High accuracy walking motion trajectory generation profile based on 6-5-6-PSPB polynomial segment with polynomial blend

Many robots, such as humanoid robot, biped robot, and robotic exoskeleton, need human guide. Particularly, there is a strong need for devices to assist individuals who lost limb function due to illnesses or injuries. Thus, several methods of generating walking motion have been implemented in order t...

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Main Author:	Mohammed, Marwan Qaid Abdulrazzaq
Format:	Thesis
Language:	English English
Published:	2018
Subjects:	T Technology (General) TJ Mechanical engineering and machinery
Online Access:	http://eprints.utem.edu.my/id/eprint/23496/1/High%20Accuracy%20Walking%20Motion%20Trajectory%20Generation%20Profile%20Based%20On%206-5-6-PSPB%20Polynomial%20Segment%20With%20Polynomial%20Blend%20-%20Marwan%20Qaid%20Abdulrazzaq%20Mohammed%20-%2024%20Pages.pdf http://eprints.utem.edu.my/id/eprint/23496/2/High%20accuracy%20walking%20motion%20trajectory%20generation%20profile%20based%20on%206-5-6-PSPB%20polynomial%20segment%20with%20polynomial%20blend.pdf
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id	my-utem-ep.23496
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Miskon, Muhammad Fahmi
topic	T Technology (General) TJ Mechanical engineering and machinery
spellingShingle	T Technology (General) TJ Mechanical engineering and machinery Mohammed, Marwan Qaid Abdulrazzaq High accuracy walking motion trajectory generation profile based on 6-5-6-PSPB polynomial segment with polynomial blend
description	Many robots, such as humanoid robot, biped robot, and robotic exoskeleton, need human guide. Particularly, there is a strong need for devices to assist individuals who lost limb function due to illnesses or injuries. Thus, several methods of generating walking motion have been implemented in order to generate walking motion according to natural human behaviour for the exoskeleton robot system. Polynomial blend technique has implemented to generate the walking motion trajectory, where the polynomial blend refers to the combination of more than one polynomial. However, three constraints (angular position, velocity, and acceleration) have been imposed by the polynomial blend techniques where the constraint of angular jerk was neglected because involving the jerk constrain will be caused problem of the non-ideal match of kinematic constraints at via point. Based on the aforementioned problem, there are three objectives to be achieved in this project. The first objective is to investigate the trajectory profile for various kinematic constraints of walking motion condition when using polynomial equation. The second objective is to modify a technique for improving a trajectory generation method to solve the problem of non-ideal match of the kinematic constraints through via points that connects between successive segments of the human walking motion. The last objective is to validate the trajectory generation method by testing the trajectory generation methods based on simulation using SimMechanics as well as to ensure that the coefficients values of the polynomial equations are correctly obtained. In this project, 5th polynomial segment with the 6th polynomial blend (6-5-6 PSPB) trajectory is proposed that aims to reduce the error that increases because of non-ideal match between kinematic constraints at the via points of successive segments. The trajectory planning of the 6-5-6 PSPB is generated based on the stance and swing phases. Each phase is presented by one full of the 6-5-6 PSPB trajectory. In order to validate the 6-5-6 PSPB trajectory, simulation using SimMechanics is conducted to ensure that the coefficients values of the polynomial equations are correctly obtained. The result shows that the error was improved almost 0.1445 degree based on the proposed 6-5-6 PSPB compared with the 4-3-4 PSPB and 5-4-5 PSPB. Thus, the 6th -5th -6th Polynomial blend leads to impose the angular jerk kinematic constraint beside the angular position, velocity, and acceleration kinematic constraints during the whole walking motion trajectory. Minimizing the maximum jerk in joint space has a beneficial effect in terms of reducing the actuator and mechanical strain and joint wear and to limit excessive wear on the robot and the excitation of resonances so that the robot life-span is expanded.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Mohammed, Marwan Qaid Abdulrazzaq
author_facet	Mohammed, Marwan Qaid Abdulrazzaq
author_sort	Mohammed, Marwan Qaid Abdulrazzaq
title	High accuracy walking motion trajectory generation profile based on 6-5-6-PSPB polynomial segment with polynomial blend
title_short	High accuracy walking motion trajectory generation profile based on 6-5-6-PSPB polynomial segment with polynomial blend
title_full	High accuracy walking motion trajectory generation profile based on 6-5-6-PSPB polynomial segment with polynomial blend
title_fullStr	High accuracy walking motion trajectory generation profile based on 6-5-6-PSPB polynomial segment with polynomial blend
title_full_unstemmed	High accuracy walking motion trajectory generation profile based on 6-5-6-PSPB polynomial segment with polynomial blend
title_sort	high accuracy walking motion trajectory generation profile based on 6-5-6-pspb polynomial segment with polynomial blend
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty Of Electrical Engineering
publishDate	2018
url	http://eprints.utem.edu.my/id/eprint/23496/1/High%20Accuracy%20Walking%20Motion%20Trajectory%20Generation%20Profile%20Based%20On%206-5-6-PSPB%20Polynomial%20Segment%20With%20Polynomial%20Blend%20-%20Marwan%20Qaid%20Abdulrazzaq%20Mohammed%20-%2024%20Pages.pdf http://eprints.utem.edu.my/id/eprint/23496/2/High%20accuracy%20walking%20motion%20trajectory%20generation%20profile%20based%20on%206-5-6-PSPB%20polynomial%20segment%20with%20polynomial%20blend.pdf
_version_	1747834051790635008
spelling	my-utem-ep.234962022-06-14T10:32:40Z High accuracy walking motion trajectory generation profile based on 6-5-6-PSPB polynomial segment with polynomial blend 2018 Mohammed, Marwan Qaid Abdulrazzaq T Technology (General) TJ Mechanical engineering and machinery Many robots, such as humanoid robot, biped robot, and robotic exoskeleton, need human guide. Particularly, there is a strong need for devices to assist individuals who lost limb function due to illnesses or injuries. Thus, several methods of generating walking motion have been implemented in order to generate walking motion according to natural human behaviour for the exoskeleton robot system. Polynomial blend technique has implemented to generate the walking motion trajectory, where the polynomial blend refers to the combination of more than one polynomial. However, three constraints (angular position, velocity, and acceleration) have been imposed by the polynomial blend techniques where the constraint of angular jerk was neglected because involving the jerk constrain will be caused problem of the non-ideal match of kinematic constraints at via point. Based on the aforementioned problem, there are three objectives to be achieved in this project. The first objective is to investigate the trajectory profile for various kinematic constraints of walking motion condition when using polynomial equation. The second objective is to modify a technique for improving a trajectory generation method to solve the problem of non-ideal match of the kinematic constraints through via points that connects between successive segments of the human walking motion. The last objective is to validate the trajectory generation method by testing the trajectory generation methods based on simulation using SimMechanics as well as to ensure that the coefficients values of the polynomial equations are correctly obtained. In this project, 5th polynomial segment with the 6th polynomial blend (6-5-6 PSPB) trajectory is proposed that aims to reduce the error that increases because of non-ideal match between kinematic constraints at the via points of successive segments. The trajectory planning of the 6-5-6 PSPB is generated based on the stance and swing phases. Each phase is presented by one full of the 6-5-6 PSPB trajectory. In order to validate the 6-5-6 PSPB trajectory, simulation using SimMechanics is conducted to ensure that the coefficients values of the polynomial equations are correctly obtained. The result shows that the error was improved almost 0.1445 degree based on the proposed 6-5-6 PSPB compared with the 4-3-4 PSPB and 5-4-5 PSPB. Thus, the 6th -5th -6th Polynomial blend leads to impose the angular jerk kinematic constraint beside the angular position, velocity, and acceleration kinematic constraints during the whole walking motion trajectory. Minimizing the maximum jerk in joint space has a beneficial effect in terms of reducing the actuator and mechanical strain and joint wear and to limit excessive wear on the robot and the excitation of resonances so that the robot life-span is expanded. UTeM 2018 Thesis http://eprints.utem.edu.my/id/eprint/23496/ http://eprints.utem.edu.my/id/eprint/23496/1/High%20Accuracy%20Walking%20Motion%20Trajectory%20Generation%20Profile%20Based%20On%206-5-6-PSPB%20Polynomial%20Segment%20With%20Polynomial%20Blend%20-%20Marwan%20Qaid%20Abdulrazzaq%20Mohammed%20-%2024%20Pages.pdf text en public http://eprints.utem.edu.my/id/eprint/23496/2/High%20accuracy%20walking%20motion%20trajectory%20generation%20profile%20based%20on%206-5-6-PSPB%20polynomial%20segment%20with%20polynomial%20blend.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=113263 mphil masters Universiti Teknikal Malaysia Melaka Faculty Of Electrical Engineering Miskon, Muhammad Fahmi 1. Aggogeri, F., Borboni, A. and Pellegrini, N., 2016. Jerk Trajectory Planning for Assistive and Rehabilitative Mechatronic Devices. International Review of Mechanical Engineering, 10(7), pp.543–551. 2. Aguilar, I.H. and Sidobre, D., 2006. Soft-Motion and Visual Control for service robots. In: 5th International Symposium on Robotics And Automation. pp. 1–7. 3. Barre, P.J., Bearee, R., Borne, P. and Dumetz, E., 2005. Influence of a jerk controlled movement law on the vibratory behaviour of high-dynamics systems. Journal of Intelligent and Robotic Systems, 42(3), pp.275–293. 4. Béarée, R. and Olabi, A., 2013. Dissociated jerk-limited trajectory applied to time-varying vibration reduction. Robotics and Computer-Integrated Manufacturing, 29(2), pp.444–453. 5. Biagiotti, L. and Melchiorri, C., 2008. Trajectory planning for automatic machines and robots. 8th ed., Berlin: Springer Science and Business Media. 6. Bianco, C.G. Lo and Ghilardelli, F., 2017. A scaling algorithm for the generation of jerklimited trajectories in the operational space. Robotics and Computer Integrated Manufacturing, 44(3), pp.284–295.123 7. Bobovský, Z., 2014. Design of Geometrical Parameters for Walking Mechanism Leg with use of Matlab Algorithm and SimMechanics. In: International Conference on Aerospace, Robotics, Manufacturing Systems, Mechanical Engineering, Neurorehabilitation and Human Motility, Romania, 2014 .pp.164–170. 8. Boryga, M. and Graboś, A., 2009. Planning of manipulator motion trajectory with higherdegree polynomials use. Mechanism and Machine Theory, 44(7), pp.1400–1419. 9. Boscariol, P., Elettrica, I. and Elettrica, I., 2012a. Jerk-Continuous Trajectories For Cyclic Tasks. In: 2012 ASME, International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. pp. 1277–1286. American Society of Mechanical Engineers. 10. Boscariol, P., Gasparetto, A. and Vidoni, R., 2012b. Planning continuous-jerk trajectories for industrial manipulators. In: ASME, 2012 11th Biennial Conference on Engineering Systems Design and Analysis. pp. 127–136. 11. Bovi, G., Rabuffetti, M., Mazzoleni, P. and Ferrarin, M., 2011. A multiple-task gait analysis approach : Kinematic , kinetic and EMG reference data for healthy young and adult subjects. Gait & Posture, 33(1), pp.6–13. 12. Boyd, J.P., 1992. Defeating the runge phenomenon for equispaced Polynomial interpolation via tikhonov regularization. Applied Mathematics Letters, 5(6), pp.57–59.124 13. Boyd, J.P., 2010. Six strategies for defeating the Runge Phenomenon in Gaussian radial basis functions on a finite interval. Computers and Mathematics with Applications, 60(12), pp.3108–3122. 14. Brady, M., 1982. Robot Motion Planning and Control, 1st ed., Cambridge: MIT press. 15. Chen, D., Qiao, T., Tan, H., Li, M. and Zhang, Y., 2014. Solving the Problem of Runge Phenomenon by Pseudoinverse Cubic Spline. In: 2014 IEEE 17th International Conference on Computational Science and Engineering. pp. 1226–1231. 16. Chen, D., Zhang, B. and Wang, M., 2015. Cartesian Space Trajectory Planning on 7-DOF Manipulator. In: Proceedings of the 2015 IEEE Conference on Robotics and Biomimetics Zhuhai, China,. pp. 940–945. 17. Chen, G., Chan, C.K., Guo, Z. and Yu, H., 2013. A review of lower extremity assistive robotic exoskeletons in rehabilitation therapy. Critical Reviews™ in Biomedical Engineering, 41(4-5), pp.343-363. 18. Colombo, G. and Morari, M., 2004. Automatic Gait-Pattern Adaptation Algorithms for Rehabilitation With a 4-DOF Robotic Orthosis. IEEE Transactions on Robotics and Automation , 20(3), pp.574–582. 19. Cook, C.C. and Ho, C.Y., 1984. The application of spline functions to trajectory generation for computer-controlled manipulators. Digital Systems for Industrial Automation, 1(4), pp.325–333.125 20. Craig, J.J., 2004. Introduction to Robotics: Mechanics and Control, 3rd ed., New Jersey: Prentice-Hall. 21. Craig, J.J., 2005. Introduction to Robotics: Mechanics and Control, 4th ed., New Jersey: Prentice-Hall. 22. David, N., Jackie, S., Butler, L. and Lewis, R., 2006. Hole’s Human Anatomy and Physiology, 10th ed., Martin J Lange. 23. Delgado, R. and Choi, B.W., 2015. Practical high curvature path planning algorithm in joint space. IET Electronics Letters, 51(6), pp.469–471. 24. Dicharry, J., 2010. Kinematics and kinetics of gait: from lab to clinic. Clinics in Sports Medicine, 29(3), pp.347–364. 25. Ezair, B., Tassa, T. and Shiller, Z., 2014. Planning high order trajectories with general initial and final conditions and asymmetric bounds. International Journal of Robotics Research, 33(6), pp.898–916. 26. Farris, R.J., Quintero, H. a. and Goldfarb, M., 2011. Preliminary evaluation of a powered lower limb orthosis to aid walking in paraplegic individuals. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19(6), pp.652–659. 27. Fornberg, B. and Zuev, J., 2007. The Runge phenomenon and spatially variable shape parameters in RBF interpolation. Computers & Mathematics with Applications, 54(3), pp.379–398.126 28. Frisoli, A., Loconsole, C., Bartalucci, R. and Bergamasco, M., 2013. A new bounded jerk on-line trajectory planning for mimicking human movements in robot-aided neurorehabilitation. Robotics and Autonomous Systems, 61(4), pp.404–415. 29. Garrido, J. and Yu, W., 2014. Trajectory Generation in Joint Space Using Modified Hidden Markov Model. In: 2014 23rd IEEE International Symposium in Robot and Human Interactive Communication, Roman, pp. 429–434. 30. Gilchrist, L.A. and Winter, D.A., 1997. A Multisegment Computer Simulation of Normal Human Gait. IEEE transactions on rehabilitation engineering, 5(4), pp.290–299. 31. Gomes, M.A., Gonc, A.A., and Gobbo, R.G., 2011. Improving the Parameters of Neural Oscillators to Generate Joint Trajectories of an Exoskeleton for Lower Limbs. In: 2011 9th IEEE International Conference in Control and Automation (ICCA). pp. 286-291 32. Gomes, M.A., Silveira, G.L.M., and Siqueira, A.A.G., 2009. Gait-Pattern Adaptation Algorithms based on Neural Network for Lower Limbs Active Orthoses. In: IROS, 2009. IEEE/RSJ International Conference in Intelligent Robots and Systems, pp.4475–4480. 33. Gribovskaya, E. and Billard, A., 2011. Learning Non-linear Multivariate Dynamics of Motion in Robotic Manipulators. International Journal of Robotics Research, 30(1), p.80– 117.127 34. Huo, W., Mohammed, S., Moreno, J.C. and Amirat, Y., 2016. Lower limb wearable robots for assistance and rehabilitation: A state of the art. IEEE systems Journal, 10(3), pp.1068- 1081. 35. Jamshidi, N., Rostami, M., Najarian, S., Menhaj, M.B., Saadatnia, M. and Firooz, S., 2009. Modelling of human walking to optimise the function of ankle-foot orthosis in GuillanBarré patients with drop foot. Singapore Med J, 50(4), pp.412–417. 36. Jazar, R.N., 2010. Theory of applied robotics: kinematics, dynamics, and control. 2nd ed., Berlin: Springer Science and Business Media. 37. Jensen, B.T. and Niss, M.O., 2007. Modelling and Control of a Biped Robot AAU-BOT1. Aalborg, Denmark: University of Aalborg, PhD thesis 38. Jianghai, Z. and Xiaodong, Y.E., 2014. Walking pattern generation of biped robot using trajectory planning of gravity center. In: 2014 IEEE International Conference on Mechatronics and Automation (ICMA), pp.890–895. 39. Knudson, D., 2007. Fundamentals of Biomechanics, 2nd ed., Berlin: Springer Science and Business Media. 40. Kuo, A.D. and Donelan, J.M., 2010. Dynamic Principles of Gait and Their Clinical Implications. Journal of the American Physical therapy association, 90(2), pp.157–174.128 41. Kutilek, P. and Hajny, O., 2010. Study of Human Walking by SimMechanics. In: Proceedings of The 18th Technical Computing. pp. 1–10. 42. Lambrechts, P., Boerlage, M. and Steinbuch, M., 2004. Trajectory Planning and Feedforward Design for High Performance Motion Systems. In: American Control Conference, 2004. 2004 pp. 1–6. 43. Lanzutti, A., 2011. Smooth Trajectory Planning Algorithms For Industrial Robots : An Experimental Evaluation. International Journal Of Engineering, 1(9), pp.127–132. 44. Li, N., Yan, L., Qian, H., Wu, H., Wu, J. and Men, S., 2015. Review on Lower Extremity Exoskeleton Robot. Open Automation and Control Systems Journal, 7(1), pp.441–453. 45. Li, X., Tan, S., Feng, X. and Rong, H., 2009. LSPB Trajectory Planning : Design for the Modular Robot Arm Applications. In: 2009 IEEE International Conference on Information Engineering and Computer Science (ICIECS )., pp. 3–6. 46. Lin, C.S. and Chang, P.-R., 1983. Joint Trajectories of Mechanical Manipulators For Cartesian Path Approximation. IEEE Transactions on Systems, Man, and Cybernetics, 13(6), pp.1094–1102. 47. Lister, S.J., Jones, N.B., Spurgeon, S.K. and Scott, J.J.A., 2006. Simulation Of Human Gait And Associated Muscle Activation Strategies Using Sliding-Mode Control Techniques. Simulation Modelling Practice and Theory, 14(1), pp.586–596.129 48. Liu, H., Lai, X., and Wu, W., 2013. Time-optimal and jerk-continuous trajectory planning for robot manipulators with kinematic constraints. Robotics and Computer Integrated Manufacturing, 29(2), pp.309–317. 49. Lu, S., Zhao, J., Jiang, L. and Liu, H., 2017. Time-jerk optimal trajectory planning of a 7- DOF redundant robot. Turkish Journal of Electrical Engineering & Computer Sciences, 2017(25), pp.4211–4222. 50. Luu, T.P., Lim, H.B., Qu, X., Hoon, K.H. and Low, K.H., 2011. Subject-Specific Lower Limb Waveforms Planning via Artificial Neural Network. In: 2011 IEEE International Conference on Rehabilitation Robotics (ICORR).pp.1-6. 51. Macfarlane, S. and Croft, E.A., 2003. Jerk-bounded manipulator trajectory planning: design for real-time applications. IEEE Transactions on Robotics and Automation, 19(1), pp.42–52. 52. Miskon, M.F.B. and Yusof, M.B.A.J., 2014. Review of Trajectory Generation of Exoskeleton Robots. In: 2014 IEEE International Symposium on Robotics and Manufacturing Automation (ROMA). pp. 12–17. 53. Muro-de-la-Herran, A., García-Zapirain, B. and Méndez-Zorrilla, A., 2014. Gait Analysis Methods: An Overview of Wearable and Non-Wearable Systems, Highlighting Clinical Applications. Sensors, 14(2), pp.3362–3394.130 54. Park, I.W., Kim, J.Y., Lee, J. and Oh, J.H., 2006. Online Free Walking Trajectory Generation for Biped Humanoid Robot KHR-3 ( HUBO ). In: 2006 IEEE International Conference On Robotics and Automation, (ICRA). pp. 1231–1236. 55. Pendrill, A., 2016. Beyond velocity and acceleration : jerk , snap and higher derivatives. European Journal of Physics, 37(6), pp.1–11. 56. Petrinec, K. and Kovacic, Z., 2007. Trajectory planning algorithm based on the continuity of jerk. In: 2007 IEEE Mediterranean Conference on Control and Automation. pp. 1–5. 57. Piazzi, A. and Visioli, A., 2000. Global minimum-jerk trajectory planning of robot manipulators. IEEE Transactions on Industrial Electronics, 47(1), pp.140–149. 58. Raheem, F.A. and Gorial, I.I., 2013. Comparative Study between Joint Space and Cartesian Space Path Planning for Two-Link Robot Manipulator using Fuzzy Logic. IRAQI J Comput Commun Control Syst Eng, 13(2), pp.1–10. 59. Rai, J.K., Tewari, R.P., and Chandra, D., 2009. Trajectory planning for all sub phases of gait cycle for human-like walking. International Journal of Engineering Systems Modelling and Simulation, 1(4), pp.206–210. 60. Rai, J.K. and Tewari, R., 2014. Quintic Polynomial Trajectory Of Biped Robot For Human-Like Walking. In: (ISCCSP), 2014 6th IEEE International Symposium on Communications, Control and Signal Processing. pp. 2325–2328.131 61. Ranky, P.G. and Ho, C.Y., 1985. Robot Modeling: Control and Applications with Software, UK: Springer-Verlag, IFS (Publications) Lts. 62. Regan, J., Andrew, R. and Cinnamon Van, P., 2015. Seeley’s Essentials of Anatomy and Physiology, 9th ed., New york: McGraw-Hill. 63. Ruixiang, Z., 2004. The Development and Walking Control of Biped Robot, Singapore: National University of Singapore (NUS),Master thesis. 64. Sahu, P.K., 2016. Optimal Trajectory Planning of Industrial Robots using Geodesic. International Journal of Robotics and Automation (IJRA), 5(3), pp.190–198. 65. Salleh, W.M.S.B.W., 2015. Development Of Knee Joint Angle Trajectory Tracking For Leg Support Mechanism,. Johor, Malaysia: Universiti Tun Hussein Onn Malaysia (UTHM), Doctoral dissertation. 66. Schwartz, M.H., Rozumalski, A., and Trost, J.P., 2008. The effect of walking speed on the gait of typically developing children. Journal of biomechanics , 41(8), pp.1639–1650. 67. Seo, K.H., Park, Y., Yun, S., Park, S., Jun, J. and Jeon, K.W., 2013. Gait Pattern Generation for Gait Rehabilitation. In: 2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI). pp. 364–367. 68. Siciliano, B., Sciavicco, L., Villani, L. and Oriolo, G., 2009. Robotics:Modelling, Planning, and Control, 1st ed., Verlag London, Springer.132 69. Sidobre, D. and He, W., 2012. Online task space trajectory generation. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),Vilamoura, Algarve, Portugal,. pp. 121–128. 70. Silva, M., Barbosa, R. and Castro, T., 2013. Multi-Legged Walking Robot Modelling in MATLAB / Simmechanics Simulation. In: 2013 8th EUROSIM Confernce on Modelling and Simulation. pp. 226–231. 71. Singla, A. and Singh, G., 2016. Matlab / simMechanics based control of four-bar passive lower-body mechanism for rehabilitation. Perspectives in Science, 8(2016), pp.351–354. 72. Siqueira, A.A., Jardim, B., Vilela, P.R. and Winter, T.F., 2008. Analysis of Gait-Pattern Adaptation Algorithms Applied in an Exoskeleton for Lower Limbs. In: 2008 IEEE 16th Mediterranean Conference on Control and Automation , pp.920–925. 73. Skelly, M. and Chizeck, H., 2002. Simulation of Bipedal Walking. In: proceedings of IASTED International Conference on Applied Modeling and Simulation, pp.4-6. 74. Taha, Z., Majeed, A.P.P.A., Tze, M.Y.W.P. and Rahman, A.G.A., 2015. Preliminary Investigation on the Development of a Lower Extremity Exoskeleton for Gait 75. Rehabilitation : A Clinical Consideration. Journal of Medical and Bioengineering , 4(1), pp.1–6. 76. Tang, S.H., Khaksar, W., Ismail, N.B. and Ariffin, M.K.A., 2012. A Review on Robot Motion Planning Approaches. Pertanika Journal of Science and Technology, 20(1), pp15–29.133 77. Tsai, C.S., Hsieh, C.H., Qiu, W. and Qi, H., 2016. Gait Analysis of a Little Biped Robot. ICIC Express Letters, Part B: Applications, International Journal of Research and Surveys, 7(5), pp.1–6. 78. Tsukahara, A., Hasegawa, Y. and Sankai, Y., 2011. Gait Support for Complete Spinal Cord Injury Patient by Synchronized Leg-Swing with HAL. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 1737–1742. 79. Vakanski, A., Mantegh, I., Irish, A. and Janabi-Sharifi, F., 2012. Trajectory learning for robot programming by demonstration Using Hidden Markov Model and Dynamic Time 80. Warping. IEEE Transactions On Systems, Man, and Cybernetics, Part B (Cybernetics), 42(4), pp.1039–1052. 81. Vallery, H., Van Asseldonk, E.H., Buss, M. and Van Der Kooij, H., 2009. Reference Trajectory Generation for Rehabilitation Robots : Complementary Limb Motion Estimation., IEEE transactions on neural systems and rehabilitation engineering, 17(1), pp.23–30. 82. Vaughan, C.L., Davis, B.L. and Jeremy, C.O., 1992. Dynamics of Human Gait, 2nd ed., Cape: Kiboho. 83. Villarreal, D.J. and Gregg, R.D., 2014. A Survey of Phase Variable Candidates of Human Locomotion. In: IEEE 36th Annual International Conference On Engineering in Medicine and Biology Society (EMBC). pp. 4017–4021.134 84. Wang, Q., Qian, J., Zhang, Y., Shen, L., Zhang, Z. and Feng, Z., 2007. Gait Trajectory Planning and Simulation for the Powered Gait Orthosis. In: IEEE International Conference On Robotics and Biomimetics (ROBIO). pp. 1693–1697. 85. Wang, Z., Mülling, K., Deisenroth, M. P., Ben Amor, H., Vogt, D., Schölkopf, B., and Peters, J., 2013. Probabilistic Movement Modeling for Intention Inference in HumanRobot Interaction. The International Journal of Robotics Research, 32(7), pp.841–858. 86. Westervelt, E.R., Chevallereau, C., Choi, J.H., Morris, B. and Grizzle, J.W., 2007. Feedback Control of Dynamic Bipedal Robot Locomotion, 1st , Florida: CRC press. 87. Williams, R.L., 2013. Simplified robotics joint-space trajectory generation with a via point using a single polynomial. Journal of Robotics, pp.1-6. 88. Wiltsche, A., 2005. Blending curves. Journal for Geometry and Graphics, 9(1), pp.67–75. 89. Wojtyra, M., 2000. Dynamical analysis of human walking. In: 15th European ADAMS user conference,Rome, Italy,2000. pp. 1–15. 90. Wu, W., Zhu, S. and Liu, S., 2009. Smooth Joint Trajectory Planning for Humanoid Robots Based on B-Splines. In: Proceedings of the 2009 IEEE International Conference on Robotics and Biomimetics, Guilin, China, 19-23 December 2009. pp. 475–479. 91. Xu, Y. and Lee, K.K.C., 2005. Human Behavior Learning and Transfer 1st ed., Florida: CRC Press.135 92. Yu, Z., Chen, X., Huang, Q., Zhang, W., Meng, L., Zhang, W. and Gao, J., 2016. Gait Planning of Omnidirectional Walk on Inclined Ground for Biped Robots. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46(7), pp.888–897. 93. Zhang, B., Jiang, S., Yan, K. and Wei, D., 2011. Human Walking Analysis , Evaluation and Classification Based on Motion Capture System. In: InTech, proceedings of Health Management-Different Approaches and Solutions. 2011 94. Zhang, J., Shen, L., Shen, L. and Li, A., 2010. Gait Analysis of Powered Bionic Lower Prosthesis. In: 2010 IEEE International Conference On Robotics and Biomimetics (ROBIO). pp. 25–29. 95. Zhao, R., 2016. Trajectory planning and control for robot manipulations. Toulouse, France: Paul Sabatier University, Doctoral dissertation. 96. Zhao, X., Wang, M., Liu, N. and Tang, Y., 2017. Trajectory Planning for 6-DOF Robotic Arm Based on Quintic Polynormial. 2nd International Conference on Control, Automation, and Artificial Intelligence. 2017 pp. 115–118. 97. Zheng, Y. F., and Hemami, H., 1984. Impact Effects of Biped Contact with the Environment. IEEE transactions on systems, man, and cybernetics, 14(3), pp.437–443.

High accuracy walking motion trajectory generation profile based on 6-5-6-PSPB polynomial segment with polynomial blend

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