ORIGINAL_ARTICLE
A Novel Multimode Mobile Robot with Adaptable Wheel Geometry for Maneuverability Improvement
In this paper, an innovative mobile platform is presented which is equipped by three new wheels. The core of the new idea is to establish a new design of rigid circular structure which can be implemented as a wheel by variable radius. The structure of wheel includes a circular pattern of a simple two-link mechanism assembled to obtain a wheel shape. Each wheel has two degrees of freedom. The first is to rotate wheel axis and the second is to change the wheel radius. As the first step, after definition of the new model, its spatial kinematics and constraints will be formulated. The well-known Newton-Raphson algorithm is implemented to find the current response of the kinematic model. A semi-dynamic formulation is further utilized to find the torque of motors for adapting the required wheel radius for maneuverability improvement on rough surfaces. The principles of virtual work will be used to extract the torque values numerically. The ability of the proposed robot for performing the required tasks will finally be checked by some simulations.
https://ijr.kntu.ac.ir/article_165700_aeefc331711f244673d58bad6b40f105.pdf
2016-03-01
1
15
adaptable wheel geometry
multimode mobile robot
maneuverability
kinematic analysis
Arman
Mardani
yovas1369@gmail.com
1
Yazd University
AUTHOR
Saeed
Ebrahimi
ebrahimi@yazd.ac.ir
2
Yazd University
LEAD_AUTHOR
ORIGINAL_ARTICLE
A Navigation System for Autonomous Robot Operating in Unknown and Dynamic Environment: Escaping Algorithm
In this study, the problem of navigation in dynamic and unknown environment is investigated and a navigation method based on force field approach is suggested. It is assumed that the robot performs navigation in unknown environment and builds the map through SLAM procedure. Since the moving objects' location and properties are unknown, they are identified and tracked by Kalman filter. Kalman observer provides important information about next paths of moving objects which are employed in finding collision point and time in future. In the time of collision detection, a modifying force is added to repulsive and attractive forces corresponding to the static environment and leads the robot to avoid collision. Moreover, a safe turning angle is defined to assure safe navigation of the robot. The performance of proposed method, named Escaping Algorithm, is verified through different simulation and experimental tests. Besides, comparison between Escaping Algorithm and Probabilistic Velocity Obstacle, based on computational complexity and required steps for finishing the mission is provided in this paper. The results show Escaping Algorithm outperforms PVO in term of dynamic obstacle avoidance and complexity as a practical method for autonomous navigation Abstract—In this study, the problem of navigation in dynamic and unknown environment is investigated and a navigation method based on force field approach is suggested. It is assumed that the robot performs navigation in unknown environment and builds the map through SLAM procedure. Since the moving objects' location and properties are unknown, they are identified and tracked by Kalman filter. Kalman observer provides important information about next paths of moving objects which are employed in finding collision point and time in future. In the time of collision detection, a modifying force is added to repulsive and attractive forces corresponding to the static environment and leads the robot to avoid collision. Moreover, a safe turning angle is defined to assure safe navigation of the robot. The performance of proposed method, named Escaping Algorithm, is verified through different simulation and experimental tests. Besides, comparison between Escaping Algorithm and Probabilistic Velocity Obstacle, based on computational complexity and required steps for finishing the mission is provided in this paper. The results show Escaping Algorithm outperforms PVO in term of dynamic obstacle avoidance and complexity as a practical method for autonomous navigation.
https://ijr.kntu.ac.ir/article_41607_d7e0e353d404db1f677bb8bf235fcaf3.pdf
2016-03-01
16
31
Farnaz
Adib yaghmaie
1
Faculty of Electrical Engineering, K. N. Toosi Univ. of Technology, Tehran, Iran
AUTHOR
Amir
Mobarhani
2
Faculty of Electrical Engineering, K. N. Toosi Univ. of Technology, Tehran, Iran
AUTHOR
Hamidreza
Taghirad
3
Faculty of Electrical Engineering, K. N. Toosi Univ. of Technology, Tehran, Iran
LEAD_AUTHOR
[1] A. Ferworn, J. Tran, A. Ufkes, and A. D’Souza, “Initial experiments on 3d modeling of complex disaster environments using unmanned aerial vehicles,” in Safety, Security, and Rescue Robotics (SSRR), 2011 IEEE International Symposium on. IEEE, 2011, pp. 167–171.
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[17] A. Bautin, L. Martinez-Gomez, and T. Fraichard, “Inevitable collision states: a probabilistic perspective,” in Robotics and Automation (ICRA), 2010IEEE International Conference on. IEEE, 2010, pp. 4022–4027.
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[19] J. Chuang and N. Ahuja, “An analytically tractable potential field model of free space and its application in obstacle avoidance,” Systems, Man, andCybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 28, no. 5, pp. 729–736, 1998.
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[29] S. Thrun, C. Martin, Y. Liu, D. Hahnel, R. Emery-Montemerlo, D. Chakrabarti, and W. Burgard, “A real-time expectation-maximization algorithm for acquiring multiplanar maps of indoor environments with mobile robots,” Robotics and Automation, IEEE Transactions on, vol. 20, no. 3, pp. 433–443, 2004.
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[35] F Adib Yaghmaie, A Mobarhani, HD Taghirad, “A new method for mobile robot navigation in dynamic environment: Escaping algorithm”, in IEEE Robotics and Mechatronics (ICRoM), 2013 First RSI/ISM International Conference on, pp. 212-217.
35
ORIGINAL_ARTICLE
Exploring Social Robots as a tool for Special Education to teach English to Iranian Kids with Autism
This paper investigates the effects of Robot Assisted Language Learning (RALL) on English vocabulary learning and retention of Iranian children with high-functioning autism. Two groups of three male students (6-10 years old) with high-functioning autism participated in the current study. The humanoid robot NAO was used as a teacher assistant to teach English to in one group whereas no robot was used in the other group. Both group programs consisted of 12 sessions held within a 2-month period. Using a pre-test, mid-test, immediate post-test, delayed post-test design, this study measured the learning gains of the participants. The group with the humanoid robot outperformed the other group in the designed tests which showed the effectiveness of robot assisted language learning. This was further supported by comparing and contrasting the both groups’ parents’ feedbacks as well as the results obtained from the qualitative analysis of the video records. The findings of this study could be a starting point for a new line of research in second/foreign language education specific to children with autism.
https://ijr.kntu.ac.ir/article_165699_ff2a61ce1568adaa77d850ef1daf8a31.pdf
2016-03-01
32
43
Social Humanoid robot
High-functioning autism
Foreign language education
RALL
vocabulary learning
Minoo
Alemi
minooalemi2000@yahoo.com
1
Assistant Professor, Islamic Azad University, and Research Associate in SR Lab, Sharif University of Technology
LEAD_AUTHOR
ORIGINAL_ARTICLE
Tumor Detection and Morphology Assessment in the Liver Tissue Using an Automatic Tactile Robot
In this paper an automatic examination robot was developed to improve the process of cancer detection, tumor localization and geometrical shape diagnosis. A uniformly distributed compressive load was applied to the top tissue surface and the resultant mechanical stress was measured that was employed for the tumor diagnosis task. The experimental examinations were performed on the soft tissue of the liver. A compression test was used to extract viscoelastic properties of tissue. Viscoelastic coefficients were used in the finite element modeling and the capability of the robotic-assisted tumor detection procedure was verified. Finally to localize the tumor embedded in the tissue, two sinusoidal and step paths were generated which was followed by the robot. The mean errors of path following by the automatic examination robot affirmed the accuracy and the reliability of the Cartesian mechanism in the soft tissue scanning.
https://ijr.kntu.ac.ir/article_41610_8a3c52cd22333603aed30562afb7ad71.pdf
2016-03-01
44
53
Soft tissue
Mechanical stress
Tumor diagnosis
Robotic examination
Seyed Mohammad Salman
Lari
1
Faculty of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
AUTHOR
Afsaneh
Mojra
mojra@kntu.ac.ir
2
Faculty of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
LEAD_AUTHOR
[1] M. Naghavi, H. Wang, R. Lozano, A. Davis, X. Liang, M. Zhou, S.E. Vollset, A.A. Ozgoren, S. Abdalla, F. Abd-Allah and et al, Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, Lancet, 385 (2015) 117-171.
1
[2] F. Xavier Bosch, J. Ribes, M. Diaz, R. Cleries, Primary liver cancer: Worldwide incidence and trends. Gastroenterology, 127 (2004) 5-16.
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[4] H.O. Yegingil, Breast cancer detection and differentiation using piezoelectric fingers, PhD diss., Drexel University, (2009).
4
[5] M Kaufmann, G von Minckwitz, R.Smith, et al. International expert panel on the use of primary (preoperative) systemic treatment of operable breast cancer: review and recommendations, JClinOncol, 21 (2003) 2600–2608.
5
[6] A. Mojra, S. Najarian, S.M. Towliat Kashani, F. Panahi, A novel tactile-guided detection and three-dimensional localization of clinically significant breast masses, Journal of medical engineering & technology, 36, no. 1 (2012) 8-16.
6
[7] A.P. Sarvazyan, Knowledge-based mechanical imaging, In Computer-Based Medical Systems, 1997. Proceedings, Tenth IEEE Symposium on, pp. 120-125. IEEE, (1997).
7
[8] S. Najarian, M. Fallahnezhad, E. Afshari, Advances in medical robotic systems with specific applications in Surgery – A Review, J Med Eng Technol, 35 (2011) 13-19.
8
[9] R.D. Howe, Tactile sensing and control of robotic manipulation. Adv Robot, 8 (1994) 245–261.
9
[10] G. Riva, L. Gamberini, Virtual reality in telemedicine, communication through virtual technology, ISO Press Amsterdam, 2003.
10
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[12] P. Dario, M.C. Carrozza, L. Lencioni, B. Magnani, S. D’Attanasio, A micro robot system for colonoscopy, Proceedings of the IEEE International Conference on Robotics and Automation, (1997) 1567–1572.
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[13] Y. Murayama, C.E. Constantinou, S. Omata, Development of tactile mapping system for the stiffness characterization of tissue slice using novel tactile sensing technology, Sensors and Actuators A-Physical, 120 (2005) 543–549.
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[14] J. Dargahi, S. Najarian, An endoscopic force position grasper with minimum sensors, Can J Electr Comput Eng, 28 (2003) 155–161.
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[15] J.R. Harris, M.E. Lippman, M. Morrow, S. Hellman, Diseases of the breast, Lippincott-Raven: 1996.
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[16] L. Han, M. Burcher, J.A. Noble, Non-invasive measurement of biomechanical properties of in vivo soft tissues, MICCAI 2002 LNCS, 2488 (2002) 208–215.
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[17] C.S. Kaufman, L. Jacobson, B.A. Bachman, L.B. Kaufman, Digital documentation of the physical examination: moving the clinical breast exam to the electronic medical record, The American journal of surgery, 192(4) (2006) 444-449.
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[18] S. Schostek, M.J. Binser, F. Rieber, C.N. Ho, M.O. Schurr, G.F. Buess, Artificial tactile feedback can significantly improve tissue examination through remote palpation, Surgical endoscopy, 24(9) (2010) 2299-2307.
18
[19] T. Hoshi, Y. Kobayashi, T. Miyashita, M.G. Fujie, (2010, October), Quantitative palpation to identify the material parameters of tissues using reactive force measurement and finite element simulation, In Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on (pp. 2822-2828). IEEE.
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[20] F.J. Carter, T.G. Frank, P.J. Davies, D. McLean, A. Cuschieri, Measurements and modelling of the compliance of human and porcine organs, Medical Image Analysis, 5(4) (2001) 231-236.
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[21] H. Liu, D.P. Noonan, K. Althoefer, L.D. Seneviratne, (2008, May), Rolling mechanical imaging: a novel approach for soft tissue modelling and identification during minimally invasive surgery, In Robotics and Automation, 2008, ICRA 2008, IEEE International Conference on (pp. 845-850), IEEE.
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[22] P.L. Yen, D.R. Chen, K.T. Yeh, P.Y. Chu, Lateral exploration strategy for differentiating the stiffness ratio of an inclusion in soft tissue. Medical engineering & physics, 30(8) (2008) 1013-1019.
22
[23] M. Ayyildiz, B. Guclu, M.Z. Yildiz, C. Basdogan, A novel tactile sensor for detecting lumps in breast tissue, In Haptics: Generating and Perceiving Tangible Sensations (pp. 367-372) (2010) Springer Berlin Heidelberg.
23
[24] J.H. Lee, C.H. Won, K. Yan, Y. Yu, L. Liao,, Tactile sensation imaging for artificial palpation, In Haptics: Generating and Perceiving Tangible Sensations (pp. 373-378) (2010) Springer Berlin Heidelberg.
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[25] M.P. Ottensmeyer, Minimally invasive instrument for in vivo measurement of solid organ mechanical impedance, Massachusetts Institute of Technology, 2001.
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[26] J. Dargahi, R. Sedaghati, H. Singh, S. Najarian, Modeling and testing of an endoscopic piezoelectric-based tactile sensor, Mechatronics, 17(8) (2007) 462-467.
26
[27] A. Mojra, S. Najarian, S. M. Towliat Kashani, F. Panahi, M. Yaghmaei, A novel haptic robotic viscogram for characterizing the viscoelastic behaviour of breast tissue in clinical examinations, The International Journal of Medical Robotics and Computer Assisted Surgery, 7(3) (2011) 282-292.
27
[28] C. Ledermann, H. Alagi, H. Woern, R. Schirren, S. Reiser, Biomimetic tactile sensor based on Fiber Bragg Gratings for tumor detection—Prototype and results, pp. 1-6.
28
[29] G. H. Büscher, R. Kõiva, C. Schürmann, R. Haschke, H. J. Ritter, Flexible and stretchable fabric-based tactile sensor, Robotics and Autonomous Systems, 63 (2015) 244-252.
29
[30] A. Mojra, S. Najarian, S.M. Hosseini, S.M. Towliat Kashani, F. Panahi, Abnormal Mass Detection in a Real Breast Model: A Computational Tactile Sensing Approach, World Congress on Medical Physics and Biomedical Engineering, 25 (2009) 115-118.
30
[31] A. Mojra, S. Najarian, S.M. Towliat Kashani, F. Panahi , Artificial Tactile Sensing Capability Analysis in Abnormal Mass Detection with Application in Clinical Breast Examination, World Congress on Engineering, 3 (2011).
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[32] A. Mojra, S. Najarian, S.M. Towliat Kashani, F. Panahi, M. Ali Tehrani, A novel robotic tactile mass detector with application in clinical breast examination, Minimally Invasive Therapy & Allied Technologies, 21 (2012) 210-221.
32
[33] Y. Kim, B. Ahn, Y. Na, T. Shin, K. Rha, J. Kim, Digital rectal examination in a simulated environment using sweeping palpation and mechanical localization. International Journal of Precision Engineering and Manufacturing, 15 (2014) 169-175.
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[34] A.A. Wahba, N.M.M. Khalifa, A.F. Seddik, M.I. El-Adawy, A Finite Element Model for Recognizing Breast Cancer, Journal of Biomedical Science and Engineering, 7 (2014).
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[35] A.P. Astrand, V. Jalkanen, B.M. Andersson, O.A. Lindahl, Detection of Stiff Nodules Embedded in Soft Tissue Phantoms, Mimicking Cancer Tumours, Using a Tactile Resonance Sensor, Journal of Biomedical Science and Engineering, 7 (2014).
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[36] Y.B. Fu, C.K. Chui, C.L. Teo, E. Kobayashi, Elasticity imaging of biological soft tissue using a combined finite element and non-linear optimization method, Inverse Problems in Science and Engineering, 23 (2014) 179-196.
36
[37] M. Heverly, P. Dupont, J. Triedman, Trajectory Optimization for Dynamic Needle Insertion, Robotics and Automation, (2005) 1646-1651.
37
[38] D. Valtorta, E. Mazza, Dynamic measurement of soft tissue viscoelastic properties with a torsional resonator device, Medical Image Analysis, 9 (2005) 481-490.
38
[39] H. Shi, A. Farag, Validating linear elastic and linear viscoelastic models of lamb liver tissue using cone-beam CT, International Congress Series, 1281 (2005) 473-478.
39
[40] M. Zhang, B. Castaneda, Z. Wu, P. Nigwekar, J.V. Joseph, D.J. Rubens, K.J. Parker, Congruence of Imaging Estimators and Mechanical Measurements of Viscoelastic Properties of Soft Tissue, Ultrasound in Medicine & Biology, 33 (2007) 1617-1631.
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[41] Y. Kobayashi, A. Onishi, T. Hoshi, K. Kawamura, M. Hashizume, M.G. Fujie, Development and validation of a viscoelastic and nonlinear liver model for needle insertion, International Journal of Computer Assisted Radiology and Surgery, 4 (2009) 53-63.
41
[42] S. Raghunathan, D. Evans, J.L. Sparks, Poroviscoelastic Modeling of Liver Biomechanical Response in Unconfined Compression, Annals of Biomedical Engineering, 38 (2010) 1789-1800.
42
[43] I. Sakuma, Y. Nishimura, C.K. Chui, E. Kobayashi, H. Inada, X. Chen, T. Hisada, In vitro measurement of mechanical properties of liver tissue under compression and elongation using a new test piece holding method with surgical glue, Surgery Simulation and Soft Tissue Modeling, 2673 (2003) 284-292.
43
ORIGINAL_ARTICLE
Dynamic modeling and control of a 4 DOF robotic finger using robust adaptive and neural adaptive controllers
Human hands are a great challenge and fascinate many roboticists since they present tremendous skill and versatility .In this research, kinematic and dynamic equations of a 4-DOF 3-link robotic finger are derived using Lagrangian formulations. The main idea for modeling the muscles is placing several springs and dampers between the linkages. By using this idea, dynamic equations of a 4-DOF robotic finger will be derived. By taking advantage of these dynamic models for robotic finger and robotic hand, applying some advanced controllers, which can control the system in presence of parametric uncertainty, will be possible. In order to track the desired trajectory of tapping, tow advanced controllers consisting of adaptive-robust and adaptive-neural are applied to the robotic finger considering a 10% parametric uncertainty in the parameters of the system. By comparing the simulation results of tracking errors and input torques, it is revealed that the adaptive-neural controller has a better performance.
https://ijr.kntu.ac.ir/article_165698_b9ce9b8d5f4d9be6e874ce88710f3839.pdf
2016-03-01
54
64
Biomechanics
Robotic Finger
Dynamic Modeling
Nural-adaptive controller
Robust-Adaptive controller
Fatemeh
Katibeh
fkatibe@gmail.com
1
shirazu.ac.ir
LEAD_AUTHOR
Mohammad
Eghtesad
eghtesad@shirazu.ac.ir
2
shirazu.ac.ir
AUTHOR
Yousef
Bazargan-Lari
bazarganlari@iaushiraz.ac.ir
3
iaushiraz.ac.ir
AUTHOR
ORIGINAL_ARTICLE
Soft Tissue Modeling Using ANFIS for Training Diagnosis of Breast Cancer in Haptic Simulator
Soft tissue modeling for the creation of a haptic simulator for training medical skills has been the focus of many attempts up to now. In soft tissue modeling the most important parameter considered is its being real-time, as well as its accuracy and sensitivity. In this paper, ANFIS approach is used to present a nonlinear model for soft tissue. The required data for training the neuro-fuzzy model of soft tissue is provided from breast tissue numerical modeling in ANSYS 12.0 software. To validate the ANSYS mode, numerical data have been compared with the experimental data with an average error of less than 3%. On the other hand, for the validation of ANFIS model, testing session indicates a root mean square error of less than 0.02 (N), which shows the high degree of accuracy for the presented model. To evaluate the efficiency of this model, it has been used in the breast cancerous tumors diagnosis training haptic simulator. The presented model’s real-time feature is about 100 times more than the maximum amount needed for force modeling simulations.
https://ijr.kntu.ac.ir/article_43094_5110b7f7ad20efa7b3a5d999070d78d5.pdf
2016-03-01
65
72
Neuro-Fuzzy
haptic simulation
soft tissue modeling
breast cancer
Saeed
Amirkhani
amirkhani_saeed@yahoo.com
1
Faculty of Mechanical Engineering, University of Guilan, Rasht, Iran
LEAD_AUTHOR
Ali
Nahvi
2
K.N. Toosi University of Technology
AUTHOR
[1] A. Jemal, R. Siegel, E. Ward, Y. Hao, J. Xu, M. J. Thun, “Cancer statistics,” CA: a cancer journal for clinicians, vol. 59, no. 4, pp. 225–249, 2009.
1
[2] D. Saslow, J. Hannan, J. Osuch, M. H. Alciati, C. Baines, M. Barton, J. K. Bobo, C. Coleman, M. Dolan, G. Gaumer, et al., “Clinical breast examination: practical recommendations for optimizing performance and reporting,” CA: A Cancer Journal for Clinicians, vol. 54, no. 6, pp. 327–344, 2004.
2
[3] H. Abbasi, S. Haghzad, S. Amirkhani, and M. Eshghi, "Early detection of breast cancer using genetic algorithm and Neuro-Fuzzy network," in 2014 22nd Iranian Conference on Electrical Engineering (ICEE), 2014, pp. 1939-1944.
3
[4] R. M. Satava, “Virtual reality surgical simulator, Surgical endoscopy 7,” vol. 3, pp. 203–205, 1993.
4
[5] J. D. Westwood, “Anatomical and physiological models for surgical simulation,” Medicine Meets Virtual Reality: The Convergence of Physical & Informational Technologies: Options for a New Era in Healthcare, vol. 62, pp. 23, 1999.
5
[6] L. M. Sutherland, P. F. Middleton, A. Anthony, J. Hamdorf, P. Cregan, D. Scott, G. J. Maddern, “Surgical simulation: a systematic review,” Annals of surgery, vol. 243, no. 3, pp. 291, 2006.
6
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