2019
5
1
0
16
1

Reconstructing human push recovery reactions using a three dimensional underactuated bipedal robot
http://ijr.kntu.ac.ir/article_90461.html
1
This paper presents the ability of hybrid zero dynamics (HZD) feedback control method to reproduce human like movements for walking push recovery of an underactuated 3D biped model. The balance recovery controller is implemented on a threedimensional underactuated bipedal model subjected to a push disturbance. The biped robot model is considered as a hybrid system with eight degrees of freedom (DOF) in the single support phase and two degrees of underactuation in the ankle joint. The control is done based on the method of virtual constraints and HZD, by adjusting the desired trajectory of the eventbased feedback controller. Several simulations have been done considering pushes exerted during walking. The results showed the performance of the method in recovery of pushes occurring in the sagittal and frontal planes and also in the both directions, simultaneously. The results showed that the simulated motions can be characterized in terms of strategies observed in human for balance recovery against perturbations during walking.
0

1
15


Behnam
Miripour Fard
Department of Robotics Engineering, Hamedan University of Technology, Hamedan, Iran
Iran
bmf@guilan.ac.ir


Ahmad
Bagheri
Department of Mechanical Engineering
Faculty of Engineering Guilan University, Guilan, Iran
Iran
bagheri@guilan.ac.ir


Nader
Nariman Zadeh
Dept. of Mechanical Eng. Engineering Faculty, University of Guilan, Po Box 3756, RASHT, IRAN
Iran
nnzadeh@guilan.ac.ir
Inverse biomimetic
Biped robot
3D model
underactuation
walking push recovery
hybrid feedback control
[1. A. Seyfarth, S. Grimmer, D. F. B. Haufle, K.T Kalveram, Can Robots Help to Understand Human Locomotion? Automatisierungstechnik, 60 (11) (2012) 653660.##2.K. T. Kalveram, A. Seyfarth, Inverse biomimetics: How robots can help to verify concepts concerning sensorimotor control of human arm and leg movements, Journal of PhysiologyParis, 103 (3–5) (2009) 232243.##3. T. Buschmann, A. Ewald, A. V. Twickel, and A. Büschges, Controlling legs for locomotioninsights from robotics and neurobiology, Bioinspir. Biomim. 10 (2015)138.##4. J. Rummel, Y. Blum, and A. Seyfarth, Robust and efficient walking with springlike legs, Bioinsp. Biomim, 5 (2010) 113##5. F. Boyer, and M. Porez, Multibody system dynamics for bioinspired locomotion: from geometric structures to computational aspects, Bioinspir. Biomim. 10 (2015) 121.##6. J. Y. Jun, and J. E. Clark, Characterization of running with compliant curved legs, Bioinspir. Biomim. 10 (2015) 118##7. B. Stephens, Push recovery control for forcecontrolled humanoid robots, PhD thesis, Carnegie Mellon University, USA, 2011.## 8. J. Pratt, J. Carff, S. Drakunov, and A.Goswami, Capture Point: A Step toward Humanoid Push Recovery, in: 6th IEEERAS International Conference on Humanoid Robots, Genova, Italy, (2006) pp. 200 – 207##9. S. K. Yun, and A. Goswami, MomentumBased Reactive Stepping Controller on Level and Nonlevel Ground for Humanoid Robot Push Recovery, IROS 2011, San Francisco, CA, September 2011.##10. B. Miripour Fard, A. Bagheri, A. S. Khoskbijari, Receding Horizon Based Control of Disturbed Upright Balance with Consideration of Foot Tilting, IJE TRANSACTIONS A: Basics, 26 (10) (2013) 12431254.##11. Y. J. Kim, J.Y. Lee, and J. J. Lee, A TorsoMoving Balance Control Strategy for a Walking Biped Robot Subject to External Continuous Forces, Int. J. Humanoid. Robotics. 12 (01) (2015).##12. Y. J. Kim, J.Y. Lee, and J. J. Lee, A Balance Control Strategy for a Walking Biped Robot under Unknown Lateral External Force using a Genetic Algorithm, Int. J. Humanoid. Robotics. 12 (02) (2015) 137##13. A. H. Adiwahono, C. M. Chew, W. Huang, and Y. Zheng, Push recovery controller for bipedal robot walking" In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Singapore, (2009) pp.162167.##14. A. H. Adiwahono, C. M. Chew, W. Huang and V. H. Dau, Humanoid robot push recovery through walking phase modification. In: IEEE Conference on Robotics Automation and Mechatronics (RAM), (2010) pp. 569574.##15. A. H. Adiwahono, C. M. Chew, and B. Liu, Push Recovery through Walking Phase Modification for Bipedal Locomotion, Int. J. Human. Robot. 10 (03) (2013) 130.##16. J. Urata, K. Nshiwaki, Y. Nakanishi, et al., Online decision of foot placement using singular LQ preview regulation, In: 2011 IEEE/RAS International Conference on Humanoid Robots, Bled, Slovenia, (2011) pp.1318.##17. T. Wang, Ch. Chevallereau and C. F. Rengifo, Walking and steering control for a 3D biped robot considering ground contact and stability, Robotics and Autonomous Systems; 60 (2012) 962977.##18. B. Miripour Fard, A. Bagheri and N. Narimanzadeh, Limit cycle walker push recovery based on a receding horizon control scheme, Proc IMechE Part I: J Systems and Control Engineering; 226(7) (2012) 914–926.##19. Ch. Chevallereau, J. W. Grizzle and Ch. L.Shih, Asymptotically stable walking of a fivelink underactuated 3D bipedal robot, IEEE Trans Robot; 25(1) (2009) 3750.##20 Ch. Chevallereau, J. W. Grizzle and Ch. L Shih, Steering of a 3D bipedal robot with an underactuated ankle, In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, (2010) pp.12421247.##21 G. Song and M. Zefran, Underactuated dynamic threedimensional bipedal walking, In: 2006 IEEE International Conference on Robotics and Automation, Orlando, FL, (2006) pp. 854859.##22 D. Tlalolini, Ch. Chevallereau, and Y. Aoustin, Humanlike walking: optimal motion of a bipedal robot with toerotation motion, IEEE/ASME Transactions on Mechatronics, 16(2) (2011) 310320.##23. J. J. Craig, Introduction to Robotics Mechanics and Control, 2th edn. (Addison Wesley, USA, 1989), pp.196205.##24. D. Tlalolini, Y. Aoustin and Ch. Chevallereau, Design of a walking cyclic gait with single support phases and impacts for the locomotor system of a thirteenlink 3D biped using the parametric optimization, Multibody Syst Dyn; 23 (2010) 3356.##25. W. Khalil, and J. Kleinfinger, A new geometric notation for open and closed loop robots, In: IEEE Conference on Robotics and Automation, (1985) pp.11741180.##26. ER. Westervelt, J. W. Grizzle, Ch. Chevallereau, et al., Feedback control of dynamic bipedal robot locomotion, (London: Taylor and Francis/CRC, 2007).##27. B. Morris and J. W. Grizzle, A restricted Poincar´e map for determining exponentially stable periodic orbits in systems with impulse effects: Application to bipedal robots, In: 2005 IEEE Conference on Decision and Control, Seville, Spain, (2005) pp. 41994206.##28. B. Morris and J. W. Grizzle, Hybrid invariant manifolds in systems with impulse effects with application to periodic locomotion in bipedal robots, IEEE Transactions on Automatic Control, 54(8) (2009) 17511764.##29. D.G.E. Hobbelen, and M. Wisse, A disturbance rejection measure for limit cycle walkers: the gait sensitive norm, IEEE Trans. on Robotics, 23(6) (2007) 12131224.##30. V. B. Semwal, S. A. Katiyar, R. Chakraborty, G. C. Nandi, Biologicallyinspired push recovery capable bipedal locomotion modeling through hybrid automata, Robotics and Autonomous Systems 70 (2015) 181–190.##31. V. B. Semwal, and G. C. Nandi, Toward Developing a Computational Model for Bipedal Push Recovery–A Brief, IEEE Sensors Journal, 15(4) (2015) 20212022.##32. V. B. Semwal, A. Bhushan, and G. C. Nandi, Study of humanoid push recovery based on experiments, in: Proceeding of IEEE International Conference on CARE, (2013), pp. 1–6.##33. Z. Jie, S. Schutz, K. Berns, Biologically motivated push recovery strategies for a 3D bipedal robot walking in complex environments, in: 2013 IEEE International Conference on Robotics and Biomimetics, (ROBIO), Shenzhen, China, 2013, pp.12581263.##34. A. F. Cordero, HJFM. Koopman and FCT. Helm, Mechanical model of the recovery from stumbling, Biological Cybernetics, 91(2004) 212220.##]
1

Workspace Boundary Avoidance in Robot Teaching by Demonstration Using Fuzzy Impedance Control
http://ijr.kntu.ac.ir/article_90484.html
1
The present paper investigates an intuitive way of robot path planning, called robot teaching by demonstration. In this method, an operator holds the robot endeffector and moves it through a number of positions and orientations in order to teach it a desired task. The presented control architecture applies impedance control in such a way that the endeffector follows the operator’s hand with desired dynamic properties. The operator often teaches the robot in the middle of the robot workspace. Then, this leads to lose a lot of accessible space. Workspace boundary is specified where a joint meets its end or a singularity happens. In this paper, a method is proposed to warn the operator before the endeffector faces the boundary of the workspace which results in using the robot workspace efficiently. It is achieved by means of two fuzzy controllers which smoothly increase the damping parameter of the impedance controller when the robot is closing on to a joint limit or a singularity. The increase of damping parameter dissipates the kinetic energy that is imposed by the operator to move the endeffector toward workspace boundary. The proposed method is applied on an industrial grade SCARA type robot. Experimental results show the effectiveness of the proposed method in a clear way.
0

16
26


Seyyed Ali
Mousavi Mohammadi
Center of excellence on soft computing and intelligent information processing, Department of mechanical engineering, Faculty of engineering, Ferdowsi Univeristy of Mashhad, Mashhad, Iran
Iran
ali.mousavimohammadi@mail.um.ac.ir


Alireza
Akbarzadeh
Mechanical Engineering Department, Center of Excellence on Soft Computing and Intelligent Information Processing, Ferdowsi University of Mashhad, Mashhad, Iran
Iran
ali_akbarzadeh@um.ac.ir


Ehsan
Adel Rastkhiz
Center of Excellence on Soft Computing and Intelligent Information Processing, Mechanical Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
Iran
ehsan_a_rastkhiz@yahoo.com


Morteza
Shariatee
Center of Excellence on Soft Computing and Intelligent Information Processing, Mechanical Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
Iran
morteza_shariatee_sc@yahoo.com
Physical humanrobot interaction
Teaching by demonstration Impedance control
Fuzzy switching control
[[1] Z. Pan, H. Zhang, Robotic machining from programming to process control: a complete solution by force control, Industrial Robot: An International Journal, Vol. 35, No. 5, pp. 400409, 2008.##[2] J. Norberto Pires, G. Veiga, R. Araújo, Programmingbydemonstration in the coworker scenario for SMEs, Industrial Robot: An International Journal, Vol. 36, No. 1, pp. 7383, 2009.##[3] R. D. Schraft, C. Meyer, The need for an intuitive teaching method for small and medium enterprises, VDI BERICHTE, Vol. 1956, pp. 95, 2006.##[4] Z. Pan, J. Polden, N. Larkin, S. Van Duin, J. Norrish, Recent progress on programming methods for industrial robots, Robotics and ComputerIntegrated Manufacturing, Vol. 28, No. 2, pp. 8794, 2012.##[5] M. H. Ang Jr, W. Lin, S.Y. Lim, A walkthrough programmed robot for welding in shipyards, Industrial Robot: An International Journal, Vol. 26, No. 5, pp. 377388, 1999.##[6] L. Bascetta, G. Ferretti, G. Magnani, P. Rocco, Walkthrough programming for robotic manipulators based on admittance control, Robotica, Vol. 31, No. 07, pp. 11431153, 2013.##[7] E. G. Kaigom, J. Roßmann, Physicsbased simulation for manual robot guidance—An eRobotics approach, Robotics and ComputerIntegrated Manufacturing, Vol. 43, pp. 155163, 2017.##[8] G. F. Rossano, C. Martinez, M. Hedelind, S. Murphy, T. A. Fuhlbrigge, Easy robot programming concepts: An industrial perspective, in Proceeding of, IEEE, pp. 11191126.##[9] ISO, 102181: 2011  Robots and Robotic Devices  Safety Requirements for Industrial Robots  Part 1: Robot Systems and Integration, International Organization for Standardization, 2011.##[10] ISO, 102182: 2011  Robots and Robotic Devices  Safety Requirements for Industrial Robots  Part 2: Robot Systems and Integration, International Organization for Standardization, 2011.##[11] D. Massa, M. Callegari, C. Cristalli, Manual guidance for industrial robot programming, Industrial Robot: An International Journal, Vol. 42, No. 5, pp. 457465, 2015.##[12] A. Brunete, C. Mateo, E. Gambao, M. Hernando, J. Koskinen, J. M. Ahola, T. Seppälä, T. Heikkila, Userfriendly task level programming based on an online walkthrough teaching approach, Industrial Robot: An International Journal, Vol. 43, No. 2, pp. 153163, 2016.##[13] G. B. Rodamilans, G. B. Rodamilans, E. Villani, E. Villani, L. G. Trabasso, L. G. Trabasso, W. R. d. Oliveira, W. R. d. Oliveira, R. Suterio, R. Suterio, A comparison of industrial robots interface: force guidance system and teach pendant operation, Industrial Robot: An International Journal, Vol. 43, No. 5, pp. 552562, 2016.##[14] A. Winkler, J. Suchý, Forceguided motions of a 6dof industrial robot with a joint space approach, Advanced Robotics, Vol. 20, No. 9, pp. 10671084, 2006.##[15] C. H. Park, J. H. Kyung, D. I. Park, K. T. Park, D. H. Kim, D. G. Gweon, Direct teaching algorithm for a manipulator in a constraint condition using the teaching force shaping method, Advanced Robotics, Vol. 24, No. 89, pp. 13651384, 2010.##[16] P. Kormushev, S. Calinon, D. G. Caldwell, Imitation learning of positional and force skills demonstrated via kinesthetic teaching and haptic input, Advanced Robotics, Vol. 25, No. 5, pp. 581603, 2011.##[17] H.C. Song, Y.L. Kim, J.B. Song, Guidance algorithm for complexshape peginhole strategy based on geometrical information and force control, Advanced Robotics, Vol. 30, No. 8, pp. 552563, 2016.##[18] A. M. Mohammadi, A. Akbarzadeh, A novel realtime singularity avoidance approach for manual guidance of industrial robots, Modares Mechanical Engineering, Vol. 16, No. 9, pp. 403413, 2016.##[19] A. M. Mohammadi, A. Akbarzadeh, A new online singularity avoidance approach for manual guidance of industrial robots using variable impedance control, Modares Mechanical Engineering, Vol. 16, No. 11, pp. 311322, 2016.##[20] A. Mousavi Mohammadi, A. Akbarzadeh, A realtime impedancebased singularity and jointlimits avoidance approach for manual guidance of industrial robots, Advanced Robotics, Vol. 31, No. 18, pp. 10161028, 2017.##[21] M. Abderrahmane, A. Djuric, W. Chen, C. Yeh, Study and validation of singularities for a Fanuc LR Mate 200iC robot, in Proceeding of, IEEE, pp. 432437.##[22] M. H. Ang, L. Wei, L. S. Yong, An industrial application of control of dynamic behavior of robotsa walkthrough programmed welding robot, in Proceeding of, IEEE, pp. 23522357.##[23] G. Grunwald, G. Schreiber, A. AlbuSchaffer, G. Hirzinger, Programming by touch: The different way of humanrobot interaction, IEEE Transactions on Industrial Electronics, Vol. 50, No. 4, pp. 659666, 2003.##[24] G. Ferretti, G. Magnani, P. Rocco, Assigning virtual tool dynamics to an industrial robot through an admittance controller, International Conference on Advanced Robotics (ICAR), pp. 16, Munich, 2009.##[25] A. M. Mohammadi, A. Akbarzadeh, E. Adel, Trajectory generation for industrial robots using impedance control, The 24th Annual International Conference on Mechanical Engineering (ISME2016), 2016.##[26] A. Mousavi, A. Akbarzadeh, M. Shariatee, S. Alimardani, Design and construction of a linearrotary joint for robotics applications, The Third International Conference on Robotics and Mechatronics (ICRoM), pp. 229233, Tehran, 2015.##[27] A. Mousavi, A. Akbarzadeh, M. Shariatee, S. Alimardani, Repeatability analysis of a SCARA robot with planetary gearbox, The Third International Conference on Robotics and Mechatronics (ICRoM), pp. 640644, Tehran, 2015.##[28] M. Shariatee, A. Akbarzadeh, A. Mousavi, S. Alimardani, Design of an economical SCARA robot for industrial applications, The Second International Conference on Robotics and Mechatronics (ICRoM), pp. 534539, Tehran, 2014.##[29] L. A. Zadeh, Fuzzy sets, Information and control, Vol. 8, No. 3, pp. 338353, 1965.##[30] L.X. Wang, A course in fuzzy systems: PrenticeHall press, USA, 1999.##[31] N. Yagiz, Y. Hacioglu, Robust control of a spatial robot using fuzzy sliding modes, Mathematical and Computer Modelling, Vol. 49, No. 1, pp. 114127, 2009.##[32] A. M. Mohammadi, A. Akbarzadeh, I. Kardan, A new mapping method for joint and Cartesian stiffness, damping and mass matrices for large displacement in impedance control, Modares Mechanical Engineering, Vol. 17, No. 1, pp. 117128, 2017.##[33] J. M. Dolan, M. B. Friedman, M. L. Nagurka, Dynamic and loaded impedance components in the maintenance of human arm posture, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 3, pp. 698709, 1993. ##]
1

Designing an Optimal Stable Algorithm for Robot Swarm Motion toward a Target
http://ijr.kntu.ac.ir/article_90486.html
1
In this paper, an optimal stable algorithm is presented for members of a robots swarm moving toward a target. Equations of motion of the swarm are based on Lagrangian energy equations. Regarding of similar research On the design of swarm motion algorithm, an equation of motion considered constraints to guarantee no collision between the members and the members and obstacles along the motion path is presented. In order to optimize the swarm motion stability algorithm, the required constraints are introduced into the equations of motion in the form of a potential function. Considering Points of various coordinates on a target and applied potential function, each member is guided to the closest point using their coordinates while communicating with others from the beginning of the swarm motion. As a result, the need for having the members gathered within an area close to the center of the swarm observed in previously designed algorithms is eliminated. The designed optimal stability algorithm is simulated in MATLAB Software for a swarm composed of two robots under different sets of conditions. Simulation results of swarm member behavior were indicative of reducing mission time with increasing motion space for the swarm members while optimizing the behavior of the swarm moving toward the target. Finally, some experimental results related to designed algorithm are presented.
0

27
34


AliReza
Khodayari
department of Mechanical Engineering, Pardis branch, Islamic Azad University, Tehran
Iran
arkhodayari@yahoo.com


houri
khodayari
department of aerospace engineering, science and research branch, Azad university, Tehran, Iran
Iran
hourikhodayari@yahoo.com


Farshad
Pazooki
faculty of Aerospace engineering, Science and Research Branch, Islamic Azad University, Tehran
Iran
pazooki_fa@srbiau.ac.ir
Swarm
Robot Swarm
Optimal Stability Algorithm
Simulation
[[1] G. Flake, The Computational Beauty of Natur, Cambridge University, MIT Press, (1999).##[2] S. Kazadi, Swarm engineering, Ph.D. thesis, California Institute of Technology, (2000).##[3] G. Beni, J. Wang, Swarm Intelligence in Cellular Robotics Systems. NATO Advanced Workshop on Robots and Biological System, (1989).##[4] C.W. Reynolds, Flocks, herds, and schools: A distributed behavioural model, Comp, ACM SIGGRAPH computer graphics, California (1987).## [5] A. Kushleyev, D. Mellinger, V. Kumar, Towards A Swarm of Agile Micro Quadrotor, GRASP Lab, University of Pennsylvania, USA, (2013).##[6] A.M. Naghsh, A. Tanoto, Analysis and design of humanrobot swarm interaction in firefighting, The 17th IEEE International Symposium on Robot and Human Interactive Communication, (2008).##[7] V. Gazi, Stability Analysis of Swarms, PhD thesis, Ohio State University, USA, (2002).##[8] A .Mong, S. Loizou, Stabilization of Multiple Robots on Stable Orbits via Local Sensing. IEEE International Conference on Robotics and Automation, (2007).##[9] H. Hashimoto, S. Aso, S. Yokota, A. Sasaki, Cooperative Movement of Human and Swarm Robot Maintaining Stability of Swarm. The 17th IEEE International Symposium , (2008).##[10] V. Gazi. On Lagrangian Dynamics Based Modelling of Swarm Behaviour. Department of Electrical and Electronics Engineering, Istanbul Kemerburgaz University, Turkey, (2013).##[11] A. Ghafari, A. Khodayari, A. Poormahmoodi, Providing an algorithm based on unwillingness to accumulate in the twodimensional movements of Swarm robots, 24th Annual International Conference on Mechanical Engineering, Iran, (2016).##[12] A. Poormahmoodi, A. Ghaffari, A. Khodayari, Stability pattern of Movements for Swarm Robots. M.Cs thesis, South Tehran Branch, Islamic Azad University, Iran, (2016).##[13] Z. Chen, H. Liao, T. Chu, Aggregation and Splitting in SelfDriven Swarms. Phisica A, Elsevier, (2012).##[14] M. Brambilla, E. Frante, M. Birattari, Swarm Robotics: a Review From the Swarm Engineering Prespective. Springer. Swarm Intell, (2013).##[15] S. Chui, X. Wang, J. Geng, Intelligent Swarm Analysis on Aggregation Based Optimal Fuzzy Controller. Controll and Decision Conference , IEEE, China, (2008).##[16] J. Rothermich, I. Ecemiş, P. Gaudiano, Distributed Localization and Mapping with a Robotic Swarm. Springer Berlin Heidelberg, Germany, (2004).##[17] V. Kumar, F. Sahin, Cognitive Maps in Swarm Robots for the Mine Detection Application. Systems, Man and Cybernetics, IEEE, (2003).##[18] J. Kim, J. Wook, J. Seo, Mapping and Path Planning Using Communication graph of Unlocalized and Randomly Deployed Robotic Swarm. Control, Automation and Systems (ICCAS), (2016).##[19] A. Khodayari, A. Ghafari, H. Khodayari, Design, construction and validation of a quadrotor with the aim of using as a swarm member, 25th Conference of Mechanical Engineering, Tarbiat Modarres University, Iran, (2017).##[20] H. Khodayari, Designing of an Optimal Fuzzy Controller Using Linear Quadratic Regulator Method for a QuadRotor. MSc thesis, Department of aerospace engineering, Science and Research branch, Azad University, Iran, (2013).##[21] F. Pazooki, H. Khodayari, Attitude stability optimization of a quadrotor with a fuzzy controller. 13th conference of aerospace community of Iran, Tehran University, Iran, (2014).##[22] H. Khodayari, F. Pazooki, Designing an optimal fuzzy controller with LQR method for controlling the attitude of quadrotor. The International Society of Mechanical Engineering Conference (ISME), Iran, (2014).##]
1

New Adaptive UKF Algorithm to Improve the Accuracy of SLAM
http://ijr.kntu.ac.ir/article_90494.html
1
SLAM (Simultaneous Localization and Mapping) is a fundamental problem when an autonomous mobile robot explores an unknown environment by constructing/updating the environment map and localizing itself in this built map. The allimportant problem of SLAM is revisited in this paper and a solution based on Adaptive Unscented Kalman Filter (AUKF) is presented. We will explain the detailed algorithm and demonstrate that the estimation error is significantly reduced and the accuracy of thenavigation is improved. A comparison among AUKF, Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF) algorithms is investigated through simulated as well as experimental dataset. An indoor dataset is generated from a twowheel differential mobile robot in order to validate the robustness of AUKFSLAM to noise of modeling and observation, and to examine the applicability of the method for realtime navigation. Both experimental and simulation results illustrate that AUKFSLAM is more accurate than the standard UKFSLAM and the EKFSLAM. Finally, the wellknown Victoria Park dataset is used to prove the applicability of the AUKF algorithm in largescale environments.
0

35
46


Mohammad
Bozorg
Yazd University
Iran
bozorg@yazd.ac.ir


Masoud
Bahraini
Department of Mech. Eng.,
Yazd University
Iran
masoudsotoodeh@yahoo.com


Ahmad
Rad
School of Mechatronic Systems Engineering,
Simon Fraser University
25013450 102 Avenue, Surrey, BC, V3T 0A3
Canada
Iran
ahmad_rad@sfu.ca
SLAM
Adaptive UKF
Scaling Parameter
Mobile robots
[[1] J. E. Guivant and E. M. Nebot, "Optimization of the simultaneous localization and mapbuilding algorithm for realtime implementation," Robotics and Automation, IEEE Transactions on, vol. 17, pp. 242257, 2001.##[2] G. P. Huang, A. I. Mourikis, and S. I. Roumeliotis, "Analysis and improvement of the consistency of extended Kalman filter based SLAM," in Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on, 2008, pp. 473479.##[3] M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit, "FastSLAM: A factored solution to the simultaneous localization and mapping problem," in AAAI/IAAI, 2002, pp. 593598.##[4] V. Elvira, J. Míguez, and P. M. Djurić, "Adapting the number of particles in sequential monte carlo methods through an online scheme for convergence assessment," IEEE Transactions on Signal Processing, vol. 65, pp. 17811794, 2017.##[5] D. Simon, Optimal state estimation: Kalman, H infinity, and nonlinear approaches: John Wiley & Sons, 2006.##[6] R. MartinezCantin and J. Castellanos, "Unscented SLAM for largescale outdoor environments," in Intelligent Robots and Systems, IEEE/RSJ International Conference on, 2005, pp. 34273432.##[7] N. Sunderhauf, S. Lange, and P. Protzel, "Using the unscented kalman filter in monoSLAM with inverse depth parametrization for autonomous airship control," in Safety, Security and Rescue Robotics, 2007. SSRR 2007. IEEE International Workshop on, 2007, pp. 16.##[8] G. P. Huang, A. Mourikis, and S. Roumeliotis, "On the complexity and consistency of UKFbased SLAM," in Robotics and Automation, IEEE International Conference on, 2009, pp. 44014408.##[9] G. Shao, L. Wan, and X. D. Shen, "Hierarchical map building based UKFSLAM approach for AUV," in Applied Mechanics and Materials, 2013, pp. 793797.##[10] M. Wu and Y. Weng, "UKFSLAM based gravity gradient aided navigation," in Intelligent Robotics and Applications, ed: Springer, 2014, pp. 7788.##[11] S. Maeyama, Y. Takahashi, and K. Watanabe, "A solution to SLAM problems by simultaneous estimation of kinematic parameters including sensor mounting offset with an augmented UKF," Advanced Robotics, vol. 29, pp. 11371149, 2015.##[12] T. S. Ho, Y. C. Fai, and E. S. L. Ming, "Simultaneous localization and mapping survey based on filtering techniques," in Control Conference, 10th Asian, 2015, pp. 16.##[13] C. Cadena, L. Carlone, H. Carrillo, Y. Latif, D. Scaramuzza, J. Neira, et al., "Past, present, and future of simultaneous localization and mapping: toward the robustperception age," IEEE Transactions on Robotics, vol. 32, pp. 13091332, 2016.##[14] N. H. Khan and A. Adnan, "Egomotion estimation concepts, algorithms and challenges: an overview," Multimedia Tools and Applications, pp. 123, 2016.##[15] M. S. Bahraini, M. Bozorg, and A. B. Rad, "SLAM in dynamic environments via MLRANSAC," Mechatronics, vol. 49, pp. 105118, 2018.##[16] M. Cugliari and F. Martinelli, "A FastSLAM algorithm based on the Unscented Filtering with adaptive selective resampling," in Field and Service Robotics, 2008, pp. 359368.##[17] J. Qi, D. Song, C. Wu, J. Han, and T. Wang, "KFbased adaptive UKF algorithm and its application for rotorcraft UAV actuator failure estimation," Int J Adv Robotic Sy, vol. 9, 2012.##[18] H. Wang, G. Fu, J. Li, Z. Yan, and X. Bian, "An adaptive UKF based SLAM method for unmanned underwater vehicle," Mathematical Problems in Engineering, vol. 2013, 2013.##[19] Z.l. Wang, S. Qin, and Y.m. Liang, "Adaptive UKFSLAM Algorithm Based on Noise Scaling," Computer Engineering, vol. 10, p. 029, 2014.##[20] M. Wu and J. Yao, "Adaptive UKFSLAM Based on Magnetic Gradient Inversion Method for Underwater Navigation," in Intelligent Robotics and Applications, ed: Springer, 2015, pp. 237247.##[21] J. Dunik, M. Simandl, and O. Straka, "Unscented Kalman filter: aspects and adaptive setting of scaling parameter," Automatic Control, IEEE Transactions on, vol. 57, pp. 24112416, 2012.##[22] L. A. Scardua and J. J. da Cruz, "Adaptively tuning the scaling parameter of the unscented kalman filter," in Proceedings of the 11th Portuguese Conference on Automatic Control, 2015, pp. 429438.##[23] O. Straka, J. Dunik, M. Simandl, and E. Blasch, "Comparison of adaptive and randomized unscented Kalman filter algorithms," in Information Fusion, 17th International Conference on, 2014, pp. 18.##[24] O. Straka, J. Dunik, and M. Simandl, "Unscented Kalman filter with advanced adaptation of scaling parameter," Automatica, vol. 50, pp. 26572664, 2014.##[25] J. Guivant, J. Nieto, and E. Nebot, "Victoria park dataset," ed, 2012.##[26] S. J. Julier and J. K. Uhlmann, "Unscented filtering and nonlinear estimation," Proceedings of the IEEE, vol. 92, pp. 401422, 2004.##[27] S. Julier, J. Uhlmann, and H. F. DurrantWhyte, "Technical Notes and Correspondence_," IEEE Transactions on automatic control, vol. 45, p. 477, 2000.##[28] O. Straka, J. Dunik, and M. Simandl, "Scaling parameter in unscented transform: Analysis and specification," in American Control Conference (ACC), 2012, 2012, pp. 55505555.##[29] T. Bailey, "Mobile robot localisation and mapping in extensive outdoor environments," Diss. The University of Sydney, 2002.##[30] T. Bailey, J. Nieto, J. Guivant, M. Stevens, and E. Nebot, "Consistency of the EKFSLAM algorithm," in Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on, 2006, pp. 35623568.##[31] Y. Tu, Z. Huang, X. Zhang, W. Yu, Y. Xu, and B. Chen, "The Mobile Robot SLAM Based on Depth and Visual Sensing in Structured Environment," in Robot Intelligence Technology and Applications 3, ed: Springer, 2015, pp. 343357##]
1

NonSingular Terminal Sliding Mode Control of a Nonholonomic Wheeled Mobile Robots Using Fuzzy Based Tyre Force Estimator
http://ijr.kntu.ac.ir/article_90491.html
1
This paper, proposes a methodology to implement a suitable nonsingular terminal sliding mode controller associated with the output feedback control to achieve a successful trajectory tracking of a nonholonomic wheeled mobile robot in presence of longitudinal and lateral slip accompanied. This implementation offers a relatively faster and high precision tracking performance. We investigate this approach and demonstrate its feasibility for such situations where robustness against perturbation and measurement errors are required. In this study, tyreforces are considered as perturbation. These forces appear because of wheel slip of the wheeled mobile robot moving at high speed or on a slippery surface. The need to compensate these forces are achieved through a design of an intelligent estimation paradigm. The estimator is realized by a fuzzy logic model that requires slip angle and slip ratio as inputs. The weight of the robot mechanical structure is an important parameter in this design. In fact, it is used to adjust the gain of the output, resulting in a fuzzy estimator that synthesizes the magic formula for a large model of tyres. Simulation results are reported and discussed.
0

47
62


Foudil
Abdessemed
Departement of Electronics, Faculty of Technology, Batna University, Batna
Iran
fodil_a@hotmail.com
WMR
Slip Fuzzy estimator TSMC Tyre forces
[[1] B. d’AndreaNovel, G. Bastin, and G. Campion. “Modeling and control of non holonomic wheeled mobile robots”, In Proceedings of 1991 IEEE Int. Conf. on Robotics and Automation, pp.11301135, Sacramento, CA, April 1991##[2] J. P. Laumond, S. Sekhavat and F. Lamiraux, “Guidelines in nonholonomic motion planning for mobile robots”, (pp. 153), Springer Berlin Heidelberg, 1998.##[3] Y. Zhuang, Y. Liu,W. Wang, Z. Zhan, “Hybrid path planning for nonholonomic mobile robot based on steering control and improved distance propagating”, Int. Conf. on Modeling, Identification and Control (ICMIC), pp. 704 – 709, Okayama, Japan, 2010##[4] Frazzoli, Emilio, Munther A. Dahleh, and Eric Feron. “Realtime motion planning for agile autonomous vehicles”, Journal of Guidance, Control, and Dynamics 25.1, pp. 116129, 2002.##[5] C. Samson and K. AitAbdcrrahim. “Feedback stabilization of a nonholonomic wheeled mobile robot”, In Proc. 8 of the Int. Conf. on Intelligent Robots and Systems (IROS), 1991.##[6] A. Isidori, Nonlinear Control Systems, Springer, 3rd Ed. 1995.##[7] N. Sarkar, X. Yun, and V. Kumar, “Dynamic Path Following A New Control Algorithm for mobile Robots”, Proc. of the 32nd IEEE Conf. on Decision and Control, 3, 26702675, San Antonio, Texas, Dec. 1993.##[8] X. Yun and Y.Yamamoto, “Internal dynamics of a wheeled mobile robot”, Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS, Vol.2, pp. 12881294, 1993.##[9] V. Utkin, “Variable structure systems with sliding modes”, IEEE Trans. on Automatic Control, Vol. 22, issue 2, pp. 212222, 1977.##[10] H. R. Ramirez, Variable structure control of nonlinear systems, Int. J. System Sci., Vol.18, no. 9, pp. 16731689, 1987.##[11] H.S. Shim, J. H. Kim; K. Koh, “Variable structure control of nonholonomic wheeled mobile robot”, Proc. of the IEEE Int. Conf. on Robotics and Automation, Vol. 2 pp. 16941699, 1995.##[12] Y. Wu, X. Yu, Z. Man, “Terminal sliding mode control design for uncertain dynamic systems”, Systems & Control Letters 34, pp. 281287, 1998##[13] M. Zhihong, A. P. Paplinsky and H. R. Wu, A robust MIMO terminal sliding mode control scheme of rigid manipulators, Automatica, Vol. 38, Issue 12, pp. 2159=2167, Dec. 2002.##[14] S. Yua, X. Yub, B. Shirinzadehc and Z. Mand, “Continuous finite time control for robotic manipulators with terminal sliding mode”, Automatica, Vol.41, no. 11, pp.19571964, nov. 2005.##[15] C. L. Chen, C. W. Chang and H. T. Yau, “Terminal sliding mode control for aeroelastic systems”, Jour. of Nonlinear Dynamic, Springer, Vo. 38, no. 12, pp. 20152026, Nov. 2012.##[16] Y. Feng, X. Yu, Z. Man, “Nonsingular terminal sliding mode control of rigid manipulators”, Automatica, Vol. 38, Issue 12, pp. 2159–2167, Dec. 2002.##[17] S. Y. Chen and F. J. Lin, “Robust nonsingular terminal slidingmode control for nonlinear magnetic bearing system,” IEEE Trans. Control Syst. Technol., vol. 19, no. 3, pp. 636–643, May 2011.##[18] Y. Feng, X. Yu, F. Han, “On nonsingular terminal slidingmode control of nonlinear systems”, Automatica, Vol. 49, Issue 6, pp. 1715–1722, June 2013.##[19] T. Binazadeha and M.H. Shafieia, “Nonsingular terminal slidingmode control of a tractor–trailer system”, Systems Science & Control Engineering: Taylor & Francis, Vol. 2, pp. 168–174, 2014.##[20] D. Zhao, S. Li, Q. Zhu, “ Output feedback terminal sliding mode control for a class of a second order nonlinear systems”, Asian Journal of control, Wiley Online Library, Vol. 15, No. 1, pp. 111, Jan. 2013.##[21] S. Ding, W. X. Zheng, “Nonsingular terminal sliding mode control of nonlinear secondorder systems with input saturation ”, Int. J. Robust Nonlinear Control, 26: 18571872, 2016.##[22] P. S. Londhe, D. D. Dhadekar, B. M. Patre, L. M. Waghmare, “Nonsingular Terminal sliding mode control for robust trajectory tracking control of an autonomous underwater vehicle”, IEEE Indian Control Conference (ICC), 46 Jan. 2017, Guwahati, India.##[23] R. Balakrishna and A. Ghosal, “Modeling of slip for wheeled mobile robots”, IEEE transactions on Robotics and Automation, 11(1):126132, 1995.##[24] L. Garcia and J. Tornero, “Kinematic modeling of wheeled mobile robots with slip”, Advanced Robotics, Taylor & Francis, V. 21, No. 11, pp. 12531279, 2007##[25] N. Sidek and N. Sarkar, “Dynamic modeling and control of nonholonomic wheeled mobile robot subjected to wheel slip”, PhD thesis, Vanderbilt University, 2008.##[26] Y. Tian and N. Sarkar, “Formation control of mobile robots subjected to wheel slip”, In IEEE International Conference on Robotics and Automation (ICRA), pp. 45534558, 2012.##[27] Y. Tian, N. Sarkar, “Control of a mobile robot subject to wheel slip”, J. Intell. Robot Syst., Springer, V. 74, issue 34, pp.915929, June 2014.##[28] R. Jayachandran, S. D. Ashok, S. Narayanan, “Fuzzy logic based modeling and simulation approach for the estimation of tyre forces”, Int. conf. on design and Manufacturing, (IConDM), Procedia Engineering 64, pp. 11091118, 2013.##[29] L. Li and F. Y. Wang, “Advanced motion control and sensing for intelligent vehicles,” Springer Science & Business Media, 2007.##[30] E. Bakker, L. Nyborg and H. B. Pacejka, “Tyre modeling for use in vehicle dynamics studies,” SAE technical paper No. 870421, 1987.##[31] E. Bakker, H. B. Pacejka and L. Linder, “A new tyre model with an application in vehicle dynamics studies,” SAE technical paper No. 890087, 1987.##[32] H. B. Pacejka and E. Bakker, “The magic formula tyre model: Tyremodel,” Proc. of 1st Int. Colloquium on tyre models for vehicle dynamics analysis, pp. 118, Netherlands, 1991.##[33] H. B. Pacejka and I. J. M. Besselink, “Magic formula tyre model with transient properties,” Vehicle system dynamics, 27(S1), 234249.##[34] G. Genta, “ Motor Vehicle Dynamics: Modeling and Simulation,” Word Scientific Pub Co Pte. Lmd, 1997, ISBN 9810229119.##]
1

RoboWalk: Comprehensive Augmented Dynamics Modeling and performance analysis
http://ijr.kntu.ac.ir/article_111583.html
1
Utilizing orthosis and exoskeletons has drawn a lot of attention in many applications including medical industries. These devices are used in the area of physical therapy to facilitate the patient’s exercises and as an assisting device to help the elderly carry out their daily activities. In this paper, the RoboWalk bodyweight support assist device is introduced and its performance is analyzed by studying its influence on a human model. For this purpose, the forward kinematics of the human model and the inverse kinematics of RoboWalk are introduced in the first step. The dynamics of the human and a comprehensive model for RoboWalk are then obtained using the NewtonEuler method without considering the contact forces. These forces are then included in the model using the jacobian of contact points. The obtained models are then augmented to estimate the RoboWalk joint forces and torques, and those of the human model. The Recursive Newton Euler Algorithm and ADAMS software are used to verify the modeling obtained from the nonrecursive NewtonEuler algorithm. The recursive algorithm is suitable for implementation purposes due to its low computational cost. After ensuring the accuracy of the obtained models, a control strategy is designed and implemented on RoboWalk. The performance of RoboWalk is then investigated by defining some criteria, e.g. floor reaction force and human model joint torques, before and after using RoboWalk.
0

63
72


S. Ali A.
Moosavian
K. N. Toosi University of Technology
Iran
moosavian@kntu.ac.ir


M.
Nabipour
K. N. Toosi University of Technology
Iran
Assistive exoskeleton
Bodyweight support
NewtonEuler
Humanrobot interaction