Static and dynamic path planning of humanoids using an advanced regression controller

Document Type : Article


Robotics Laboratory, Mechanical Engineering Department, National Institute of Technology, Rourkela-769008, Odisha, India


With an ability to mimic the human behaviour, humanoid robots have become a topic of major interest among research fellows dealing with robotic investigation. The current work is focussed on the design of a novel navigational controller based on the logic of the regression analysis to be used in the path planning and navigation of humanoid robots. In the current investigation, static and dynamic path planning of humanoid NAOs are encountered. The static path planning represents a single NAO navigating through random static obstacles. The dynamic path planning represents multiple humanoid NAOs navigating through random static obstacles and acting as dynamic obstacles for each other. A Petri-Net controller is designed to avoid the collision among the multiple NAOs in dynamic path planning. To reduce the path length and time travel and to provide the shortest possible path, an advanced regression controller is implemented in the NAOs in both simulation and experimental environments. Finally, a comparison has been performed between the simulation and experimental results, and a good agreement is observed between both the results with a minimal percentage of error. The proposed navigational controller is also tested against other existing navigational technologies to validate better efficiency.


Main Subjects

1.Atkinson, A.C. Robust and diagnostic regression analyses", Communications in Statistics-Theory and Methods, 11(22), pp. 2559-2571 (1982).
2. Asano, T., Asano, T., Guibas, L., Hershberger, J., and Imai, H. Visibility-polygon search and euclidean shortest paths", 26th Annual Symposium on Foundations of Computer Science, pp. 155-164 (1985).
3. Takahashi, O. and Schilling, R.J. Motion planning in a plane using generalized Voronoi diagrams", IEEE Transactions on Robotics and Automation, 5(2), pp. 143-150 (1989).
4. Hwang, Y.K. and Ahuja, N. A potential _eld approach to path planning", IEEE Transactions on Robotics and Automation, 8(1), pp. 23-32 (1992).
5. Lazaro, J.L., Gardel, A., Mataix, C., Rodriguez, F.J., and Martin, E. Adaptive workspace modeling, using regression methods, and path planning to the alternative guide of mobile robots in environments with obstacles", 7th IEEE International Conference on Emerging Technologies and Factory Automation, 1, pp. 529-534 (1999).
6. Bai, S. and Low, K.H. Terrain evaluation and its application to path planning for walking machines", Advanced Robotics, 15(7), pp. 729-748 (2001).
7. Lee, Y.J. and Bien, Z. Path planning for a quadruped robot: an artificial field approach", Advanced Robotics, 16(7), pp. 609-627 (2002).
8. Minguez, J. and Montano, L. Nearness diagram (ND) navigation: collision avoidance in troublesome scenarios", IEEE Transactions on Robotics and Automation, 20(1), pp. 45-59 (2004).
9. Benamati, L., Cosma, C., and Fiorini, P. Path planning using at potential field approach", 12th International Conference on Advanced Robotics, pp. 103-108 (2005).
10. Liang, T.C., Liu, J.S., Hung, G.T., and Chang, Y.Z. Practical and exible path planning for car-like mobile robot using maximal-curvature cubic spiral", Robotics and Autonomous Systems, 52(4), pp. 312-335 (2004).
11. Papadopoulos, E., Papadimitriou, I., and Poulakakis, I. Polynomial-based obstacle avoidance techniques for nonholonomic mobile manipulator systems", Robotics and Autonomous Systems, 51(4), pp. 229-247 (2005).
12. Masehian, E. and Sedighizadeh, D. Classic and heuristic approaches in robot motion planning-a chronological review", World Academy of Science, Engineering and Technology, 23, pp. 101-106 (2007).
13. Qi, N., Ma, B., Liu, X.E., Zhang, Z., and Ren, D. A modified artificial potential field algorithm for mobile robot path planning", 7th World Congress on Intelligent Control and Automation, pp. 2603-2607 (2008). 1
4. Jolly, K.G., Kumar, R.S., and Vijayakumar, R. A Bezier curve based path planning in a multi-agent robot soccer system without violating the acceleration limits", Robotics and Autonomous Systems, 57(1), pp. 23-33 (2009).
15. Keshmiri, S. and Payandeh, S. Multi-robots, multilocations recharging paradigm: a regression route technique", In Proceedings of the 14th IASTED International Conference, Robotics and Applications, Cambridge, MA, USA, pp. 160-165 (2009).
16. Shi, P. and Zhao, Y. Global path planning for mobile robot based on improved arti_cial potential function", IEEE International Conference on Automation and Logistics, pp. 1900-1904 (2009).
17. Singh, M.K., Parhi, D.R., and Pothal, J.K. ANFIS approach for navigation of mobile robots", International Conference on Advances in Recent Technologies in Communication and Computing, pp. 727-731 (2009).
18. Parhi, D.R. and Singh, M.K. Navigational strategies of mobile robots: a review", International Journal of Automation and Control, 3(2-3), pp. 114-134 (2009).
19. Singh, M.K. and Parhi, D.R. Path optimisation of a mobile robot using an artificial neural network controller", International Journal of Systems Science, 42(1), pp. 107-120 (2011).
20. Razzazi, M. and Sepahvand, A. Time complexity of two disjoint simple paths", Scientia Iranica, 24(3), pp. 1335-1343 (2017). 392 P.B. Kumar et al./Scientia Iranica, Transactions B: Mechanical Engineering 26 (2019) 375{393
21. Mohammadi, E., Zohoor, H., and Khadem, S.M. Design and prototype of an active assistive exoskeletal robot for rehabilitation of elbow and wrist", Scientia Iranica. Transactions B, Mechanical Engineering, 23(3), p. 998 (2016).
22. Kala, R., Shukla, A., and Tiwari, R. Dynamic environment robot path planning using hierarchical evolutionary algorithms", Cybernetics and Systems: An International Journal, 41(6), pp. 435-454 (2010).
23. Kashmiri, S. and Payandeh, S. Robot navigation controller: a non-parametric regression approach", IFAC Proceedings, 43(22), pp. 22-27 (2010).
24. Sheng, J., He, G., Guo, W., and Li, J. An improved arti_cial potential _eld algorithm for virtual human path planning", Entertainment for Education. Digital Techniques and Systems, pp. 592-601 (2010).
25. Hong, Z., Liu, Y., Zhongguo, G., and Yi, C. The dynamic path planning research for mobile robot based on arti_cial potential field", International Conference on Consumer Electronics, Communications and Networks, pp. 2736-2739 (2011). 26. Mohanty, P.K. and Parhi, D.R. Optimal path planning for a mobile robot using cuckoo search algorithm", Journal of Experimental & Theoretical Arti_cial Intelligence, 28(1-2), pp. 35-52 (2016).
27. Mohanty, P.K. and Parhi, D.R. Path planning strategy for mobile robot navigation using MANFIS controller", In Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications, pp. 353-361 (2014).
28. Parhi, D.R. and Mohanty, P.K. IWO-based adaptive neuro-fuzzy controller for mobile robot navigation in cluttered environments", The International Journal of Advanced Manufacturing Technology, 83(9-12), pp. 1607-1625 (2016).
29. Pham, D.T. and Parhi, D.R. Navigation of multiple mobile robots using a neural network and a Petri Net model", Robotica, 21(1), pp. 79-93 (2003).
30. Chen, C.Y. and Ko, C.C. An evolutionary method to vision-based self-localization for soccer robots", Scientia Iranica. Transactions B, Mechanical Engineering, 22(6), p. 2071 (2015).
31. Korayem, M.H., Maddah, S.M., Taherifar, M., and Tourajizadeh, H. Design and programming a 3D simulator and controlling graphical user interface of ICaSbot, a cable suspended robot", Scientia Iranica. Transactions B, Mechanical Engineering, 21(3), p. 663 (2014).
32. Sayyaadi, H. and Babaee, M. Control of nonholonomic mobile manipulators for cooperative object transportation", Scientia Iranica. Transaction B, Mechanical Engineering, 21(2), p. 347 (2014).
33. Ohki, T., Nagatani, K., and Yoshida, K. Local path planner for mobile robot in dynamic environment based on distance time transform method", Advanced Robotics, 26(14), pp. 1623-1647 (2012).
34. Li, G., Yamashita, A., Asama, H., and Tamura, Y. An e_cient improved arti_cial potential _eld based regression search method for robot path planning", International Conference on Mechatronics and Automation, pp. 1227-1232 (2012).
35. Hong, J. and Park, K. A new mobile robot navigation using a turning point searching algorithm with the consideration of obstacle avoidance", The International Journal of Advanced Manufacturing Technology, 52(5), pp. 763-775 (2011).
36. Keshmiri, S. and Payandeh, S. Regression analysis of multi-rendezvous recharging route in multi-robot environment", International Journal of Social Robotics, 4(1), pp. 15-27 (2012).
37. Tingbin, C. and Qisong, Z. Robot motion planning based on improved arti_cial potential _eld", 3rd International Conference on Computer Science and Network Technology, pp. 1208-1211 (2013).
38. Clever, D. and Mombaur, K.D. An inverse optimal control approach for the transfer of human walking motions in constrained environment to humanoid robots", In Robotics: Science and Systems (2016).
39. Mirjalili, R., Youse_-koma, A., Shirazi, F.A., and Mansouri, S. Online path planning for SURENA III humanoid robot using model predictive control scheme", 4th International Conference on Robotics and Mechatronics, pp. 416-421 (2016).
40. Ko_nas, N., Orfanoudakis, E., and Lagoudakis, M.G. Complete analytical inverse kinematics for NAO", 13th International Conference on Autonomous Robot Systems, pp. 1-6 (2013).
41. Peterson, J.L., Petri Net Theory and the Modeling of Systems, Prentice-Hall, Englewood cli_s (1981).
42. Qu, H., Xing, K., and Alexander, T. An improved genetic algorithm with co-evolutionary strategy for global path planning of multiple mobile robots", Neurocomputing, 120, pp. 509-517 (2013).