Intelligent navigation of a self-fabricated biped robot using a regression controller

Document Type : Article

Authors

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

Abstract

With increasing demand towards use of biped robots in industrial automation and other related applications, navigation and path planning has emerged as one of the most challenging research topic over the last few decades. In this paper, a novel navigational controller is designed and implemented in a self-fabricated biped robot. After fabricating the biped equipped with a large set of sensors, a regression controller is implemented in it for obstacle avoidance and path optimization purpose. The obstacle distances detected by the sensory network of the biped are fed as input parameters to the regression controller and the output obtained from the controller is the necessary heading angle required to avoid the obstacles present randomly in the environment. The biped is tested in a simulation environment for obstacle avoidance and target following behaviour. Along with that, to validate the simulation results, a real-time experimental set up is designed under laboratory conditions. The results obtained from both the environments are compared in terms of navigational parameters and a good agreement between them is observed. Being a relatively new area of research, the navigation of bipeds can serve as a pioneer act towards industrial automation.

Keywords

Main Subjects


  1. References:

    1. Vukobratović, M. and Borovac, B. “Zero-moment point-thirty-five years of its life”, International journal of humanoid robotics, 1(01), pp. 157-173 (2004).
    2. Pa, P.S. and Wu, C.M. “Design of a hexapod robot with a servo control and a man-machine interface”, Robotics and Computer-Integrated Manufacturing, 28(3), pp. 351-358 (2012).
    3. Liu, C., Wang, D. and Chen, Q. “Central pattern generator inspired control for adaptive walking of biped robots”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(5), pp. 1206-1215 (2013).
    4. Sahu, C., Kumar, P.B. and Parhi, D.R. “An Intelligent Path Planning Approach for Humanoid Robots Using Adaptive Particle Swarm Optimisation”, International Journal on Artificial Intelligence Tools, 27(5), pp. 1850015 (2018).
    5. Sahu, C., Parhi, D.R. and Kumar, P.B. “An Approach to Optimize the Path of Humanoids using Adaptive Ant Colony Optimization”, Journal of Bionic Engineering 15(4), pp. 623-635 (2018).
    6. Yeon, J.S. and Park, J.H. “A fast turning method for biped robots with foot slip during single-support phase”, IEEE/ASME Transactions on Mechatronics, 19(6), pp. 1847-1858 (2014).
    7. Asa, K., Ishimura, K. and Wada, M. “Behavior transition between biped and quadruped walking by using bifurcation”, Robotics and Autonomous Systems, 57(2), pp. 155-160 (2009).
    8. Iida, F., Minekawa, Y., Rummel, J. and Seyfarth, A. “Toward a human-like biped robot with compliant legs”, Robotics and Autonomous Systems, 57(2), pp. 139-144 (2009).
    9. Rath, A.K., Das, H.C., Parhi, D.R. and Kumar, P.B. “Application of artificial neural network for control and navigation of humanoid robot”, Journal of Mechanical Engineering and Sciences, 12(2), pp. 3529-3538 (2018).
    10. Rath, A.K., Parhi, D.R., Das, H.C., Muni, M.K. and Kumar, P.B. “Analysis and use of fuzzy intelligent technique for navigation of humanoid robot in obstacle prone zone”, Defence Technology. DOI: doi.org/10.1016/j.dt.2018.03.008 (2018).
    11. Rath, A.K., Parhi, D.R., Das, H.C. and Kumar, P.B. “Behaviour based navigational control of humanoid robot using genetic algorithm technique in cluttered environment”, Modelling, Measurement and Control A, 91(1), pp. 32-36 (2018).
    12. Rushdi, K., Koop, D. and Wu, C.Q. “Experimental studies on passive dynamic bipedal walking”, Robotics and Autonomous Systems, 62(4), pp. 446-455 (2014).
    13. Silva, P., Santos, C.P., Matos, V. and Costa, L. “Automatic generation of biped locomotion controllers using genetic programming”, Robotics and Autonomous Systems, 62(10), pp. 1531-1548 (2014).
    14. Kumar, A., Kumar, P.B. and Parhi, D.R. “Intelligent Navigation of Humanoids in Cluttered Environments Using Regression Analysis and Genetic Algorithm”, Arabian Journal for Science and Engineering, 1-24 (2018).
    15. Kumar, P.B., Mohapatra, S. and Parhi, D.R. “An intelligent navigation of humanoid NAO in the light of classical approach and computational intelligence”, Computer Animation and Virtual Worlds, e1858 (2018).
    16. Kumar, P.B., Pandey, K.K., Sahu, C., Chhotray, A, and Parhi, D.R. “A hybridized RA-APSO approach for humanoid navigation”, In: Nirma University International Conference on Engineering (NUiCONE), pp. 1-6 (2017).
    17. Kumar, P.B., Sahu, C. and Parhi, D.R. “A hybridized regression-adaptive ant colony optimization approach for navigation of humanoids in a cluttered environment”, Applied Soft Computing, 68, pp. 565-585 (2018).
    18. Kumar, P.B., Sahu, C., Parhi, D.R., Pandey, K.K. and Chhotray, A. “Static and Dynamic Path Planning of Humanoids using an Advanced Regression Controller”, Scientia Iranica. DOI: 10.24200/SCI.2018.5064.1071 (2018).
    19. Geng, T. “Torso inclination enables faster walking in a planar biped robot with passive ankles”, IEEE Transactions on Robotics, 30(3), pp. 753-758 (2014).
    20. Nakamura, Y., Mori, T., Sato, M.A. and Ishii, S. “Reinforcement learning for a biped robot based on a CPG-actor-critic method”, Neural Networks, 20(6), pp. 723-735 (2007).
    21. Cristiano, J., Puig, D. and Garcia, M.A. “Efficient locomotion control of biped robots on unknown sloped surfaces with central pattern generators”, Electronics Letters, 51(3), pp. 220-222 (2015).
    22. Deepak, B.B.V.L. and Parhi, D.R. “Control of an automated mobile manipulator using artificial immune system”, Journal of Experimental & Theoretical Artificial Intelligence, 28(1-2), pp. 417-439 (2016).
    23. Pandey, A. and Parhi, D.R. “Multiple mobile robots navigation and obstacle avoidance using minimum rule based ANFIS network controller in the cluttered environment”, Int J Adv Robot Automation, 1(1), pp. 1-11 (2016).
    24. Pandey, A., Sonkar, R.K., Pandey, K.K. and Parhi, D.R. “Path planning navigation of mobile robot with obstacles avoidance using fuzzy logic controller”, In: IEEE 8th International Conference on Intelligent Systems and Control (ISCO), 39-41 (2014).
    25. Kajita, S., Kanehiro, F., Kaneko, K., Fujiwara, K., Harada, K., Yokoi, K. and Hirukawa, H. “Biped walking pattern generation by using preview control of zero-moment point”, In: IEEE International Conference on Robotics and Automation (ICRA'03),2, pp. 1620-1626 (2003).
    26. Mirjalili, R., Yousefi-koma, A., Shirazi, F.A. and Mansouri, S. “Online path planning for SURENA III humanoid robot using model predictive control scheme”, In: IEEE 4th International Conference on Robotics and Mechatronics (ICROM), 416-421 (2016).
    27. Hwang, Y.K. and Ahuja, N. “A potential field approach to path planning”, IEEE Transactions on Robotics and Automation, 8(1), pp. 23-32 (1992).
    28. Atkinson, A.C. “Robust and diagnostic regression analyses”, Communications in Statistics-Theory and Methods, 11(22), pp. 2559-2571 (1982).
    29. Qi, N., Ma, B., Liu, X.E., Zhang, Z. and Ren, D. “A modified artificial potential field algorithm for mobile robot path planning”, In: 7th World Congress on Intelligent Control and Automation, (WCICA 2008), pp. 2603-2607 (2008).
    30. Asano, T., Asano, T., Guibas, L., Hershberger, J. and Imai, H. “Visibility-polygon search and euclidean shortest paths”, In IEEE 26th Annual Symposium onFoundations of Computer Science, 155-164 (1985).
    31. 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).
    32. 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).
    33. Singh, M.K., Parhi, D.R. and Pothal, J.K. “ANFIS approach for navigation of mobile robots”, In: International Conference on Advances in Recent Technologies in Communication and Computing, (ARTCom'09), pp. 727-731 (2009).
    34. 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).
    35. Parhi, D.R. and Singh, M.K. “Real-time navigational control of mobile robots using an artificial neural network”, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 223(7), pp. 1713-1725 (2009).
    36. Mohanty, P.K. and Parhi, D.R. “Optimal path planning for a mobile robot using cuckoo search algorithm”, Journal of Experimental & Theoretical Artificial Intelligence, 28(1-2), pp. 35-52 (2016).
    37. 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).
    38. Mohanty, P.K. and Parhi, D.R. “A new intelligent motion planning for mobile robot navigation using multiple adaptive neuro-fuzzy inference system”, Applied Mathematics & Information Sciences, 8(5), pp. 2527 (2014).
    39. Sanjuan, J., Serje, D. and Pacheco, J. “Closed form solution for direct and inverse kinematics of a US-RS-RPS 2-DOF parallel robot”, Scientia Iranica, 25(4), pp. 2144-2154 (2018).
    40. Korayem, M.H., Yousefzadeh, M. and Manteghi, S. “Tracking control and vibration reduction of flexible cable-suspended parallel robots using a robust input shaper”, Scientia Iranica. Transaction B, Mechanical Engineering, 25(1), pp. 230-252 (2018).