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An intelligent computer vision integrated regression based navigation approach for humanoids in a cluttered environment

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With a clear edge over the mobile robot counterparts, humanoids have become the centre of attraction for people dealing with robotics research. The enhanced use of the humanoids in industrial automation, manufacturing and other related areas has forced researchers to focus on their navigational aspects. In the current work, a computer vision integrated regression based navigational approach has been designed and implemented on a humanoid. Initially, a regression control architecture has been formulated for path planning and obstacle avoidance of a humanoid considering sensor information regarding obstacle distances as the inputs and the necessary heading angle as the output for the controller. Then, the limitations available in the regression based approach have been found out. To avoid the limitations, a computer vision based technique has been integrated with the original regression based approach. It has been observed that by the use of computer vision based technique, the robot is able to clearly distinguish between different types of obstacles, arena and target and reach the target position safely. Multiple simulations and real-time experiments have been conducted to verify the effectiveness of the proposed controller. The results obtained from both the simulation and experimental platforms have been compared against each other in terms of navigational parameters, and a good agreement between them is observed. The developed approach has also been assessed against another existing navigational technique, and a significant performance improvement has been observed. Finally, concluding remarks have been given regarding the use of both the techniques in humanoid navigation in complex environments.

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  1. Abdessemed F, Benmahammed K, Monacelli E (2004) A fuzzy-based reactive controller for a non-holonomic mobile robot. Robot Auton Syst 47(1):31–46

    Article  Google Scholar 

  2. Benamati L, Cosma C, Fiorini P (2005) Path planning using flat potential field approach. In: IEEE 12th International Conference on Advanced Robotics:103–108

  3. Bruce J, Balch T, Veloso M (2000) Fast and inexpensive color image segmentation for interactive robots. IEEE/RSJ International Conference on Intelligent Robots and Systems 3:2061–2066

    Google Scholar 

  4. Burns B, Brock O (2004) Model-based motion planning. Computer Science Department Faculty Publication Series 51

  5. Clever D, Mombaur KD (2016). An inverse optimal control approach for the transfer of human walking motions in constrained environment to humanoid robots. In Robotics: Science and Systems

  6. Dahlkamp H, Kaehler A, Stavens D, Thrun S, Bradski GR (2006) Self-supervised Monocular Road Detection in Desert Terrain. In: Robotics: science and systems l:38

  7. Davison AJ, Murray DW (1998) Mobile robot localisation using active vision. In: European Conference on Computer Vision, 809–825

  8. Deng-Peng X, Xu L (2011) Multiple balance strategies for humanoid standing control. Acta Automat Sin 37(2):228–233

    MathSciNet  Google Scholar 

  9. DeSouza GN, Kak AC (2002) Vision for mobile robot navigation: A survey. IEEE Trans Pattern Anal Mach Intell 24(2):237–267

    Article  Google Scholar 

  10. Dirik M (2018) Collision-Free Mobile Robot navigation using Fuzzy Logic Approach. Int J Comput Appl 179(9):33–39

    Google Scholar 

  11. Duchoň F, Babinec A, Kajan M, Beňo P, Florek M, Fico T, Jurišica L (2014) Path planning with modified a star algorithm for a mobile robot. Procedia Engineering 96:59–69

    Article  Google Scholar 

  12. Engedy I, Horvath G (2010) Artificial neural network based local motion planning of a wheeled mobile robot. In: IEEE 11th International Symposium on Computational Intelligence and Informatics 213–218

  13. Frank B, Stachniss C, Abdo N, Burgard W (2011) Using Gaussian Process Regression for Efficient Motion Planning in Environments with Deformable Objects. In: Automated Action Planning for Autonomous Mobile Robots

  14. Görner M, Chilian A, Hirschmüller H (2010) Towards an autonomous walking robot for planetary surfaces. In: The 10th International Symposium on Artificial Intelligence, robotics and Automation in Space

  15. Gutmann JS, Fukuchi M, Fujita M (2005) Real-time path planning for humanoid robot navigation. In: IJCAI, 1232–1237

  16. Hong YD (2015) Real-time footstep planning and following for navigation of humanoid robots. J Elect Eng Technol 10(5):2142–2148

    Article  Google Scholar 

  17. Kala R, Shukla A, Tiwari R (2010) Dynamic environment robot path planning using hierarchical evolutionary algorithms. Cybernetics and Systems: An International Journal 41(6):435–454

    Article  Google Scholar 

  18. Keshmiri S, Payandeh S (2012) Regression analysis of multi-rendezvous recharging route in multi-robot environment. Int J Soc Robot 4(1):15–27

    Article  Google Scholar 

  19. Kofinas N, Orfanoudakis E, Lagoudakis MG (2013) Complete analytical inverse kinematics for NAO. In: 13th International Conference on Autonomous Robot Systems, 1–6

  20. Kumar A, Kumar PB, Parhi DR (2018) Intelligent Navigation of Humanoids in Cluttered Environments Using Regression Analysis and Genetic Algorithm. Arabian Journal for Science and Engineering 1–24

  21. Kumar PB, Pandey KK, Sahu C, Chhotray A, Parhi DR (2017) A hybridized RA-APSO approach for humanoid navigation. In: Nirma University International Conference on Engineering (NUiCONE), 1–6

  22. Kumar PB, Sahu C, Parhi DR (2018) A hybridized regression-adaptive ant colony optimization approach for navigation of humanoids in a cluttered environment. Appl Soft Comput 68:565–585

    Article  Google Scholar 

  23. Lee YJ, Bien Z (2002) Path planning for a quadruped robot: an artificial field approach. Adv Robot 16(7):609–627

    Article  Google Scholar 

  24. Lee KB, Myung H, Kim JH (2015) Online multiobjective evolutionary approach for navigation of humanoid robots. IEEE Trans Ind Electron 62(9):5586–5597

    Article  Google Scholar 

  25. Lemaire T, Berger C, Jung IK, Lacroix S (2007) Vision-based slam: Stereo and monocular approaches. Int J Comput Vis 74(3):343–364

    Article  Google Scholar 

  26. Liu W, Zhang Z, Li S, Tao D (2017) Road detection by using a generalized Hough transform. Remote Sens 9(6):590

    Article  Google Scholar 

  27. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  28. Mahyuddin MN, Khan SG, Herrmann G (2014) A novel robust adaptive control algorithm with finite-time online parameter estimation of a humanoid robot arm. Robot Auton Syst 62(3):294–305

    Article  Google Scholar 

  29. Meléndez A, Castillo O, Valdez F, Soria J, Garcia M (2013) Optimal design of the fuzzy navigation system for a mobile robot using evolutionary algorithms. Int J Adv Robot Syst 10(2):139

    Article  Google Scholar 

  30. Min H, Lin Y, Wang S, Wu F, Shen X (2015) Path planning of mobile robot by mixing experience with modified artificial potential field method. Advances in Mechanical Engineering 7(12):1–17

    Article  Google Scholar 

  31. Mirjalili R, Yousefi-koma A, Shirazi FA, Mansouri S (2016) Online path planning for SURENA III humanoid robot using model predictive control scheme. In: IEEE 4th International Conference on Robotics and Mechatronics, 416–421

  32. Mizuuchi I, Inaba M, Inoue H (1999) Adaptive pick-and-place behaviors in a whole-body humanoid robot with an autonomous layer based on parallel sensor-motor modules. Robot Auton Syst 28(2–3):99–113

    Article  Google Scholar 

  33. Mohamed Z, Capi G (2012) Development of a new mobile humanoid robot for assisting elderly people. Procedia Engineering 41:345–351

    Article  Google Scholar 

  34. Mohanta JC, Parhi DR, Mohanty SR, Keshari A (2018) A Control Scheme for Navigation and Obstacle Avoidance of Autonomous Flying Agent. Arab J Sci Eng 43(3):1395–1407

    Article  Google Scholar 

  35. Mohanty PK, Parhi DR (2014) A new intelligent motion planning for mobile robot navigation using multiple adaptive neuro-fuzzy inference system. Applied Mathematics & Information Sciences 8(5):2527

    Article  Google Scholar 

  36. Mohanty PK, Parhi DR (2014) Navigation of autonomous mobile robot using adaptive network based fuzzy inference system. J Mech Sci Technol 28(7):2861–2868

    Article  Google Scholar 

  37. Mohanty PK, Parhi DR (2014) Path planning strategy for mobile robot navigation using MANFIS controller. In: Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications 353–361

  38. Mohanty PK, Parhi DR (2015) A new hybrid optimization algorithm for multiple mobile robots navigation based on the CS-ANFIS approach. Memetic Computing 7(4):255–273

    Article  Google Scholar 

  39. Montemerlo M, Thrun S, Koller D, Wegbreit B (2002) FastSLAM: A factored solution to the simultaneous localization and mapping problem. AAAI/IAAI 593598

  40. Ohki T, Nagatani K, Yoshida K (2012) Local path planner for mobile robot in dynamic environment based on distance time transform method. Adv Robot 26(14):1623–1647

    Article  Google Scholar 

  41. Pandey A, Parhi DR (2016) 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):1–11

    Article  Google Scholar 

  42. Parhi DR, Singh MK (2009) Navigational strategies of mobile robots: a review. Int J Autom Control 3(2–3):114–134

    Article  Google Scholar 

  43. Pham DT, Parhi DR (2003) Navigation of multiple mobile robots using a neural network and a Petri Net model. Robotica 21(1):79–93

    Article  Google Scholar 

  44. Pothal JK, Parhi DR (2015) Navigation of multiple mobile robots in a highly clutter terrains using adaptive neuro-fuzzy inference system. Robot Auton Syst 72:48–58

    Article  Google Scholar 

  45. Pun-Cheng LSC, Tang MYF, Cheung IKL (2007) Exact cell decomposition on base map features for optimal path finding. Int J Geogr Inf Sci 21(2):175–185

    Article  Google Scholar 

  46. Qi N, Ma B, Liu XE, Zhang Z, Ren D (2008) A modified artificial potential field algorithm for mobile robot path planning. In: IEEE 7th World Congress on Intelligent Control and Automation 2603–2607

  47. Singh MK, Parhi DR (2011) Path optimisation of a mobile robot using an artificial neural network controller. Int J Syst Sci 42(1):107–120

    Article  MathSciNet  MATH  Google Scholar 

  48. Singh MK, Parhi DR, Pothal JK (2009) ANFIS approach for navigation of mobile robots. In: IEEE International Conference on Advances in Recent Technologies in Communication and Computing: 727–731

  49. Tao D, Guo Y, Li Y, Gao X (2018) Tensor rank preserving discriminant analysis for facial recognition. IEEE Trans Image Process 27(1):325–334

    Article  MathSciNet  MATH  Google Scholar 

  50. Tao D, Lin X, Jin L, Li X (2016) Principal component 2-D long short-term memory for font recognition on single Chinese characters. IEEE Transactions on Cybernetics 46(3):756–765

    Article  Google Scholar 

  51. Tuncer A, Yildirim M (2012) Dynamic path planning of mobile robots with improved genetic algorithm. Comput Electr Eng 38(6):1564–1572

    Article  Google Scholar 

  52. Xia Z, Xiong J, Chen K (2011) Global navigation for humanoid robots using sampling-based footstep planners. IEEE/ASME Transactions on Mechatronics 16(4):716–723

    Article  Google Scholar 

  53. Xue F, Wang W, Li N, Yang Y (2014) FPGA Implementation of Self-Organized Spiking Neural Network Controller for Mobile Robots. Advances in Mechanical Engineering 6:180620

    Article  Google Scholar 

  54. Zhang X, Zhao Y, Deng N, Guo K (2016) Dynamic path planning algorithm for a mobile robot based on visible space and an improved genetic algorithm. Int J Adv Robot Syst 13(3):91

    Article  Google Scholar 

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Correspondence to Priyadarshi Biplab Kumar.

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Kumar, P.B., Sethy, M. & Parhi, D.R. An intelligent computer vision integrated regression based navigation approach for humanoids in a cluttered environment. Multimed Tools Appl 78, 11463–11486 (2019).

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