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Size recognition and adaptive grasping using an integration of actuating and sensing soft pneumatic gripper
Robotics and Autonomous Systems 104 (2018) 14–24
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Robotics and Autonomous Systems journal homepage: www.elsevier.com/locate/robot
Size recognition and adaptive grasping using an integration of actuating and sensing soft pneumatic gripper Yang Chen, Shaofei Guo, Cunfeng Li, Hui Yang, Lina Hao * School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China
highlights • A home-made soft pneumatic gripper can do size recognition task for different material objects, and adaptively grasp the objects stably for a long time even existing disturbance based on the information of a bending sensor and an air pressure sensor.
• An application of the gripper affixed on home-made palletizing robot realizes sorting of apples and lemons based on size recognition and adaptive grasping abilities.
article
info
Article history: Received 7 August 2017 Received in revised form 4 February 2018 Accepted 28 February 2018
Keywords: Soft pneumatic gripper Bending sensor Air pressure sensor Size recognition Adaptive grasping
a b s t r a c t Due to less appropriate sensors for representing the pose of soft robotic hand, the soft robotic hand usually works in an open loop control system. In this paper, only two simple sensors were used in a soft pneumatic gripper (SPG) control system to make it possess innervated-like ability. The control system was conducted with only two sensors, air pressure sensor and bending sensor where the air pressure sensor is able to detect grasping force and the bending sensor is able to detect grasping position The bending sensor was characterized by testing on pressure vs. bending and bending vs. size relationships. The principle of circular-shaped object size recognition was presented in Algorithm 1. Multi-group tests had been done on grasping different size and material objects, and test results had a strong effect on size recognition ability of the SPG. Finally, adaptive grasping control process was presented in Algorithm 2. The tests on grasping the same size objects had been done. The test results demonstrate that the SPG is able to maintain stable grasping state for a long time based on size recognition ability no matter what material the object is. At the same time, it had no effect on the grasping state at the time of existing disturbance, which had been verified by four different disturbance tests. The SPG will benefit the development of soft robotics field in picking and sorting fruits and vegetables, and an application of the SPG affixed on the home-made palletizing robot was described and conducted at last. © 2018 Elsevier B.V. All rights reserved.
1. Introduction Soft robots have been gained wide attention and researched in recent years [1–5]. Comparing with conventional rigid-bodied robots, soft robots are inherently compliant and made of soft or extensible materials, so they always can exhibit large deformation in normal operation. In addition, soft robots generally have the properties of high dexterity, safety, conformability to obstacles, manipulating variable size objects and working in structured and unstructured environment. Due to advantages of soft robots, some researchers focus on soft robotic hand instead of rigid robotic hand. The soft robotic hands can be driven by various actuators, such as motor [6], cable [7], shape memory alloy (SMA) [8,9], hydraulic author. * Corresponding E-mail address: [email protected] (L. Hao). https://doi.org/10.1016/j.robot.2018.02.020 0921-8890/© 2018 Elsevier B.V. All rights reserved.
[10] and pneumatic [11–17] actuators. However, only five of them have integration of sensors or actuators on bodies [8,14–17], and two of them separate from sensors or actuators [7,13], making the system bulky and incompact. The key challenge for fabricating a soft robotic hand is to exploit of controllable soft bodies using suitable sensors to describe its behavior. The conventional sensors for detecting grasping force and position may not be available on soft robot bodies, thus new soft material sensors are required to represent mechanical characteristic (such as deformation, compliance and extensibility) of soft robotic hand. The stretchable and flexible sensors which can be integrated with the soft bodies are fabricated and introduced [18,19]. Capacitive sensors [20], resistive sensors [21], magnetic sensors [22] and optoelectronic sensors [17] are the main four category sensors which can be mounted on soft robotic hand to measure motion information. Performance of the four sensors which are suitable for soft robotic hand rehabilitation
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Fig. 1. The physical view of molds: (a) chamber mold, (b) cover mold, (c) base mold.
Fig. 2. The physical view of SPG.
(We call this adaptive grasping), and this grasping ability make it available as the end effector in application of a robotic system. All above form the most exciting things of our work. In this paper, we only use the commercial bending sensor [23] (Flexpoint Sensor Systems Inc., UT, USA) which is two inches in length, covered in polyimide laminate, bi-directional type, solder tab connector and air pressure sensor (40PC150G2A, Honeywell Inc., NJ, USA) for the home-made soft pneumatic gripper (SPG) system, which can make SPG possess size recognition ability for circular-shaped object in the whole paper. The SPG can also achieve adaptive grasping for different size and material objects. The proposed method which makes SPG with intelligent abilities gives us another way to analyze and control the soft robotic hand. This paper is organized as follows. Section 2 introduces the fabrication technology of SPG. Section 3 tells us the control system of SPG, and the function of each component is depicted briefly. Section 4 gives the test results to verify the intelligent ability of SPG, and discusses the phenomena and results in the tests. Section 5 depicts an application example in picking and sorting apples and lemons. Section 6 summarizes the main contribution of this paper, and makes a concise conclusion. 2. Fabrication of soft pneumatic gripper
Fig. 3. The working principle and structure of one finger: (a) uninflated state, (b) inflated state.
devices were evaluated [23], and the test results indicate that a commercial bending sensor has the best overall performance with high sensitivity, short settling time, small normalized signal variation, low relaxation and hysteresis. According to the literature review, an appropriate sensor should be able to adapt to the deformation of soft robotic hand, so that the measured value from the sensor can well understand the true state of soft robotic hand in operation with innervated ability like human hand. Although the hand with integrated curvature and pressure sensing abilities can complete object recognition and grasping detection in Refs. [15] and [16], the proposed in the two papers can grasp objects with different forces being destroyed for a long time
In recent years, researchers have paid more attention on soft pneumatic hand. A good fabrication technology will benefit the working performance of soft pneumatic hand. Related works have been concluded below. A compliant, underactuated, and dexterous anthropomorphic robotic hand was presented based on soft robotic technology [11]. The hand had five fingers and a palm. The inelastic fabric was embedded into the fingers to constrain radial motion. An integrated soft pneumatic gripper with three fingers was proposed and fabricated [13]. The hand was able to adapt the finger shape to the target object and apply a certain amount of force. An air-powered prosthetic hand was built on a regular FDM 3D printer [24]. The detailed fabrication technology and required materials were introduced as open source data. This hand was developed with the purpose of being available on the medical market. All above three soft pneumatic hands are fabricated by shape deposition manufacturing method which is one of rapid prototyping method. Owing to 3D printing technology, some researchers have already tried fabricating the soft pneumatic hand by 3D printer [12,25]. The hands fabricated by 3D printer have high intensity with high tolerance inflation pressure, so that they can generate higher grasping force on objects. The novel soft pneumatic actuator-pack architecture was presented and aimed to increase the force output and bandwidth requirements for many tasks in development of the soft robotics field [26]. To summarize, soft hand actuated by pneumatic power supply has advantages of large deformation, durable, easy fabrication, low
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Fig. 4. Schematic diagram of hardware for SPG.
(1) The appropriate mold can form a well working SPG, so it is a key challenge for mold design. The molds are fabricated by 3D printer with PLA, which are shown in Fig. 1 (chamber mold, cover mold and base mold). (2) Put the segmentation blocks into the chamber mold forming different space chambers in SPG. (3) Pour the mixed silicone rubber into chamber mold. A cover mold is used to make silicone rubber full fill the chamber mold. At the same time, a bolt is fixed on the top of chamber mold forming an air hole. (4) After about 40 min, one portion of SPG is shaped. Pour the mixed silicone rubber onto base mold. At the same time a bending sensor is put on one surface of base mold, and then a fabric cloth is put onto all the surfaces of base mold. Lastly cover the ready-made portion of SPG onto the fabric cloth, and seal the joint part with the mixed silicone rubber. (5) After about 40 min, the SPG integrated with the bending sensor can be obtained. The physical view of SPG is shown in Fig. 2. Fig. 3 illustrates the working principle and structure of one finger. Fig. 3a shows the details of finger inner chamber at uninflated state. When inflated, the extensible layer (silicone rubber) extends as shown in Fig. 3b, hence the bending motion is generated and constrained by the fabric cloth which is embedded in silicone rubber forming inextensible layer. The fabric cloth relieves the silicone rubber of non-functional strains, making the inflation lead to bending rather than to radial expansion. 3. Hardware setup
Fig. 5. The Wheatstone half bridge circuit for the bending sensor.
cost. The SPG is made of silicone rubber material called Dragon Skin FX-Pro (Smooth-on, Inc., PA, USA). It is fabricated by shape deposition manufacturing method with three fingers to grasp objects. All the fingers integrated in the whole gripper can simply the pneumatic circuit, make the control system easy and realize synchronous grasping motion. Through trial and error, we concluded a relatively mature fabrication technology which can insure SPG durable for a long time. The procedure of fabrication process of SPG is described as follows.
The SPG can work effectively with a feasible control system. The schematic diagram of hardware for SPG is shown in Fig. 4. Each component and its functions of this hardware are introduced briefly. Arduino Mega 2560 is selected as the microcontroller board for SPG. It has 54 digital input/output pins (of which 15 can be used as PWM outputs), 16 analog inputs, 4 UARTs (hardware serial ports), and abundant I/O resources are in favor of control system to extend other applications. AD 620 chip is a type of low cost and low power instrumentation amplifier, and we use it to make a Wheatstone half bridge circuit which can amplify and adjust the output value of bending sensor. L298N is a type of dual full-bridge driver which can drive inductive loads such as relays, solenoids, DC and stepping motors. We use it to drive a mini pump in this paper. Both AD 620 amplification circuit and L298N driving circuit need to be supplied by DC power. The air pressure sensor with three pins (power, ground, and signal) can detect chamber pressure of SPG.
Fig. 6. Characterization of bending sensor: (a) schematic diagram in one working cycle, (b) ten group test results in different maximum inflation pressures.
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Fig. 7. Pressure vs. bending in effective working pressure range. Fig. 9. Bending vs. size fitting curve.
The bending sensor whose resistance changes as it is bent, and it has been tested to show with overall performance in sensitivity, relaxation, repeatability and hysteresis [23], and many applications of bending sensor have been studied [27,28]. Its sensing ability has been evaluated for soft robotic hand rehabilitation, so in this work, we mount it on SPG to measure the grasping position. All program algorithms are executed in MATLAB/Simulink environment, so it is easy for us to debug the control system to realize desired functions. The signal amplification circuit of the bending sensor is a key portion in hardware setup. The output signal of bending sensor is used to understand the behavior of SPG, so we expect to acquire small variation of output signal in grasping operation. In this paper, we select AD 620 chip to make the Wheatstone half bridge circuit in order to measure the position of SPG. The conducted signal amplification circuit is shown in Fig. 5. A 12 V lithium battery is used as the power source. We employ a commercial adjusting voltage module (4–38 V input voltage, 1.25–36 V output voltage) to obtain a stable 5 V output voltage (+VE /GND in Fig. 5), and then a DC/DC converter is used to acquire a dual output voltage of ±12 V (±VS /GND in Fig. 5). These constitute the power supply system. The output voltage is amplified by gain resistor RG (unit: k), and the gain value is determined by Eq. (1). We select a variable resistor (0– 200 k) to adjust the amplification factor. A constant resistor R1 , a variable resistor R2 and two variable resistors of the bending sensor comprise the Wheatstone electric bridge. The function of a variable resistor R2 is to balance the difference of two variable resistors of the bending sensor, and we can set the input voltage to AD620 at a
Fig. 10. Size recognition curve of PLA ball with diameter 60 mm. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
required value. The two variable resistors of the bending sensor are measured at between 5 and 10 k range, so bridge-arm resistor R1 is selected as 10 k and bridge-arm resistor R2 is selected as 0–20 k, which can make electric bridge balance. G=
49.4 RG
+ 1.
Fig. 8. Balls with different diameters and materials.
(1)
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Fig. 11. Size recognition test results: (a) ten group results of PLA ball with diameter 60 mm, (b) results of PLA ball with diameters 50 mm, 60 mm, 70 mm, 80 mm and 90 mm, (c) results of PLA ball, silica gel ball and rubber ball with diameter 60 mm.
4. Test results First, we clarify the meaning of some symbols to make it readable. Variable b represents the bending angle of SPG, which is acquired from Arduino Mega 2560, and we cannot know the certain bending angle according to the output voltage value. Due to repeatability and stability of bending sensor, the bending amplitude and current inflation pressure correspond to a certain value from Arduino Mega 2560, and this work will be verified in Section 4.1. Variable s represents the diameter of spherical object (hereinafter referred to as ‘‘size’’), and we select different size of objects to evaluate grasping and size recognition abilities of SPG. There is a specific transformational relationship between output voltage from air pressure sensor (unit: V) and chamber pressure (unit: bar) based on data sheet. We use variable p to represent the output voltage from air pressure sensor. 4.1. Characterization of bending sensor The working process of bending sensor during SPG grasping and releasing motions is tested in Fig. 6a. The pressure value is increasing obviously, while the bending value is not in A–B segment. The inner chamber of SPG needs a certain pressure to make it bend, and then the SPG can grasp object effectively. B–C segment represents the grasping motion within the normal working range of SPG. C– D segment represents the releasing process of SPG. D–A segment
represents the back relaxation characteristic of the bending sensor. We define the output value of air pressure sensor without bending as the initial state (A point in Fig. 6a). The initial pressure value is not zero, because of the existing barometric pressure. Due to the analog input of Arduino Mega 2560 is from 0 to 5 V, the initial bending value is set as one volt artificially by adjusting signal amplifier circuit, and the output value can be detected in initial state at any time. According to trial and error, ten groups of tests with different maximum inflation pressures are conducted in empty load condition as shown in Fig. 6b, and the effective working pressure is determined from 170 kPa to 184 kPa. It is clear that the pressure vs. bending has a linear relation characteristic in its effective working pressure range. 4.1.1. Pressure vs. bending The maximum inflation pressures ranging from 175 kPa to 184 kPa (in Fig. 6b) are used to fit linear relation curve in MATLAB software. Each group data can obtain a linear relationship, and an average of above five linear relationships to acquire the final pressure vs. bending curve which is shown in Fig. 7 as Eq. (2). The correlation coefficients of five fitted data are 0.9953, 0.9954, 0.9956, 0.9890 and 0.9844 respectively, so that the pressure vs. bending relationship in effective working pressure range is available. p = 0.0249b + 0.7146.
(2)
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Fig. 12. Grasping control results: (a) PLA ball, (b) silica gel ball, (c) rubber ball.
4.1.2. Bending vs. size Due to the repeatability and robustness characteristics of bending sensor, when the SPG reaches a grasping position, the bending sensor can output a stable value, so that we suppose this gripper can do some intelligent things, such as circular-shaped object size recognition. First, some balls are fabricated by 3D printer with different diameters in PLA, and also bought online with silica gel and rubber materials shown in Fig. 8. There are six PLA balls with diameters 50 mm, 60 mm, 70 mm, 80 mm, 90 mm and 100 mm, silica gel and rubber ball with diameter 60 mm as test objects. The PLA balls with six different diameters are selected as grasped object to fit curve. Because the size value is much bigger than the bending value, the size data are used by reducing ten times. Finally, Eq. (3) is obtained to represent the bending vs. size relationship, and its curve is shown in Fig. 9. b = 0.0169s3 − 0.3641s2 + 2.2258s − 1.5514.
(3)
4.2. Size recognition To demonstrate size recognition ability, we use the SPG to grasp objects with different sizes and materials. We measure the object
size by positioning the tip of gripper at initial state. A suitable inflation pressure is provided in effective working pressure range to insure grasping stability, and grasping state should maintain for a period of time, and then deflate pressure to make gripper release. Lastly, the grasped object size is displayed on the test system interface. The size recognition process is described in Algorithm 1. When human hand grasps an object, it forms enveloping grasping for the shape and dimension of object at first, and then power grasps. It is noteworthy that the hand pose is invariable in power grasping process, that is to say the object size determines the hand operation pose. Algorithm 1 imitates human hand grasping operation process to realize size recognition, and the key of size recognition is step 7 in Algorithm 1. A typical size recognition curve is shown in Fig. 10. The blue line represents the real reading from the bending sensor, and the red line represents the predicted value calculated by inverse of Eq. (2). By comparing the real bending value and the predicted bending value, when the predicted bending value exceeds the threshold value than the real bending value at the first time, we judge that the SPG contacts the object and begins power grasping operation, and the bending value at this time determines the object size. We set the threshold value as 0.1 to eliminate the influence of sensor noise.
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Fig. 13. Grasping control results with sine disturbance force: (a) PLA ball, (b) silica gel ball, (c) rubber ball.
Fig. 14. Grasping control results for PLA ball with disturbance at time of 10 s, 30 s and 50 s: (a) numerical simulate pulse pressure disturbance (b) pulse pressure disturbance manually.
Fig. 11a shows ten group test results of PLA ball with diameter 60 mm, and Fig. 11b shows test results of PLA ball with diameters 50 mm, 60 mm, 70 mm, 80 mm and 90 mm. The results demonstrate that the SPG can identify different size of objects accurately, and the prediction error is less than 5 mm in general. In order to verify the effectiveness of the size recognition algorithm on the
other materials, experiments have been conducted and the results shown in Fig. 11c are also satisfying. We can conclude that circularshaped object size recognition ability of SPG has a high confidence level. A correction term can be added to Algorithm 1 to make the predicted result more precise in the future. It is important to note that all these tests should be done in effective working pressure
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Fig. 15. Grasping performance: (a) PLA ball, (b) silica gel ball, (c) rubber ball, (d) PLA ball with sine disturbance.
Fig. 16. Grasping control result for PLA ball with pulse pressure disturbance manually at time of 10 s, 30 s and 50 s in improved control system.
range, and the grasping size range of SPG is about from 50 mm to 90 mm. 4.3. Adaptive grasping Taking advantage of air pressure sensor detecting grasping force and bending sensor detecting grasping position, the SPG can
accomplish adaptive grasping operation. The adaptive grasping control process is described in Algorithm 2. In Algorithm 2, when the bending sensor detects the gripper reaching desired grasping position, pressure control loop is triggered to execute, and we set desired pressure bigger than the predicted pressure by Eq. (2) to insure grasping object with different weights successfully. We just need input the grasped circularshaped object size to the control system, and the SPG can complete a stable grasping operation task. Grasping control results of PLA ball, silica gel ball and rubber ball with diameter 60 mm are shown in Fig. 12. From the test processes and results, grasping state is safe and reliable, and will hold on desired pressure under safe working pressure for a long time. In order to verify the robustness of system, an external disturbance force is applied on SPG at stable grasping period from 20 to 50 s. We use a signal generator (the output signal is set as 4 sin(10 ∗ pi ∗ t)) to determine the form of disturbance force, so that the sine disturbance force acts on the SPG in the adaptive grasping control tests. The disturbance force is generated by vibration exciter whose signal is magnified from power amplifier. The grasping control results are shown in Fig. 13. The effectiveness of the adaptive grasping control is evaluated by a mean error emean as expressed in Eq. (4), where pr represents the real measured pressure, pd represents the desired pressure, N represents the sampling number. Through experiments, the mean errors of adaptive grasping tests without any disturbance are 391 Pa, 276 Pa and 299 Pa respectively, and these with sine disturbance force are 322 Pa, 345 Pa and 368 Pa respectively. The tracking control results have little difference. Maybe this type disturbance cannot cause the undulation of pressure in chamber. Next, we do two group tests on adaptive grasping control for PLA ball with diameter 60 mm. Fig. 14a shows the control result with numerical simulate pulse pressure disturbance at time of 10 s, 30 s and
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Fig. 17. The performance of the system for picking and sorting apples and lemons task: (a) initial state, (b) grasping lemon, (c) grasping apple, (d) final state.
50 s, and Fig. 14b shows the control result with pulse pressure disturbance manually at time of 10 s, 30 s and 50 s. We can see both can arrive at desired pressure value rapidly after disturbance. Some grasping performance of tests is also shown in Fig. 15.
∑N emean =
i=1
|pr − pd | N
.
(4)
To improve the dynamic regulating performance when pulse pressure disturbance is applied, we replace the mini-pump with Mead valve (V1A02-AW1, Mead Fluid Dynamics Inc., IL, USA) in our control system. The control results with pulse pressure disturbance manually at time of 10 s, 30 s and 50 s are shown in Fig. 16.
By comparing with the mini-pump control system, the average regulating time shortens by 1.74 s (0.73 s and 2.47 s, respectively) when pulse air leakage occurs. In addition, the rising time in initial grasping manipulation shortens by 1.64 s (0.33 s and 1.97 s, respectively). The control efficiency effect improves greatly on resisting the pulse pressure disturbance in grasping manipulation. 5. Application Combined with the home-made 4-DOF palletizing robot [29], the SPG is affixed on the end joint of palletizing robot. Thus, the total system can accomplish sorting small, light and irregular shape
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objects. Next, the physical platform is used for picking and sorting apples and lemons task. The apples and lemons are put on tape transport system randomly at first. The position command is sent through the control system panel of palletizing robot manually. When the end joint of palletizing robot arrives at the desired position, the control system of SPG is triggered, and the SPG will complete size recognition. Due to different size of apples and lemons, the SPG can pick up them in specific basket sequentially. Then the palletizing robot returns to its initial state waiting for the next command. The performance of the system for picking up and sorting these two different size fruits (apples and lemons) task is shown in Fig. 17. 6. Conclusion The air pressure sensor and bending sensor proposed in our work are low cost and durable, as well as high repeatability and low relaxation. The sensors have the functions of sensing grasping force and position for soft robotic hand. With these common sensors, we believe that the proposed application method will benefit the field of soft robotics and make soft robotics more intelligent with versatile sensory capabilities. The main contribution of this paper is to integrate the bending sensor and the air pressure sensor on SPG to make it with innervated-like ability. The SPG has versatile sensory capabilities of circular-shaped object size recognition, power grasping and adaptive grasping for different size and material objects. All the grasping tests in this paper demonstrate excellent performance of SPG and its control system. The SPG affixed on the home-made palletizing robot is also verified in application of sorting different fruits effectively due to above intelligent capabilities. In the future, we hope to mount bending sensor on each finger of SPG to recognize an arbitrary object’s shape or size. Acknowledgments This work was sponsored by the National High Technology Research and Development Program of China (863 Program) under Grant No. 2015AA042302, the National Natural Science Foundation of China under Grant No. 61573093 and No. U1613205. The authors would like to thank Dr Jingwen Xu for her help with data processing in School of Science at Northeastern University, China. Yang Chen made all the circuit boards, designed the control system, carried out all the experiments, analyzed the data and wrote the paper. The soft pneumatic gripper was fabricated by Shaofei Guo. Cunfeng Li participated in conducting experiment and analyzing experimental results. Hui Yang provided aspect of pneumatic knowledge discussing on system design. Lina Hao inspired us to design a soft hand with intelligent characteristics such as abundant sensing ability, recognition capability, adaptive to diversity of object and reviewed this manuscript. References [1] D. Trivedi, C.D. Rahn, W.M. Kier, I.D. Walker, Soft robotics: Biological inspiration, state of the art, and future research, Appl. Bionics Biomech. 3 (2008) 99–117. [2] C. Laschi, B. Mazzolai, M. Cianchetti, Soft robotics: Technologies and systems pushing the boundaries of robot abilities, Sci. Robot. 1 (2016) eaah3690. [3] D. Rus, M.T. Tolley, Design, fabrication and control of soft robots, Nature 7553 (2015) 467–475. [4] S.G. Nurzaman, F. Iida, L. Margheri, C. Laschi, Soft robotics on the move: scientific networks, activities, and future challenges, Soft Robot. 2 (2014) 154– 158. [5] C. Majidi, Soft robotics: a perspective—current trends and prospects for the future, Soft Robot. 1 (2014) 5–11. [6] M. Tavakoli, A.T. de Almeida, Adaptive under-actuated anthropomorphic hand: Isr-softhand, in: Chicago, IL: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS. September 2014, pp. 1629–1634.
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Yang Chen received the B.S. degree in mechanical engineering and automation from China University of Mining and Technology, Xuzhou, China in 2012, M.S. degree in mechatronic engineering from Northeastern University, Shenyang, China in 2014. He is currently a Ph.D. candidate at Northeastern University, Shenyang, China. His research interests include driving, modeling and control of artificial muscles especially in IPMC and SMA, and soft robotic system. He is a student member of IEEE and International Society of Bionic Engineering.
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Y. Chen et al. / Robotics and Autonomous Systems 104 (2018) 14–24 Shaofei Guo received the B.S. degree in mechanical engineering from Changchun University of Science and Technology, Changchun, China in 2015. He is currently a master candidate at Northeastern University, Shenyang, China.
Hui Yang received the B.S. degree and M.S. degree in machinery design and manufacture from Liaoning Shihua University, Fushun, China in 2010 and 2013, respectively. He is currently a Ph.D. candidate at Northeastern University, Shenyang, China.
Cunfeng Li received the B.S. degree in mechanical engineering and automation from Hebei University, Baoding, China in 2015. He is currently a master candidate at Northeastern University, Shenyang, China.
Lina Hao received the B.S. degree in machinery design and manufacture from Shenyang Ligong University, Shenyang, China in 1989, M.S. degree in solid mechanics from Northeastern University, Shenyang, China in 1994 and Ph.D. degree in control theory and control engineering from Northeastern University, Shenyang, China in 2001. From Dec. 2005 to Dec. 2006, she did research on AFM as a visiting scholar in Michigan State University: Detroit, America. Currently, she is a professor in School of Mechanical Engineering and Automation in Northeastern University, China. Her research interests include robot system and intelligent control, intelligent structure and precision motion control system.
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Robotics and Autonomous Systems journal homepage: www.elsevier.com/locate/robot
Size recognition and adaptive grasping using an integration of actuating and sensing soft pneumatic gripper Yang Chen, Shaofei Guo, Cunfeng Li, Hui Yang, Lina Hao * School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China
highlights • A home-made soft pneumatic gripper can do size recognition task for different material objects, and adaptively grasp the objects stably for a long time even existing disturbance based on the information of a bending sensor and an air pressure sensor.
• An application of the gripper affixed on home-made palletizing robot realizes sorting of apples and lemons based on size recognition and adaptive grasping abilities.
article
info
Article history: Received 7 August 2017 Received in revised form 4 February 2018 Accepted 28 February 2018
Keywords: Soft pneumatic gripper Bending sensor Air pressure sensor Size recognition Adaptive grasping
a b s t r a c t Due to less appropriate sensors for representing the pose of soft robotic hand, the soft robotic hand usually works in an open loop control system. In this paper, only two simple sensors were used in a soft pneumatic gripper (SPG) control system to make it possess innervated-like ability. The control system was conducted with only two sensors, air pressure sensor and bending sensor where the air pressure sensor is able to detect grasping force and the bending sensor is able to detect grasping position The bending sensor was characterized by testing on pressure vs. bending and bending vs. size relationships. The principle of circular-shaped object size recognition was presented in Algorithm 1. Multi-group tests had been done on grasping different size and material objects, and test results had a strong effect on size recognition ability of the SPG. Finally, adaptive grasping control process was presented in Algorithm 2. The tests on grasping the same size objects had been done. The test results demonstrate that the SPG is able to maintain stable grasping state for a long time based on size recognition ability no matter what material the object is. At the same time, it had no effect on the grasping state at the time of existing disturbance, which had been verified by four different disturbance tests. The SPG will benefit the development of soft robotics field in picking and sorting fruits and vegetables, and an application of the SPG affixed on the home-made palletizing robot was described and conducted at last. © 2018 Elsevier B.V. All rights reserved.
1. Introduction Soft robots have been gained wide attention and researched in recent years [1–5]. Comparing with conventional rigid-bodied robots, soft robots are inherently compliant and made of soft or extensible materials, so they always can exhibit large deformation in normal operation. In addition, soft robots generally have the properties of high dexterity, safety, conformability to obstacles, manipulating variable size objects and working in structured and unstructured environment. Due to advantages of soft robots, some researchers focus on soft robotic hand instead of rigid robotic hand. The soft robotic hands can be driven by various actuators, such as motor [6], cable [7], shape memory alloy (SMA) [8,9], hydraulic author. * Corresponding E-mail address: [email protected] (L. Hao). https://doi.org/10.1016/j.robot.2018.02.020 0921-8890/© 2018 Elsevier B.V. All rights reserved.
[10] and pneumatic [11–17] actuators. However, only five of them have integration of sensors or actuators on bodies [8,14–17], and two of them separate from sensors or actuators [7,13], making the system bulky and incompact. The key challenge for fabricating a soft robotic hand is to exploit of controllable soft bodies using suitable sensors to describe its behavior. The conventional sensors for detecting grasping force and position may not be available on soft robot bodies, thus new soft material sensors are required to represent mechanical characteristic (such as deformation, compliance and extensibility) of soft robotic hand. The stretchable and flexible sensors which can be integrated with the soft bodies are fabricated and introduced [18,19]. Capacitive sensors [20], resistive sensors [21], magnetic sensors [22] and optoelectronic sensors [17] are the main four category sensors which can be mounted on soft robotic hand to measure motion information. Performance of the four sensors which are suitable for soft robotic hand rehabilitation
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Fig. 1. The physical view of molds: (a) chamber mold, (b) cover mold, (c) base mold.
Fig. 2. The physical view of SPG.
(We call this adaptive grasping), and this grasping ability make it available as the end effector in application of a robotic system. All above form the most exciting things of our work. In this paper, we only use the commercial bending sensor [23] (Flexpoint Sensor Systems Inc., UT, USA) which is two inches in length, covered in polyimide laminate, bi-directional type, solder tab connector and air pressure sensor (40PC150G2A, Honeywell Inc., NJ, USA) for the home-made soft pneumatic gripper (SPG) system, which can make SPG possess size recognition ability for circular-shaped object in the whole paper. The SPG can also achieve adaptive grasping for different size and material objects. The proposed method which makes SPG with intelligent abilities gives us another way to analyze and control the soft robotic hand. This paper is organized as follows. Section 2 introduces the fabrication technology of SPG. Section 3 tells us the control system of SPG, and the function of each component is depicted briefly. Section 4 gives the test results to verify the intelligent ability of SPG, and discusses the phenomena and results in the tests. Section 5 depicts an application example in picking and sorting apples and lemons. Section 6 summarizes the main contribution of this paper, and makes a concise conclusion. 2. Fabrication of soft pneumatic gripper
Fig. 3. The working principle and structure of one finger: (a) uninflated state, (b) inflated state.
devices were evaluated [23], and the test results indicate that a commercial bending sensor has the best overall performance with high sensitivity, short settling time, small normalized signal variation, low relaxation and hysteresis. According to the literature review, an appropriate sensor should be able to adapt to the deformation of soft robotic hand, so that the measured value from the sensor can well understand the true state of soft robotic hand in operation with innervated ability like human hand. Although the hand with integrated curvature and pressure sensing abilities can complete object recognition and grasping detection in Refs. [15] and [16], the proposed in the two papers can grasp objects with different forces being destroyed for a long time
In recent years, researchers have paid more attention on soft pneumatic hand. A good fabrication technology will benefit the working performance of soft pneumatic hand. Related works have been concluded below. A compliant, underactuated, and dexterous anthropomorphic robotic hand was presented based on soft robotic technology [11]. The hand had five fingers and a palm. The inelastic fabric was embedded into the fingers to constrain radial motion. An integrated soft pneumatic gripper with three fingers was proposed and fabricated [13]. The hand was able to adapt the finger shape to the target object and apply a certain amount of force. An air-powered prosthetic hand was built on a regular FDM 3D printer [24]. The detailed fabrication technology and required materials were introduced as open source data. This hand was developed with the purpose of being available on the medical market. All above three soft pneumatic hands are fabricated by shape deposition manufacturing method which is one of rapid prototyping method. Owing to 3D printing technology, some researchers have already tried fabricating the soft pneumatic hand by 3D printer [12,25]. The hands fabricated by 3D printer have high intensity with high tolerance inflation pressure, so that they can generate higher grasping force on objects. The novel soft pneumatic actuator-pack architecture was presented and aimed to increase the force output and bandwidth requirements for many tasks in development of the soft robotics field [26]. To summarize, soft hand actuated by pneumatic power supply has advantages of large deformation, durable, easy fabrication, low
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Fig. 4. Schematic diagram of hardware for SPG.
(1) The appropriate mold can form a well working SPG, so it is a key challenge for mold design. The molds are fabricated by 3D printer with PLA, which are shown in Fig. 1 (chamber mold, cover mold and base mold). (2) Put the segmentation blocks into the chamber mold forming different space chambers in SPG. (3) Pour the mixed silicone rubber into chamber mold. A cover mold is used to make silicone rubber full fill the chamber mold. At the same time, a bolt is fixed on the top of chamber mold forming an air hole. (4) After about 40 min, one portion of SPG is shaped. Pour the mixed silicone rubber onto base mold. At the same time a bending sensor is put on one surface of base mold, and then a fabric cloth is put onto all the surfaces of base mold. Lastly cover the ready-made portion of SPG onto the fabric cloth, and seal the joint part with the mixed silicone rubber. (5) After about 40 min, the SPG integrated with the bending sensor can be obtained. The physical view of SPG is shown in Fig. 2. Fig. 3 illustrates the working principle and structure of one finger. Fig. 3a shows the details of finger inner chamber at uninflated state. When inflated, the extensible layer (silicone rubber) extends as shown in Fig. 3b, hence the bending motion is generated and constrained by the fabric cloth which is embedded in silicone rubber forming inextensible layer. The fabric cloth relieves the silicone rubber of non-functional strains, making the inflation lead to bending rather than to radial expansion. 3. Hardware setup
Fig. 5. The Wheatstone half bridge circuit for the bending sensor.
cost. The SPG is made of silicone rubber material called Dragon Skin FX-Pro (Smooth-on, Inc., PA, USA). It is fabricated by shape deposition manufacturing method with three fingers to grasp objects. All the fingers integrated in the whole gripper can simply the pneumatic circuit, make the control system easy and realize synchronous grasping motion. Through trial and error, we concluded a relatively mature fabrication technology which can insure SPG durable for a long time. The procedure of fabrication process of SPG is described as follows.
The SPG can work effectively with a feasible control system. The schematic diagram of hardware for SPG is shown in Fig. 4. Each component and its functions of this hardware are introduced briefly. Arduino Mega 2560 is selected as the microcontroller board for SPG. It has 54 digital input/output pins (of which 15 can be used as PWM outputs), 16 analog inputs, 4 UARTs (hardware serial ports), and abundant I/O resources are in favor of control system to extend other applications. AD 620 chip is a type of low cost and low power instrumentation amplifier, and we use it to make a Wheatstone half bridge circuit which can amplify and adjust the output value of bending sensor. L298N is a type of dual full-bridge driver which can drive inductive loads such as relays, solenoids, DC and stepping motors. We use it to drive a mini pump in this paper. Both AD 620 amplification circuit and L298N driving circuit need to be supplied by DC power. The air pressure sensor with three pins (power, ground, and signal) can detect chamber pressure of SPG.
Fig. 6. Characterization of bending sensor: (a) schematic diagram in one working cycle, (b) ten group test results in different maximum inflation pressures.
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Fig. 7. Pressure vs. bending in effective working pressure range. Fig. 9. Bending vs. size fitting curve.
The bending sensor whose resistance changes as it is bent, and it has been tested to show with overall performance in sensitivity, relaxation, repeatability and hysteresis [23], and many applications of bending sensor have been studied [27,28]. Its sensing ability has been evaluated for soft robotic hand rehabilitation, so in this work, we mount it on SPG to measure the grasping position. All program algorithms are executed in MATLAB/Simulink environment, so it is easy for us to debug the control system to realize desired functions. The signal amplification circuit of the bending sensor is a key portion in hardware setup. The output signal of bending sensor is used to understand the behavior of SPG, so we expect to acquire small variation of output signal in grasping operation. In this paper, we select AD 620 chip to make the Wheatstone half bridge circuit in order to measure the position of SPG. The conducted signal amplification circuit is shown in Fig. 5. A 12 V lithium battery is used as the power source. We employ a commercial adjusting voltage module (4–38 V input voltage, 1.25–36 V output voltage) to obtain a stable 5 V output voltage (+VE /GND in Fig. 5), and then a DC/DC converter is used to acquire a dual output voltage of ±12 V (±VS /GND in Fig. 5). These constitute the power supply system. The output voltage is amplified by gain resistor RG (unit: k), and the gain value is determined by Eq. (1). We select a variable resistor (0– 200 k) to adjust the amplification factor. A constant resistor R1 , a variable resistor R2 and two variable resistors of the bending sensor comprise the Wheatstone electric bridge. The function of a variable resistor R2 is to balance the difference of two variable resistors of the bending sensor, and we can set the input voltage to AD620 at a
Fig. 10. Size recognition curve of PLA ball with diameter 60 mm. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
required value. The two variable resistors of the bending sensor are measured at between 5 and 10 k range, so bridge-arm resistor R1 is selected as 10 k and bridge-arm resistor R2 is selected as 0–20 k, which can make electric bridge balance. G=
49.4 RG
+ 1.
Fig. 8. Balls with different diameters and materials.
(1)
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Fig. 11. Size recognition test results: (a) ten group results of PLA ball with diameter 60 mm, (b) results of PLA ball with diameters 50 mm, 60 mm, 70 mm, 80 mm and 90 mm, (c) results of PLA ball, silica gel ball and rubber ball with diameter 60 mm.
4. Test results First, we clarify the meaning of some symbols to make it readable. Variable b represents the bending angle of SPG, which is acquired from Arduino Mega 2560, and we cannot know the certain bending angle according to the output voltage value. Due to repeatability and stability of bending sensor, the bending amplitude and current inflation pressure correspond to a certain value from Arduino Mega 2560, and this work will be verified in Section 4.1. Variable s represents the diameter of spherical object (hereinafter referred to as ‘‘size’’), and we select different size of objects to evaluate grasping and size recognition abilities of SPG. There is a specific transformational relationship between output voltage from air pressure sensor (unit: V) and chamber pressure (unit: bar) based on data sheet. We use variable p to represent the output voltage from air pressure sensor. 4.1. Characterization of bending sensor The working process of bending sensor during SPG grasping and releasing motions is tested in Fig. 6a. The pressure value is increasing obviously, while the bending value is not in A–B segment. The inner chamber of SPG needs a certain pressure to make it bend, and then the SPG can grasp object effectively. B–C segment represents the grasping motion within the normal working range of SPG. C– D segment represents the releasing process of SPG. D–A segment
represents the back relaxation characteristic of the bending sensor. We define the output value of air pressure sensor without bending as the initial state (A point in Fig. 6a). The initial pressure value is not zero, because of the existing barometric pressure. Due to the analog input of Arduino Mega 2560 is from 0 to 5 V, the initial bending value is set as one volt artificially by adjusting signal amplifier circuit, and the output value can be detected in initial state at any time. According to trial and error, ten groups of tests with different maximum inflation pressures are conducted in empty load condition as shown in Fig. 6b, and the effective working pressure is determined from 170 kPa to 184 kPa. It is clear that the pressure vs. bending has a linear relation characteristic in its effective working pressure range. 4.1.1. Pressure vs. bending The maximum inflation pressures ranging from 175 kPa to 184 kPa (in Fig. 6b) are used to fit linear relation curve in MATLAB software. Each group data can obtain a linear relationship, and an average of above five linear relationships to acquire the final pressure vs. bending curve which is shown in Fig. 7 as Eq. (2). The correlation coefficients of five fitted data are 0.9953, 0.9954, 0.9956, 0.9890 and 0.9844 respectively, so that the pressure vs. bending relationship in effective working pressure range is available. p = 0.0249b + 0.7146.
(2)
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Fig. 12. Grasping control results: (a) PLA ball, (b) silica gel ball, (c) rubber ball.
4.1.2. Bending vs. size Due to the repeatability and robustness characteristics of bending sensor, when the SPG reaches a grasping position, the bending sensor can output a stable value, so that we suppose this gripper can do some intelligent things, such as circular-shaped object size recognition. First, some balls are fabricated by 3D printer with different diameters in PLA, and also bought online with silica gel and rubber materials shown in Fig. 8. There are six PLA balls with diameters 50 mm, 60 mm, 70 mm, 80 mm, 90 mm and 100 mm, silica gel and rubber ball with diameter 60 mm as test objects. The PLA balls with six different diameters are selected as grasped object to fit curve. Because the size value is much bigger than the bending value, the size data are used by reducing ten times. Finally, Eq. (3) is obtained to represent the bending vs. size relationship, and its curve is shown in Fig. 9. b = 0.0169s3 − 0.3641s2 + 2.2258s − 1.5514.
(3)
4.2. Size recognition To demonstrate size recognition ability, we use the SPG to grasp objects with different sizes and materials. We measure the object
size by positioning the tip of gripper at initial state. A suitable inflation pressure is provided in effective working pressure range to insure grasping stability, and grasping state should maintain for a period of time, and then deflate pressure to make gripper release. Lastly, the grasped object size is displayed on the test system interface. The size recognition process is described in Algorithm 1. When human hand grasps an object, it forms enveloping grasping for the shape and dimension of object at first, and then power grasps. It is noteworthy that the hand pose is invariable in power grasping process, that is to say the object size determines the hand operation pose. Algorithm 1 imitates human hand grasping operation process to realize size recognition, and the key of size recognition is step 7 in Algorithm 1. A typical size recognition curve is shown in Fig. 10. The blue line represents the real reading from the bending sensor, and the red line represents the predicted value calculated by inverse of Eq. (2). By comparing the real bending value and the predicted bending value, when the predicted bending value exceeds the threshold value than the real bending value at the first time, we judge that the SPG contacts the object and begins power grasping operation, and the bending value at this time determines the object size. We set the threshold value as 0.1 to eliminate the influence of sensor noise.
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Fig. 13. Grasping control results with sine disturbance force: (a) PLA ball, (b) silica gel ball, (c) rubber ball.
Fig. 14. Grasping control results for PLA ball with disturbance at time of 10 s, 30 s and 50 s: (a) numerical simulate pulse pressure disturbance (b) pulse pressure disturbance manually.
Fig. 11a shows ten group test results of PLA ball with diameter 60 mm, and Fig. 11b shows test results of PLA ball with diameters 50 mm, 60 mm, 70 mm, 80 mm and 90 mm. The results demonstrate that the SPG can identify different size of objects accurately, and the prediction error is less than 5 mm in general. In order to verify the effectiveness of the size recognition algorithm on the
other materials, experiments have been conducted and the results shown in Fig. 11c are also satisfying. We can conclude that circularshaped object size recognition ability of SPG has a high confidence level. A correction term can be added to Algorithm 1 to make the predicted result more precise in the future. It is important to note that all these tests should be done in effective working pressure
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Fig. 15. Grasping performance: (a) PLA ball, (b) silica gel ball, (c) rubber ball, (d) PLA ball with sine disturbance.
Fig. 16. Grasping control result for PLA ball with pulse pressure disturbance manually at time of 10 s, 30 s and 50 s in improved control system.
range, and the grasping size range of SPG is about from 50 mm to 90 mm. 4.3. Adaptive grasping Taking advantage of air pressure sensor detecting grasping force and bending sensor detecting grasping position, the SPG can
accomplish adaptive grasping operation. The adaptive grasping control process is described in Algorithm 2. In Algorithm 2, when the bending sensor detects the gripper reaching desired grasping position, pressure control loop is triggered to execute, and we set desired pressure bigger than the predicted pressure by Eq. (2) to insure grasping object with different weights successfully. We just need input the grasped circularshaped object size to the control system, and the SPG can complete a stable grasping operation task. Grasping control results of PLA ball, silica gel ball and rubber ball with diameter 60 mm are shown in Fig. 12. From the test processes and results, grasping state is safe and reliable, and will hold on desired pressure under safe working pressure for a long time. In order to verify the robustness of system, an external disturbance force is applied on SPG at stable grasping period from 20 to 50 s. We use a signal generator (the output signal is set as 4 sin(10 ∗ pi ∗ t)) to determine the form of disturbance force, so that the sine disturbance force acts on the SPG in the adaptive grasping control tests. The disturbance force is generated by vibration exciter whose signal is magnified from power amplifier. The grasping control results are shown in Fig. 13. The effectiveness of the adaptive grasping control is evaluated by a mean error emean as expressed in Eq. (4), where pr represents the real measured pressure, pd represents the desired pressure, N represents the sampling number. Through experiments, the mean errors of adaptive grasping tests without any disturbance are 391 Pa, 276 Pa and 299 Pa respectively, and these with sine disturbance force are 322 Pa, 345 Pa and 368 Pa respectively. The tracking control results have little difference. Maybe this type disturbance cannot cause the undulation of pressure in chamber. Next, we do two group tests on adaptive grasping control for PLA ball with diameter 60 mm. Fig. 14a shows the control result with numerical simulate pulse pressure disturbance at time of 10 s, 30 s and
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Fig. 17. The performance of the system for picking and sorting apples and lemons task: (a) initial state, (b) grasping lemon, (c) grasping apple, (d) final state.
50 s, and Fig. 14b shows the control result with pulse pressure disturbance manually at time of 10 s, 30 s and 50 s. We can see both can arrive at desired pressure value rapidly after disturbance. Some grasping performance of tests is also shown in Fig. 15.
∑N emean =
i=1
|pr − pd | N
.
(4)
To improve the dynamic regulating performance when pulse pressure disturbance is applied, we replace the mini-pump with Mead valve (V1A02-AW1, Mead Fluid Dynamics Inc., IL, USA) in our control system. The control results with pulse pressure disturbance manually at time of 10 s, 30 s and 50 s are shown in Fig. 16.
By comparing with the mini-pump control system, the average regulating time shortens by 1.74 s (0.73 s and 2.47 s, respectively) when pulse air leakage occurs. In addition, the rising time in initial grasping manipulation shortens by 1.64 s (0.33 s and 1.97 s, respectively). The control efficiency effect improves greatly on resisting the pulse pressure disturbance in grasping manipulation. 5. Application Combined with the home-made 4-DOF palletizing robot [29], the SPG is affixed on the end joint of palletizing robot. Thus, the total system can accomplish sorting small, light and irregular shape
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objects. Next, the physical platform is used for picking and sorting apples and lemons task. The apples and lemons are put on tape transport system randomly at first. The position command is sent through the control system panel of palletizing robot manually. When the end joint of palletizing robot arrives at the desired position, the control system of SPG is triggered, and the SPG will complete size recognition. Due to different size of apples and lemons, the SPG can pick up them in specific basket sequentially. Then the palletizing robot returns to its initial state waiting for the next command. The performance of the system for picking up and sorting these two different size fruits (apples and lemons) task is shown in Fig. 17. 6. Conclusion The air pressure sensor and bending sensor proposed in our work are low cost and durable, as well as high repeatability and low relaxation. The sensors have the functions of sensing grasping force and position for soft robotic hand. With these common sensors, we believe that the proposed application method will benefit the field of soft robotics and make soft robotics more intelligent with versatile sensory capabilities. The main contribution of this paper is to integrate the bending sensor and the air pressure sensor on SPG to make it with innervated-like ability. The SPG has versatile sensory capabilities of circular-shaped object size recognition, power grasping and adaptive grasping for different size and material objects. All the grasping tests in this paper demonstrate excellent performance of SPG and its control system. The SPG affixed on the home-made palletizing robot is also verified in application of sorting different fruits effectively due to above intelligent capabilities. In the future, we hope to mount bending sensor on each finger of SPG to recognize an arbitrary object’s shape or size. Acknowledgments This work was sponsored by the National High Technology Research and Development Program of China (863 Program) under Grant No. 2015AA042302, the National Natural Science Foundation of China under Grant No. 61573093 and No. U1613205. The authors would like to thank Dr Jingwen Xu for her help with data processing in School of Science at Northeastern University, China. Yang Chen made all the circuit boards, designed the control system, carried out all the experiments, analyzed the data and wrote the paper. The soft pneumatic gripper was fabricated by Shaofei Guo. Cunfeng Li participated in conducting experiment and analyzing experimental results. Hui Yang provided aspect of pneumatic knowledge discussing on system design. Lina Hao inspired us to design a soft hand with intelligent characteristics such as abundant sensing ability, recognition capability, adaptive to diversity of object and reviewed this manuscript. References [1] D. Trivedi, C.D. Rahn, W.M. Kier, I.D. Walker, Soft robotics: Biological inspiration, state of the art, and future research, Appl. Bionics Biomech. 3 (2008) 99–117. [2] C. Laschi, B. Mazzolai, M. Cianchetti, Soft robotics: Technologies and systems pushing the boundaries of robot abilities, Sci. Robot. 1 (2016) eaah3690. [3] D. Rus, M.T. Tolley, Design, fabrication and control of soft robots, Nature 7553 (2015) 467–475. [4] S.G. Nurzaman, F. Iida, L. Margheri, C. Laschi, Soft robotics on the move: scientific networks, activities, and future challenges, Soft Robot. 2 (2014) 154– 158. [5] C. Majidi, Soft robotics: a perspective—current trends and prospects for the future, Soft Robot. 1 (2014) 5–11. [6] M. Tavakoli, A.T. de Almeida, Adaptive under-actuated anthropomorphic hand: Isr-softhand, in: Chicago, IL: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS. September 2014, pp. 1629–1634.
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Yang Chen received the B.S. degree in mechanical engineering and automation from China University of Mining and Technology, Xuzhou, China in 2012, M.S. degree in mechatronic engineering from Northeastern University, Shenyang, China in 2014. He is currently a Ph.D. candidate at Northeastern University, Shenyang, China. His research interests include driving, modeling and control of artificial muscles especially in IPMC and SMA, and soft robotic system. He is a student member of IEEE and International Society of Bionic Engineering.
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Y. Chen et al. / Robotics and Autonomous Systems 104 (2018) 14–24 Shaofei Guo received the B.S. degree in mechanical engineering from Changchun University of Science and Technology, Changchun, China in 2015. He is currently a master candidate at Northeastern University, Shenyang, China.
Hui Yang received the B.S. degree and M.S. degree in machinery design and manufacture from Liaoning Shihua University, Fushun, China in 2010 and 2013, respectively. He is currently a Ph.D. candidate at Northeastern University, Shenyang, China.
Cunfeng Li received the B.S. degree in mechanical engineering and automation from Hebei University, Baoding, China in 2015. He is currently a master candidate at Northeastern University, Shenyang, China.
Lina Hao received the B.S. degree in machinery design and manufacture from Shenyang Ligong University, Shenyang, China in 1989, M.S. degree in solid mechanics from Northeastern University, Shenyang, China in 1994 and Ph.D. degree in control theory and control engineering from Northeastern University, Shenyang, China in 2001. From Dec. 2005 to Dec. 2006, she did research on AFM as a visiting scholar in Michigan State University: Detroit, America. Currently, she is a professor in School of Mechanical Engineering and Automation in Northeastern University, China. Her research interests include robot system and intelligent control, intelligent structure and precision motion control system.