1.Atkinson, A. C., “Robust and diagnostic regression analyses,” Commun. Stat. Theory Methods 11(22), 2559–2571 (1982).
2.Frank, B., Stachniss, C., Abdo, N. and Burgard, W., “Using Gaussian Process Regression for Efficient Motion Planning in Environments with Deformable Objects,” Proceedings of the 9th AAAI Conference on Automated Action Planning for Autonomous Mobile Robots, San Francisco, USA (2011) pp. 2–7.
3.Frank, B., Stachniss, C., Abdo, N. and Burgard, W., “Efficient Motion Planning for Manipulation Robots in Environments with Deformable Objects,” 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), San Francisco, USA, IEEE (2011) pp. 2180–2185.
4.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, Chongqing, China, IEEE (2008) pp. 2603–2607.
5.Lee, Y. J. and Bien, Z., “Path planning for a quadruped robot: an artificial field approach,” Adv. Robot. 16(7), 609–627 (2002).
6.Kim, E., Choi, S. and Oh, S. “Structured kernel subspace learning for autonomous robot navigation,” Sensors 18(2), 582 (2018).
7.Dirik, M., “Collision-free mobile robot navigation using fuzzy logic approach,” Int. J. Comput. Appl. 179(9), 33–39 (2018).
8.Keshmiri, S. and Payandeh, S., “Multi-robots, Multi-locations Recharging Paradigm: A Regression Route Technique,” Proceedings of the 14th IASTED International Conference, Robotics and Applications, Cambridge, MA, USA (2009) pp. 160–165.
9.Keshmiri, S. and Payandeh, S., “Regression analysis of multi-rendezvous recharging route in multi-robot environment,” Int. J. Soc. Robot. 4(1), 15–27 (2012).
10.Li, G., Yamashita, A., Asama, H. and Tamura, Y., “An Efficient Improved Artificial Potential Field Based Regression Search Method for Robot Path Planning,” 2012 International Conference on Mechatronics and Automation (ICMA), Chengdu, Sichuan, China, IEEE (2012), pp. 1227–1232.
11.Li, G., Tamura, Y., Yamashita, A. and Asama, H., “Effective improved artificial potential field-based regression search method for autonomous mobile robot path planning,” Int. J. Mechatron. Autom. 3(3), 141–170 (2013).
12.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,” 1999 7th IEEE International Conference on Emerging Technologies and Factory Automation, Barcelona, Spain, IEEE, vol. 1 (1999) pp. 529–534.
13.Dongre, V. and Raikwal, J., “An improved user browsing behavior prediction using regression analysis on Web Logs,” Int. J. Comput. Appl. 120(19), 19–23 (2015).
14.Kumar, P. B., Sahu, C. and R. Parhi, D., “A hybridized regression-adaptive ant colony optimization approach for navigation of humanoids in a cluttered environment,” Appl. Soft Comput. 68, 565–585 (2018).
15.Kumar, P. B., Mohapatra, S. and R. Parhi, D., “An intelligent navigation of humanoid NAO in the light of classical approach and computational intelligence,” Comput. Animat. Virt. Worlds 30(12), e1858 (2018).
16.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,” Sci. Iran. 26(1), 375–393 (2019).
17.Kumar, P. B., Sethy, M. and R. Parhi, D., “An intelligent computer vision integrated regression based navigation approach for humanoids in a cluttered environment,” Multimedia Tools Appl. 1–24 (2018).
18.Al, S., Dülger, L. C. and Kirecci, A., “Hybrid actuator: Motion control using genetic algorithms,” Proc. Inst. Mech. Eng., Part C: J. Mech. Eng. Sci. 223(7), 1657–1665 (2009).
19.Wang, S., Lu, Z., Wei, L., Ji, G. and Yang, J., “Fitness-scaling adaptive genetic algorithm with local search for solving the multiple depot vehicle routing problem,” Simulation 92(7), 601–616 (2016).
20.Nagib, G. and Gharieb, W., “Path Planning for a Mobile Robot Using Genetic Algorithms,” International Conference on Electrical, Electronic and Computer Engineering, Cairo, Egypt (2004) pp. 185–189.
21.Raouf, N. and Pourtakdoust, S. H., “Launch vehicle multi-objective reliability-redundancy optimization using a hybrid genetic algorithm-particle swarm optimization,” Proc. Inst. Mech. Eng., Part G: J. Aerosp. Eng. 229(10), 1785–1797 (2015).
22.Saraswathi, M., Murali, G. B. and Deepak, B. B. V. L., “Optimal path planning of mobile robot using hybrid cuckoo search-bat algorithm,” Procedia Comput. Sci. 133, 510–517 (2018).
23.Singh, N. H. and Thongam, K., “Mobile robot navigation using fuzzy logic in static environments,” Procedia Comput. Sci. 125, 11–17 (2018).
24.Zhang, X., Zhao, Y., Deng, N. and Guo, K., “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 (2016).
25.Tuncer, A. and Yildirim, M., “Dynamic path planning of mobile robots with improved genetic algorithm,” Comput. Electr. Eng. 38(6), 1564–1572 (2012).
26.Allaire, F. C., Tarbouchi, M., Labonté, G. and Fusina, G., “FPGA Implementation of Genetic Algorithm for UAV Real-Time Path Planning,” In: Unmanned Aircraft Systems (Springer, Dordrecht, 2008) pp. 495–510.
27.Hu, L., Gu, Z. Q., Huang, J., Yang, Y. and Song, X., “Research and realization of optimum route planning in vehicle navigation systems based on a hybrid genetic algorithm,” Proc. Inst. Mech. Eng., Part D: J. Automobile Eng. 222(5), 757–763 (2008).
28.Elshamli, A., Abdullah, H. A. and Areibi, S., “Genetic Algorithm for Dynamic Path Planning,” Canadian Conference on Electrical and Computer Engineering, Ontario, Canada, IEEE, vol. 2 (2004) pp. 677–680.
29.Kwaśniewski, K. K. and Gosiewski, Z., “Genetic algorithm for mobile robot route planning with obstacle avoidance,” Acta Mech. Autom. 12(2), 151–159 (2018).
30.Lamini, C., Benhlima, S. and Elbekri, A., “Genetic algorithm based approach for autonomous mobile robot path planning,” Procedia Comput. Sci. 127, 180–189 (2018).
31.Bakdi, A., Hentout, A., Boutami, H., Maoudj, A., Hachour, O. and Bouzouia, B., “Optimal path planning and execution for mobile robots using genetic algorithm and adaptive fuzzy-logic control,” Robot. Autonomous Syst. 89, 95–109 (2017).
32.Silva Arantes, J. D., Silva Arantes, M. D., Motta Toledo, C. F., Júnior, O. T. and Williams, B. C., “Heuristic and genetic algorithm approaches for UAV path planning under critical situation,” Int. J. Artif. Intell. Tools 26(01), 1760008 (2017).
33.Meléndez, A., Castillo, O., Valdez, F., Soria, J. and Garcia, M., “Optimal design of the fuzzy navigation system for a mobile robot using evolutionary algorithms,” Int. J. Adv. Robot. Syst. 10(2), 139 (2013).
34.Hartjes, S. and Visser, H. G., “Efficient trajectory parameterization for environmental optimization of departure flight paths using a genetic algorithm,” Part G: J. Aerospace Eng. 231(6), 1115–1123 (2017).
35.Sachin, M. U. and Gaonkar, P., “Design, implementation and control of a humanoid robot for obstacle avoidance using 8051 Microcontroller,” IOSR J. Electron. Commun. Eng. 5(5), 40–50 (2013).
36.Kim, J. Y., Park, I. W. and Oh, J. H., “Walking control algorithm of biped humanoid robot on uneven and inclined floor,” J. Intell. Robot. Syst. 48(4), 457–484 (2007).
37.Hereid, A., Cousineau, E. A., Hubicki, C. M. and Ames, A. D., “3D Dynamic Walking with Underactuated Humanoid Robots: A Direct Collocation Framework for Optimizing Hybrid Zero Dynamics,” 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, IEEE (2016), pp. 1447–1454.
38.Baskoro, A. S. and Priyono, M. G., “Design of Humanoid Robot Stable Walking Using Inverse Kinematics and Zero Moment Point,” 2016 International Electronics Symposium (IES), Denpasar, Indonesia, IEEE (2016), pp. 335–339.
39.Lin, C. Y., Lee, K. F., Wang, H. C., Kuo, P. H., Ho, Y. F. and Li, T. H. S., “Design and Implementation of 3-DOF Dynamic Balancing Waist and Its Fuzzy Control for Adult-Sized Humanoid Robot,” 2014 Proceedings of the SICE Annual Conference (SICE), Sapporo, Japan, IEEE (2014), pp. 2133–2138.
40.Inomata, K. and Uchimura, Y., “3DZMP-Based Control of a Humanoid Robot with Reaction Forces at 3-Dimensional Contact Points,” 2010 11th IEEE International Workshop on Advanced Motion Control, Nagaoka, Niigata, IEEE (2010), pp. 402–407.
41.Kofinas, N., Orfanoudakis, E. and G., M. Lagoudakis, “Complete Analytical Inverse Kinematics for NAO,” 13th International Conference on Autonomous Robot Systems (Robotica), Lisbon, Portugal (2013) pp. 1–6.
42.Peterson, J. L., Petri Net Theory and the Modeling of Systems (Prentice-Hall, Englewood Cliffs, 1981).
43.Pham, D. T. and Parhi, D. R., “Navigation of multiple mobile robots using a neural network and a Petri Net model,” Robotica 21(1), 79–93 (2003).