- About this Journal ·
- Abstracting and Indexing ·
- Advance Access ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Journal of Sensors
Volume 2013 (2013), Article ID 624981, 5 pages
A Tent Map Based Conversion Circuit for Robot Tactile Sensor
1NARI Technology Development Limited Company, Nanjing 210061, China
2Experiment & Verification Center, State Grid Electric Power Research Institute, Nanjing 210061, China
Received 10 July 2013; Accepted 25 July 2013
Academic Editor: Aiguo Song
Copyright © 2013 Jianxin Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Force and tactile sensors are basic elements for robot perception and control, which call for large range and high-accuracy amplifier. In this paper, a novel conversion circuit for array tactile sensor is proposed by using nonlinear tent map phenomenon, which is characterized by sensitivity to small signal and nonlinear amplifying function. The tent map based conversion circuits can simultaneously realize amplifying and converting functions. The proposed circuit is not only simple but also easy to integrate and produce. It is very suited for multipath signal parallel sampling and converting of large array tactile sensor.
In recent decades, with the rapidly development of robot technology, robot sensors have received much attention as a sensing element for robot. Multiaxis force sensors and array tactile sensors, usually called haptic sensors, especially, have become the major research content in the robot sensor research areas [1, 2]. People hope that robot haptic sensor can be like human perception organs which have high measurement accuracy, with similar hand force and tactile organ of integration, miniaturization, and flexibility characteristics. For instance, Song developed a small four-degree-of-freedom wrist force sensor with high precision, which consists of small cross-elastic beam, compliant beams, and the base of the elastic body. It is a kind of self-decoupling force sensor in mechanical structure . Beyeler et al. designed a six-axis MEMS force sensor with a movable body suspended by flexures which allow deflections and rotations along the -, -, and -axes. And the orientation of this movable body is sensed by seven capacitors based on transverse sensing, resulting in a high sensitivity . Ma et al. proposed a novel nonlinear static decoupling algorithm based on the establishment of a coupling error model for 3-axis force sensor in order to avoid overfitting and minimize the negative effect of random noises in calibration data, which can obtain high precise measurement results of 3-axis force for robot force control . Although robot array tactile sensor can be regarded as a multipoint integrated force sensor, due to flexible and miniaturization requirements of tactile sensor which are high, the measurement principle is more complex than the multiaxis force sensor [6, 7]. Song et al. proposed a novel design of a haptic texture sensor by using PVDF film to fabricate a high-accuracy, high-speed-response texture sensor . Lee and Won developed a novel tactile imaging sensor by using a multilayer polydimethylsiloxane optical waveguide as the sensing probe, which is capable of measuring the elasticity of the touched object with high precision . Based on semiconductor technique, piezoresistive, capacitive, piezoelectric, and other types of robot, array tactile sensors are developing rapidly. Array tactile sensors have a corresponding increase in array size and resolution [10, 11]. Nonetheless, because of measurement mechanism limit, output signal of sensing element of most high-resolution tactile sensors is relatively weak. In addition, as the expansion of tactile array, it hopes that tactile image signal has a fast conversion rate. Sharing the same conversion in small scale tactile sensor cannot meet the real-time requirements of signal acquisition for the large scale array tactile sensor.
In recent years, some of the unique properties of nonlinear systems have much in measurement area for their unique character, such as the chaotic system sensitivity to small signal , nonlinear mapping , nonlinear information storage , and resonance stochastic . In this paper, a tent map is sensitive to small signal circuit and nonlinear transform of unique properties. A novel conversion circuit for robot tactile sensor array is proposed to achieve parallel sampling of multichannel tactile signals and conversion with high cost performance, which has a conditioning amplification and conversion function integration advantages in combination, simple circuit, and easy integration to realize.
2. Signal Acquisition System for Robot Tactile Sensor
Typical signal acquisition circuit for robot tactile sensor is shown in Figure 1, including timing control circuit, line scan circuit, read circuit, and interface circuit. The whole signal acquisition process was controlled and coordinated by timing control circuit. According to the arrangements of the timing control circuit, line scan circuit is ordered in clock cycles to send the periodic excitation signal to -line array sensitive element, while the read circuit is ordered in clock cycles to read the output signal of column in parallel. Then through interface circuit which consists of signal conditioning and conversion, tactile signals were transferred to the computer for processing and target recognition.
Conventional robot array tactile sensor, because of the small array size (about ), often uses an conversion in order to complete the analog-digital conversion of output signal of sensitive elements.
3. Small Signal Nonlinear Amplifier and Conversion Based on Tent Map
Tent map was a typical one-dimensional chaotic system , which was described as where, .
This map consisted of two steps: the first step was to uniformly elongate the interval to its doubled range; the second step was to fold the elongated interval into the original interval . These iterative operations would cause the separation of adjacent points index, eventually to achieve the state of chaos.
Tent map on the initial value (the system input signal) amplification was different from the linear amplification method. Linear amplification multiples were a constant and also limited by system operating range. Tent map system in chaotic state amplified the signal doubly and folded the doubled range symmetrically in each iteration, so that the initial small signal could eventually be greatly amplified without beyond range of system operating after several iterations.
The initial value , which was corresponding to the input signal of tent map system , could be described as a binary fraction
In order to obtain the relationship of the iterative output and the initial signal of discrete tent map, here, this paper would introduce the nonlinear relationship of Bernoulli shift; its kinetic equation was
In each iteration, Bernoulli shift left shifted the binary fraction one place
For Bernoulli shift, was defined as the th iteration output; there , and , , was a binary sequence. For tent map, if we define, then the corresponding relationship between , , and , , was as follows:(1)when, that is, , then, and ;(2)when , that is, , then , and ;(3)when , that is, , then , and ;(4)when , that is, , then , and .Therefore, , , was a Gray-code sequence of , :
According to the above formula and initial time , we could obtain
So we designed a tent map iteration output Gray-code sequence , , into a binary sequence of Bernoulli map, , and then calculated the initial value through binary fraction sequence as follows: where , , was the needed digital value. Here, tent map completed signal amplification and conversion function.
4. Implementation on the Circuit of Tent Map for Conversion
circuit basic element was switched capacitor integral circuit shown in Figure 2, where and were analog switches and and were capacitors. The clocks , were in reverse phase with same period . During the former half period of , the was on and was off; thus the was charged by input voltage . During the latter half period of , was off and was on, so that the was charged by . The output voltage of this circuit in a period was
The tent map circuit consisted of the above circuit as shown in Figure 3. Parts I and III implemented the function of, and parts II and III implemented the function of . Part IV was a circuit for holding and delay.
Figure 4 shows the control logic for conversion. First, start signal got high, and switch connected the input signal with the part I and part II. After delay of , trigger produces a switch instruction; if , then was switched on; if , then was switched on. At time , the control signal switches were high, so that the charge of or was transferred to , due to , so that the input voltage was amplified doubly. In the meantime, was also charged. At the next time 0, the switches were switched off and e switches were switched on, so that the charge of was transferred to and results in . At the same time, was disconnected with input signal , so that an iterative feedback loop exists in the conversion, and , were charged by the same feedback voltage to realizeand. This circle continuously iterated times.
Hence the binary bits output of trigger was the Gray-code sequence of conversion
The above sequence, the initial condition into (6), and Bernoulli binary sequence () could be obtained.
5. Array Tactile Sensor Signal Acquisition System Based on Tent Map of Circuit
The schematic diagram of signal amplification of array tactile sensor analog and conversion based on tent map circuit is shown in Figure 5. According to the arrangements of the timing control circuit, line scan circuit sent the periodic excitation signal to -line array sensitive element, while the read circuit read the output signal of column in parallel. The -column signal was generated by simultaneously signaling nonlinear amplification and analog-digital conversion of of converters based on tent map. The resulting Gray-code sequence was sent directly to the computer which will complete the conversion of Gray code to binary code. Then, under the control of timing logic, the read -column signal on the next line, and the completed amplification and conversion, after obtaining an tactile image signal by the computer, we could process tactile signals.
We could carry out the amplification and conversion experiments sensitive element signal of microarray tactile sensor based on the above circuit. The results of the conversion for eight sensitive elements in the 8th line were shown in Table 1, which indicates that circuits based on the tent map could effectively achieve the amplification and conversion of a small signal.
Figure 6 is the output of a micro array tactile sensor manufactured by us based on the tent map circuit when a very light hexagon aluminum flake was put on it. The measurement range of each tactile sensing unit was from 0.01 N to 10 N, which shows that the proposed tent map based conversion circuits had the advantages of large amplification range and high resolution. By comparison with the conventional high precision array tactile sensor , our method is more cost effective and easier to realize.
This paper presents a novel conversion circuit for robot array tactile sensor of unique properties. The circuit makes use of unique advantage for tent map sensitive to small signal circuit and nonlinear transform and has conditioning amplification and conversion function integration advantages, simple circuit, and easy integration to realize. This method can achieve the parallel sampling of multichannel tactile signal and converter, which can meet the real-time requirements of signal acquisition for the large scale array tactile sensor. This experiment gives the effectiveness of this method.
This work was supported by the Science and Technology Project of National Energy Administration (no. NY20110702-1) and the Technology Project of State Grid Corporation of China in 2013-2014 (project name is Study of Testing and Detection Technology for Electric Vehicle Charging and Battery Swap Infrastructure).
- H. Yousef, M. Boukallel, and K. Althoefer, “Tactile sensing for dexterous in-hand manipulation in robotics—a review,” Sensors and Actuators A, vol. 167, no. 2, pp. 171–187, 2011.
- J. Ma and A. Song, “Fast estimation of strains for cross-beams six-axis force/torque sensors by mechanical modeling,” Sensors, vol. 13, no. 5, pp. 6669–6686, 2013.
- A. Song, J. Wu, G. Qin, and W. Huang, “A novel self-decoupled four degree-of-freedom wrist force/torque sensor,” Measuremen, vol. 40, no. 9-10, pp. 883–891, 2007.
- F. Beyeler, S. Muntwyler, and B. J. Nelson, “A six-axis MEMS force-torque sensor with micro-Newton and nano-Newtonmeter resolution,” Journal of Microelectromechanical Systems, vol. 18, no. 2, pp. 433–441, 2009.
- J. Ma, A. Song, and J. Xiao, “A robust static decoupling algorithm for 3-axis force sensors based on coupling error model and ε-SVR,” Sensors, vol. 12, no. 11, pp. 14537–14555, 2012.
- J.-H. Kim, J.-I. Lee, H.-J. Lee, Y.-K. Park, M.-S. Kim, and D.-I. Kang, “Design of flexible tactile sensor based on three-component force and its,” in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 2578–2581, April 2005.
- M. H. Lee and H. R. Nicholls, “Tactile sensing for mechatronics—a state of the art survey,” Mechatronics, vol. 9, no. 1, pp. 1–31, 1999.
- A. Song, Y. Han, H. Hu, L. Tian, and J. Wu, “Active perception-based haptic texture sensor,” Sensors and Materials, vol. 25, no. 1, pp. 1–15, 2013.
- J.-H. Lee and C.-H. Won, “High-resolution tactile imaging sensor using total internal reflection and nonrigid pattern matching algorithm,” IEEE Sensors Journal, vol. 11, no. 9, pp. 2084–2093, 2011.
- J. Dargahi and S. Najarian, “Advances in tactile sensors design/manufacturing and its impact on robotics applications—a review,” Industrial Robot, vol. 32, no. 3, pp. 268–281, 2005.
- S. Saga, H. Kajimoto, and S. Tachi, “High-resolution tactile sensor using the deformation of a reflection image,” Sensor Review, vol. 27, no. 1, pp. 35–42, 2007.
- N. Singh and A. Sinha, “Chaos-based secure communication system using logistic map,” Optics and Lasers in Engineering, vol. 48, no. 3, pp. 398–404, 2010.
- L. Zhang, A. Song, and J. He, “Stochastic resonance of a subdiffusive bistable system driven by Lévy noise based on the subordination process,” Journal of Physics A, vol. 42, no. 47, Article ID 475003, 2009.
- A. Song, J. Duan, J. Wu, and H. Li, “Design 2D nonlinear system for information storage,” Chaos, Solitons and Fractals, vol. 41, no. 1, pp. 157–163, 2009.
- D. Rousseau, J. R. Varela, and F. Chapeau-Blondeau, “Stochastic resonance for nonlinear sensors with saturation,” Physical Review E, vol. 67, no. 2, Article ID 021102, 6 pages, 2003.
- X. Yi, “Hash function based on chaotic tent maps,” IEEE Transactions on Circuits and Systems II, vol. 52, no. 6, pp. 354–357, 2005.
- H.-K. Kim, S. Lee, and K.-S. Yun, “Capacitive tactile sensor array for touch screen application,” Sensors and Actuators A, vol. 165, no. 1, pp. 2–7, 2011.