Navigation of humanoids by a hybridized regression-adaptive particle swarm optimization approach

• Autorzy: Kumar P. B. , Sahu C. , Parhi D. R.

Identyfikatory

DOI

10.24425/acs.2018.124707

• Języki publikacji: EN

Abstrakty

EN

In the era of humanoid robotics, navigation and path planning of humanoids in complex environments have always remained as one of the most promising area of research. In this paper, a novel hybridized navigational controller is proposed using the logic of both classical technique and computational intelligence for path planning of humanoids. The proposed navigational controller is a hybridization of regression analysis with adaptive particle swarm optimization. The inputs given to the regression controller are in the forms of obstacle distances, and the output of the regression controller is interim turning angle. The output interim turning angle is again fed to the adaptive particle swarm optimization controller along with other inputs. The output of the adaptive particle swarm optimization controller termed as final turning angle acts as the directing factor for smooth navigation of humanoids in a complex environment. The proposed navigational controller is tested for single as well as multiple humanoids in both simulation and experimental environments. The results obtained from both the environments are compared against each other, and a good agreement between them is observed. Finally, the proposed hybridization technique is also tested against other existing navigational approaches for validation of better efficiency.

Słowa kluczowe

EN

navigation; humanoid NAO; RA; APSO; Petri-Net; V-REP

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2018
• Tom: Vol. 28, No. 3
• Strony: 349–--378
• Opis fizyczny: Bibliogr. 33 poz., rys., tab., wykr., wzory

Twórcy

autor

Kumar P. B.

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

autor

Sahu C.

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

autor

Parhi D. R.

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

Bibliografia

• [1] I. Châari, A. Koubaa, H. Bennaceur, S. Trigui and K. Alshalfan: Smartpath: A hybrid aco-ga algorithm for robot path planning, IEEE Congress on Evolutionary Computation (CEC), (2012), 1-8.
• [2] M. Clerc and J. Kennedy: The particle swarm-explosion, stability, and convergence in a multidimensional complex space, IEEE transactions on Evolutionary Computation, 6(1), (2002), 58-73.
• [3] D. Clever and K. Mombaur: An inverse optimal control approach for the transfer of human walking motions in constrained environment to humanoid robots, In Robotics: Science and Systems (2016).
• [4] M. A. Contreras-Cruz, V. Ayala-Ramirez and U. H. Hernandez-Belmonte: Mobile robot path planning using artificial bee colony and evolutionary programming, Applied Soft Computing, 30, (2015), 319-328.
• [5] S. Dalibard, A. El Khoury, F. Lamiraux, A. Nakhaei, M. Taïx and J. P. Laumond: Dynamic walking and whole-body motion planning for humanoid robots: an integrated approach, The International Journal of Robotics Research, 32(9-10), (2013), 1089-1103.
• [6] P. K. Das, H. S. Behera and B. K. Panigrahi: A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning, Swarm and Evolutionary Computation, 28, (2016), 14-28.
• [7] L. Fen, Z. Jiang-Hai, S. Xiao-Bo, Z. Pei-Ying, F. Shi-Hui and L. Zhong-Jie: Path planning of 6-DOF humanoid manipulator based on improved ant colony algorithm, 24th Chinese Conference on Control and Decision (CCDC) (2012), 4158-4161.
• [8] Y. Gigras, K. Choudhary and K. Gupta: A hybrid ACO-PSO technique for path planning, 2nd International Conference on Computing for Sustainable Global Development (INDIACom), (2015), 1616-1621.
• [9] H. C. Huang: A Taguchi-based heterogeneous parallel metaheuristic ACO-PSO and its FPGA realization to optimal polar-space locomotion control of four-wheeled redundant mobile robots, IEEE Transactions on Industrial Informatics, 11(3), (2015), 915-922.
• [10] V. Hugel and N. Jouandeau: Walking patterns for real time path planning simulation of humanoids, In RO-MAN, (2012), 424-430.
• [11] J. Ido, Y. Shimizu, Y. Matsumoto and T. Ogasawara: Indoor navigation for a humanoid robot using a view sequence, The International Journal of Robotics Research, 28(1), (2009), 315-325.
• [12] O. Kanoun, J. P. Laumond and E. Yoshida: Planning foot placements for a humanoid robot: A problem of inverse kinematics, The International Journal of Robotics Research, 30(3), (2011), 476-485.
• [13] P. Karkowski, S. Osswald and M. Bennewitz: Real-time footstep planning in 3D environments, IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), (2016), 69-74.
• [14] R. M. Khan: Problem solving and data analysis using minitab: A clear and easy guide to six sigma methodology, John Wiley & Sons (2013).
• [15] N. Kofinas, E. Orfanoudakis and M. G. Laugoudakis: Complete analytical inverse kinematics for NAO, 13th International Conference on Autonomous Robot Systems (Robotica), (2013), 1-6.
• [16] R. Mirjalili, A. Yousefi-Koma, F. A. Shirazi and S. Mansouri: Online path planning for SURENA III humanoid robot using model predictive control scheme, 4th International Conference on Robotics and Mechatronics (ICROM), (2016), 416-421.
• [17] P. K. Mohanty, D. R. Parhi, A. K. Jha and A. Pandey: Path planning of an autonomous mobile robot using adaptive network based fuzzy controller, IEEE 3rd International on Advance Computing Conference (IACC), (2013), 651-656.
• [18] P. K. Mohanty and D. R. Parhi: Optimal path planning for a mobile robot using cuckoo search algorithm, Journal of Experimental & Theoretical Artificial Intelligence, 28(1-2), (2016), 35-52.
• [19] P. K. Mohanty and D. R. Parhi: Path planning strategy for mobile robot navigation using MANFIS controller, In Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA), (2014), 353-361.
• [20] K. Nishiwaki, J. Chestnutt and S. Kagami: Autonomous navigation of a humanoid robot over unknown rough terrain using a laser range sensor, The International Journal of Robotics Research, 31(11), (2012), 1251-1262.
• [21] D. R . Parhi and M. K. Singh: Navigational strategies of mobile robots: a review, International Journal of Automation and Control, 3(2-3), (2009), 114-134.
• [22] N. Perrin, O. Stasse, F. Lamiraux and E. Yoshida: Weakly collision-free paths for continuous humanoid footstep planning, IEEE/RSJ InternationalConference on Intelligent Robots and Systems (IROS), (2011), 4408-4413.
• [23] J. L. Peterson: Petri net theory and the modeling of systems (1981).
• [24] H. Qu, K. Xing and T. Alexander: An improved genetic algorithm with co-evolutionary strategy for global path planning of multiple mobile robots, Neurocomputing, 120, (2013), 509-517.
• [25] S. H. Ryu, Y. Kang, S. J. Kim, K. Lee, B. J. You and N. L. Doh: Humanoid path planning from hri perspective: A scalable approach via waypoints with a time index, IEEE transactions on cybernetics, 43(1), (2013), 217-229.
• [26] M. Sadodel, A. Yousefi-Koma and M. Khadiv: Offline path planning, dynamic modeling and gait optimization of a 2D humanoid robot, Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM), (2014), 131-136.
• [27] A. J. Schmid and H. Woern: Path planning for a humanoid using NURBS curves, IEEE International Conference on Automation Science and Engineering, (2005), 351-356.
• [28] R. Shakiba, M. Najafipour and M. E. Salehi: An improved PSObased path planning algorithm for humanoid soccer playing robots, 3rd Joint Conference on AI & Robotics and 5th RoboCup Iran Open International Symposium (RIOS), (2013), 1-6.
• [29] Y. Shi and R. Eberhart: A modified particle swarm optimizer. In Evolutionary Computation Proceedings, IEEEWorld Congress on Computational Intelligence, (1998), 69-73.
• [30] Y. Shimizu and T. Sugihara: Efficient path planning of humanoid robots with automatic conformation of body representation to the complexity of environments, 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids), (2012), 755-760.
• [31] M. K. Singh, D. R. Parhi and J. K. Pothal: ANFIS approach for navigation of mobile robots, International Conference on Advances in Recent Technologies in Communication and Computing, (2009), 727-731.
• [32] M. K. Singh and D. R. Parhi: Path optimisation of a mobile robot using an artificial neural network controller, International Journal of Systems Science, 42(1), (2011), 107-120.
• [33] J. K. Yoo and J. H. Kim: Gaze control-based navigation architecture with a situation-specific preference approach for humanoid robots, IEEE/ASME Transactions on Mechatronics, 20(4), (2015), 2425-2436.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).