Navigational analysis of a humanoid using genetic algorithm with vision assistance
Tóm tắt
In this paper, a novel vision assisted genetic algorithm based navigational controller has been designed for smooth and collision-free path generation of a humanoid robot. Here, sensory information regarding the nearest obstacle distance and path left to the destination are considered as the inputs to the genetic algorithm controller, and necessary turning angle is generated as the required output to avoid the obstacles present in the path and advance towards the destination. The vision based technique is integrated along with the sensor based navigational model to assist in deciding a safe direction of turn in case the humanoid encounters a dead end situation while negotiating with complicated obstacle settings. The developed model has been verified by navigational analysis of a NAO humanoid in a V-REP simulation arena. The simulation results are also validated against an experimental set-up prepared under laboratory conditions that resembles the simulation arena. The results obtained from both the platforms are compared in terms of selected navigational parameters, and a close agreement has been found between them with a minimal percentage of errors. Finally, the developed model is also evaluated against other existing navigational schemes, and substantial performance improvements have been observed.
Tài liệu tham khảo
McGookin EW, Murray-Smith DJ, Li Y, Fossen TI (2000) The optimization of a tanker autopilot control system using genetic algorithms. Trans Inst Meas Control 22(2):141–178
Hartjes S, Visser HG (2017) Efficient trajectory parameterization for environmental optimization of departure flight paths using a genetic algorithm. Proc Inst Mech Eng G J Aerosp Eng 231(6):1115–1123
Hermanu A, Manikas TW, Ashenayi K, Wainwright RL (2004) Autonomous robot navigation using a genetic algorithm with an efficient genotype structure. Intelligent Engineering Systems Through Artificial Neural Networks: Smart Engineering Systems Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, Complex Systems and Artificial Life, pp 319–324
Huwedi A, Budabbus S (2012) Finding an optimal path planning for multiple robots using genetic algorithms. In: The 13th international Arab conference on information technology (ACIT), pp 200–207
Han Z, Wang D, Liu F, Zhao Z (2017) Multi-AGV path planning with double-path constraints by using an improved genetic algorithm. PLoS One 12(7):e0181747
Parvez W, Dhar S (2013) Path planning of robot in static environment using genetic algorithm (GA) technique. Int J Adv Eng Technol 6(3):1205
da Silva AL, da Silva SA, Coelho CJ, Van Baalen J (2016) An evolutionary algorithm for autonomous robot navigation. Proc Comput Sci 80:2261–2265
Bagherian M, Alos A (2015) 3D UAV trajectory planning using evolutionary algorithms: a comparison study. Aeronaut J 119(1220):1271–1285
Erinc G, Carpin S (2007) A genetic algorithm for nonholonomic motion planning. In: ICRA, pp 1843–1849
Hui NB, Biswas T, Samanta S, Pratap S (2016) Multi-agent Mobile robot navigation through genetic-fuzzy algorithm. GRD J Eng 1(4):53–66
Alsouly H, Bennaceur H (2016) Enhanced genetic algorithm for Mobile robot path planning in static and dynamic environment. In: IJCCI (ECTA), pp 121–131
Cosio FA, Castañeda MP. Autonomous robot navigation using genetic algorithms
Tamilselvi D, Shalinie M, Hariharasudan (2011) Optimal path selection for Mobile robot navigation using genetic algorithm. Int J Comput Sci Issues (IJCSI) 8(4):433
Singh NH, Thongam K (2017) Fuzzy logic-genetic algorithm-neural network for Mobile robot navigation: a survey. International Research Journal of Engineering and Technology (IRJET) 4(8):24–45
Tuncer A, Yildirim M, Erkan K (2012) A motion planning system for mobile robots. Adv Electr Comput Eng 12(1):57–62
Klidbary SH, Shouraki SB, Faraji S (2013) Finding proper configurations for modular robots by using genetic algorithm on different terrains. Int J Mater Mech Manuf 1(4):360–365
Kwaśniewski KK, Gosiewski Z (2018) Genetic algorithm for Mobile robot route planning with obstacle avoidance. Acta Mech Automat 12(2):151–159
Zhang J, Zhao S, Zhang Y, Li Y (2015) Optimal planning approaches with multiple impulses for rendezvous based on hybrid genetic algorithm and control method. Adv Mech Eng 7(3):1687814015573783
Ibrahim MF, Zaira A, Bakar A, Hussain A (2009) Genetic algorithm-based robot path planning. In: Industrial electronic seminar
Wang Y, Zhou H, Wang Y (2017) Mobile robot dynamic path planning based on improved genetic algorithm. In: AIP conference proceedings 1864(1):020046
Chen B, Yang Z, Huang S, Du X, Cui Z, Bhimani J, Xie X, Mi N (2017) Cyber-physical system enabled nearby traffic flow modelling for autonomous vehicles. In: IEEE 36th international performance computing and communications conference (IPCCC), pp 1–6.
Iossifidis I, Malysiak D, Reimann H (2011) Model-free local navigation for humanoid robots. In: IEEE international conference on robotics and biomimetics (ROBIO), pp 2204–2210
Bertram D, Kuffner J, Dillmann R, Asfour T (2006) An integrated approach to inverse kinematics and path planning for redundant manipulators. In: IEEE international conference on robotics and automation (ICRA 2006), pp 1874–1879
Kuffner J, Kagami S, Nishiwaki K, Inaba M, Inoue H (2003) Online footstep planning for humanoid robots. In: IEEE international conference on robotics and automation (ICRA’03), vol 1, pp 932–937
Akdas D (2014) An effective mechanical design and realization of a humanoid robot BUrobot. Acta Polytechnica Hungarica 11(10):115–134
Ferro M, Paolillo A, Cherubini A, Vendittelli M (2016) Omnidirectional humanoid navigation in cluttered environments based on optical flow information. In: IEEE-RAS 16th international conference on humanoid robots (humanoids), pp 75–80
Murray D, Little JJ (2000) Using real-time stereo vision for mobile robot navigation. Auton Robot 8(2):161–171
Konolige K, Agrawal M, Bolles RC, Cowan C, Fischler M, Gerkey B (2008) Outdoor mapping and navigation using stereo vision. In: Experimental robotics. Springer, Berlin, pp 179–190
Kia C, Arshad MR (2005) Robotics vision-based heuristic reasoning for underwater target tracking and navigation. Int J Adv Robot Syst 2(3):25
Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395
Gaspar J, Winters N, Santos-Victor J (2000) Vision-based navigation and environmental representations with an omnidirectional camera. IEEE Trans Robot Autom 16(6):890–898
Macesanu G, Moldoveanu F (2010) Computer vision based Mobile robot navigation in unknown environments. Bull Transilv Univ Brasov Eng Sci Ser I 3:259
Güzel MS (2013) Autonomous vehicle navigation using vision and mapless strategies: a survey. Adv Mech Eng 5:234747
Deepu R, Honnaraju B, Murali S (2015) Path generation for robot navigation using a single camera. Proc Comput Sci 46:1425–1432
Kofinas N, Orfanoudakis E, Lagoudakis MG (2013) Complete analytical inverse kinematics for NAO. In: Proceedings of the 13th international conference on autonomous robot systems and competitions (ROBOTICA) 13
Shi W, Wang K, Yang SX (2009) A fuzzy-neural network approach to multisensor integration for obstacle avoidance of a mobile robot. Intell Autom Soft Comput 15(2):289–301
Silva C, de Oliveira Á, Fernandes M (2018) Validation of a dynamic planning navigation strategy applied to mobile terrestrial robots. Sensors 18(12):4322