, H.W. (Haomiao Wang); Resources, J.Y.; Data curation, Y.Z.; Writing—original draft preparation, Y.Z.; Writing—review & editing, Y.Z. and H.W. (Haibin Wu); Visualization, S.H.; Supervision, J.Y.; Project administration, H.W. (Haibin Wu); Funding acquisition, H.W. (Haibin Wu). Funding: This research was supported by National Natural Science Foundation of China (Nos. 51175084, 51575111, and 51605093). Conﬂicts of Interest: The authors declare no conﬂict of interest.

References

1. Kazuto, T.; Satoshi, H.; Toshiharu, M.; Kenji, I.; Kazumi, M. Piezoelectric properties of vinylidene fluoride oligomer for use in medical tactile sensor applications. Sens. Actuators A Phys. 2008, 144, 90–96. [CrossRef] 2. Sandra, C.; Hagen, L.; Kerstin, D.; Ben, R.; Filomena, S. Using a humanoid robot to elicit body awareness and appropriate physical interaction in children with autism. Int. J. Soc. Robot. 2015, 7, 265–278. [CrossRef] 3. Ting, Z.; Li, J.; Hong, L. Design and functional evaluation of a dexterous myoelectric hand prosthesis with biomimetic tactile sensor. IEEE Trans. Neural Syst. Rehabil. Eng. 2018, 26, 1391–1399. [CrossRef] 4. Chortos, A.; Liu, J.; Bao, Z. Pursuing prosthetic electronic skin. Nat. Mater. 2016, 15, 937–950. [CrossRef] [PubMed] 5. Cheng, C.; Sun, X.; Xue, N.; Li, T.; Liu, C. Recent progress in technologies for tactile sensors. Sensors 2018, 18, 948. [CrossRef] 6. Hammock, M.L.; Chortos, A.; Tee, B.C.-K.; Tok, J.B.-H.; Bao, Z. 25th anniversary article: The evolution of electronic skin (E-Skin): A brief history, design considerations, and recent progress. Adv. Mater. 2013, 25, 5997–6038. [CrossRef][PubMed] 7. Morteza, V.; Erik, D.; Jae, W. Force and slip detection with direct-write compliant tactile sensor using multi-walled carbon nanotube/polymer composites. Sens. Actuators A Phys. 2013, 195, 90–97. [CrossRef] 8. Mannsfeld, S.C.B.; Tee, B.C.-K.; Stoltenberg, R.M.; Chen, C.V.H.; Barman, S.; Muir, B.V.O.; Sokolov, A.N.; Reese, C.; Bao, Z. Highly sensitive flexible pressure sensors with microstructured rubber dielectric layers. Nat. Mater. 2010, 9, 859–864. [CrossRef] 9. Song, A.; Han, Y.; Hu, H.; Li, J. A novel texture sensor for fabric texture measurement and classification. IEEE Trans. Instrum. Meas. 2014, 63, 1739–1747. [CrossRef] 10. Pedro, R.; Mohammed, A.; Ahmed, A.; Jurgen, K.; Fernando, F.; Susana, C.; Alexandre, B.; Alexander, S.; Jose, S.; Lorenzo, J. Bio-inspired ciliary force sensor for robotic platforms. IEEE Robot. Autom. Lett. 2017, 2, 971–976. [CrossRef] 11. Pinyo, P.; Liu, H.; Lakmal, D.; Prokar, D.; Kaspar, A. Miniature 3-axis distal force sensor for minimally invasive surgical palpation. IEEE/ASME Trans. Mechatron. 2012, 17, 646–656. [CrossRef] 12. Takao, S.; Tsuyoshi, S.; Shingo, I.; Yusaku, K.; Hiroshi, K.; Takayasu, S. A large-area, flexible pressure sensor matrix with organic field-effect transistors for artificial skin applications. Proc. Natl. Acad. Sci. USA 2004, 101, 9966–9970. [CrossRef] 13. Ahmed, A.; Jurgen, K. Magnetic nanocomposite cilia tactile sensor. Adv. Mater. 2016, 27, 7888–7892. [CrossRef] 14. Dana, D.; Taylor, H.; Rolf, P.; Allison, M. Artificial tactile sensing of position and slip speed by exploiting geometrical features. IEEE/ASME Trans. Mechatron. 2015, 20, 263–274. [CrossRef] 15. Visentin, F.; Paolo, F.; Kenji, S. A deformable smart skin for continuous sensing based on electrical impedance tomography. Sensors 2016, 16, 1928. [CrossRef][PubMed] 16. Pan, Z.; Cui, H.; Zhu, Z. Flexible full-body tactile sensor of low cost and minimal output connections for service robot. Ind. Robot. 2005, 32, 485–491. [CrossRef] 17. Wu, H.; Chen, J.; Su, Y.; Li, Z.; Ye, J. New tactile sensor for position detection based on distributed planar electric field. Sens. Actuators A Phys. 2016, 242, 146–161. [CrossRef] 18. David, S.; David, R.; Mari, V. Improved image reconstruction for an EIT-Based sensitive skin with multiple internal electrodes. IEEE Trans. Robot. 2011, 27, 425–435. [CrossRef] 19. Yang, Z.; Gierad, L.; Chris, H. Electrick: Low-cost touch sensing using electric field tomography. In Proceedings of the CHI Conference on Human Factors in Computing Systems, Denver, CO, USA, 6–11 May 2017; pp. 1–14. Sensors 2019, 19, 27 17 of 17

20. Markus, F.; Norbert, E.; Erik, S. Tactile sensing: A key technology for safe physical human robot interaction. In Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, Lausanne, Switzerland, 6–9 March 2011; pp. 139–140. [CrossRef] 21. Wu, H.; Su, Y.; Shi, J.; Li, Z.; Ye, J. A new robot skin for force and positon detection. Ind. Robot Int. J. 2014, 41, 534–542. [CrossRef] 22. Qin, L.; Yi, Z.; Zhang, Y. Enhanced surface roughness discrimination with optimized features from bio-inspired tactile sensor. Sens. Actuators A Phys. 2017, 264, 133–140. [CrossRef] 23. Liu, H.; Juan, G.; Song, X.; Lakmal, S.; Kaspar, A. Tactile image based contact shape recognition using neural network. In Proceedings of the IEEE International Conference on Multisensor Fusion and Intergration for Intelligent System, Hamburg, Germany, 13–15 September 2012; pp. 138–143. [CrossRef] 24. Wei, X.; Hu, G.; Wang, Y.; Pan, H.; Wang, F.; Ge, Y.; Feng, S. Multi-physical simulation and decoupling of a ﬂexible resistance-type three-dimensional force sensor. In Proceedings of the IEEE International Conference on Electronic Measurement & Instruments, Beijing, China, 16–18 July 2015; pp. 1462–1466. 25. Zhuang, X.; Sun, X.; Pan, H.; Wang, Y.; Ge, Y.; Feng, S. Radical basis function neural network for a novel tactile sensor design. In Proceedings of the IEEE International Conference on Information and Automation, Shenyang, China, 6–8 June 2012; pp. 926–931. 26. Ken, F.; Aaron, R.; Angelika, P.; Chris, E. Human-inspired Neurorobotic system for classifying surface textures by touch. IEEE Robot. Autom. Lett. 2016, 1, 516–523. [CrossRef] 27. Anany, D.; Anand, R.; Aniketh, R.; Kunal, P.; Selim, O.; Cagdas, D. Design, modeling, and validation of a soft magnetic 3-D force sensor. IEEE Sens. J. 2018, 18, 3852–3863. [CrossRef]

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).