Behaviour Based Navigational Control of Humanoid Robot Using Genetic Algorithm Technique in Cluttered Environment

Behaviour Based Navigational Control of Humanoid Robot Using Genetic Algorithm Technique in Cluttered Environment

Asita K. Rath Dayal R. Parhi  Harish C. Das  Priyadarshi B. Kumar 

Centre of Biomechanical Science, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, Odisha 751030, India

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

Mechanical Engineering Department, National Institute of Technology, Shillong, Meghalaya 793003, India

Corresponding Author Email: 
asitr06@gmail.com
Page: 
32-36
|
DOI: 
https://doi.org/10.18280/mmc_a.910105
Received: 
23 January 2018
|
Accepted: 
17 April 2018
|
Published: 
31 March 2018
| Citation

OPEN ACCESS

Abstract: 

Humanoids are popular than their wheeled counterparts by the virtue of their ability to mimic the human behaviour and replace human efforts if required. Navigation and path planning is a complex and challenging problem for humanoids as it involves careful consideration of the navigational parameters. This paper introduces the path planning of a humanoid robot utilizing genetic hereditary calculation. The objective of the paper is to design a navigational controller using genetic algorithm for path planning of a humanoid in a complex environment cluttered with obstacles. The basic working of a genetic algorithm has been explained and an objective function for path optimization has been formulated using the logic of the genetic algorithm. The working of the controller has been tested both in simulation and experimental platforms using NAO humanoid robot. Finally, the results obtained from both the environments have been compared against each other with a good agreement between them.

Keywords: 

GA, humanoid robot, navigation, path planning

1. Introduction
2. Basic Overview of Genetic Algorithm
3. Genetic Algorithm Application
4. Control Architecture of Genetic Algorithm
5. Implementation of GA in Humanoid Path Planning
6. Conclusion
  References

[1] Kong JS, Lee BH, Kim JG. (2004). A study on the gait generation of a humanoid robot using genetic algorithm. In SICE 2004 Annual Conference IEEE 1: 187-191.

[2] Capi G, Nasu Y, Barolli L, Mitobe K, Yamanot M, Takedat K. (2002). A new gait optimization approach based on genetic algorithm for walking biped robots and a neural network implementation. IPSJ Journal.

[3] Kundu S, Parhi R, Deepak BBVL. (2012). Fuzzy-neuro based navigational strategy for mobile robot. International Journal of Scientific & Engineering Research 3(6): 1-6.

[4] Kundu S, Parhi DR. (2016). Navigation of underwater robot based on dynamically adaptive harmony search algorithm. Memetic Computing 8(2): 125-146.

[5] Weinberg G, Godfrey M, Rae A, Rhoads J. (2007). A real-time genetic algorithm in human-robot musical improvisation. In International Symposium on Computer Music Modeling and Retrieval Springer, Berlin, Heidelberg 351-359. 

[6] Mohammadi E, Zohoor H, Khadem SM. (2016). Design and prototype of an active assistive exoskeletal robot for rehabilitation of elbow and wrist. Scientia Iranica. Transaction B, Mechanical Engineering 23(3): 998.

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

[8] 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.

[9] Rath AK, Parhi DR, Das HC, Muni MK, Kumar PB. (2018). Analysis and use of fuzzy intelligent technique for navigation of humanoid robot in obstacle prone zone. Defence Technology.

[10] 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.

[11] Mohanty PK, Parhi DR. (2016). Optimal path planning for a mobile robot using cuckoo search algorithm. Journal of Experimental & Theoretical Artificial Intelligence 28(1-2): 35-52.

[12] Mohanty PK, Parhi DR. (2013). Cuckoo search algorithm for the mobile robot navigation. In: International Conference on Swarm, Evolutionary, and Memetic Computing 527-536.

[13] 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.

[14] Mohanty PK, Parhi DR. (2014). Navigation of autonomous mobile robot using adaptive network based fuzzy inference system. Journal of Mechanical Science and Technology 28(7): 2861-2868.

[15] 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.

[16] Mohanty PK, Parhi DR. (2014). A new efficient optimal path planner for mobile robot based on Invasive Weed Optimization algorithm. Frontiers of Mechanical Engineering 9(4): 317-330.

[17] Das PK, Behera HS, Panigrahi BK. (2016). A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning. Swarm and Evolutionary Computation 28: 14-28.

[18] Razzazi M, Sepahvand A. (2017). Time complexity of two disjoint simple paths. Scientia Iranica. 24(3): 1335-1343.

[19] Singh MK, Parhi DR, Pothal JK. (2009). ANFIS approach for navigation of mobile robots. In: International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom'09) 727-731.

[20] Parhi DR, Singh MK. (2009). Navigational strategies of mobile robots: A review. International Journal of Automation and Control 3(2-3): 114-134.

[21] Singh MK, Parhi DR. (2011). Path optimisation of a mobile robot using an artificial neural network controller. International Journal of Systems Science 42(1): 107-120.

[22] Nishiyama M, Iba H. (2015). Converting motion between different types of humanoid robots using genetic algorithms. International Journal of Computer Theory and Engineering 7(2): 97.

[23] Roberts JM, Kee D, Wyeth G. (2003). Improved joint control using a genetic algorithm for a humanoid robot. In: Proceedings of the 2003 Australasian Conference on Robotics and Automation. Australian Robotics and Automation Association Inc.

[24] Pandey A, Parhi DR. (2016). Multiple mobile robots navigation and obstacle avoidance using minimum rule based ANFIS network controller in the cluttered environment. International Journal of Advanced Robotics and Automation 1: 11.

[25] Pandey A, Parhi DR. (2014). MATLAB simulation for mobile robot navigation with hurdles in cluttered environment using minimum rule based fuzzy logic controller. Procedia Technology 14: 28-34.

[26] Villela LFC, Colombini EL, Técnico-IC-PFG R. (2017). Humanoid Robot Walking Optimization using Genetic Algorithms.

[27] Eliot E, BBVL D, Parhi DR. (2012). Design & kinematic analysis of an articulated robotic manipulator.

[28] Pothal JK, Parhi DR. (2015). Navigation of multiple mobile robots in a highly clutter terrains using adaptive neuro-fuzzy inference system. Robotics and Autonomous Systems 72: 48-58.

[29] Karkowski P, Oßwald S, Bennewitz M. (2016). Real-time footstep planning in 3D environments. In: IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids) 69-74.

[30] Cruz RSN, Zannatha JMI. (2017). Efficient mechanical design and limit cycle stability for a humanoid robot: An application of genetic algorithms. Neurocomputing 233: 72-80.