Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-18T13:18:56.710Z Has data issue: false hasContentIssue false
  • English
  • Français

Trajectory Planning and the Target Search by the Mobile Robot in an Environment Using a Behavior-Based Neural Network Approach

Published online by Cambridge University Press:  14 November 2019

Krishna Kant Pandey Open the ORCID record for Krishna Kant Pandey [Opens in a new window]  and
Dayal R. Parhi
Krishna Kant Pandey*
Affiliation:
Mechanical Engineering Department, National Institute of Technology, Rourkela, India
Dayal R. Parhi
Affiliation:
Mechanical Engineering Department, National Institute of Technology, Rourkela, India
*
*Corresponding author. E-mail: [email protected]
Rights & Permissions [Opens in a new window]

Summary

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Navigation and path analysis in a cluttered environment is a challenging task over the last few decades. In this paper, a behavior-based neural network (BNN) and reactive control architecture have been presented for navigation of the mobile robot. Two different reactive behaviors have been taken as inputs function. Obstacle position is the first reactive behavior given by u(o), whereas obstacle angle u(n) according to the target position is the second reactive behavior. The angular velocity and steering angle are the output of the controller. The backpropagation architecture reduces the errors of weight function and records the best weight data that match the BNN controller. Using the BNN algorithm, the robot reacts quickly as compared to other developed techniques. To validate the performance of the controller, simulation and experimental results have been compared in the common platforms. The deviation in results for both the scenarios is found to be within 10%. The results of the BNN algorithm have also been compared with other existing techniques. Effectiveness of the proposed technique is measured in terms of smoothness of the realistic path, collision point detection, path length, and performance time.

Keywords

Behavior-based neural network (BNN)NavigationPath planningObstacle avoidanceRobot localization.
Type
Articles
Information
Robotica , Volume 38 , Issue 9 , September 2020 , pp. 1627 - 1641
Copyright
© Cambridge University Press 2019

References

Fierro, R. and Lewis, F. L., “Control of a nonholonomic mobile robot using neural networks,” IEEE Trans. Neural Networks 9(4), 589–600 (1998).CrossRefGoogle Scholar
Khaledyan, M. and de Queiroz, M., “A formation maneuvering controller for multiple non-holonomic robotic vehicles,” Robotica 37(1), 189–211 (2019).CrossRefGoogle Scholar
El-Sheimy, N., Chiang, K. W. and Noureldin, A., “The utilization of artificial neural networks for multisensor system integration in navigation and positioning instruments,” IEEE Trans. Instrum. Meas. 55(5), 1606–1615 (2006).CrossRefGoogle Scholar
Araujo, A., Portugal, D., Couceiro, M. S. and Rocha, R. P, “Integrating Arduino-based educational mobile robots in ROS,” J. Intell. Rob. Syst. 77(2), 281–298 (2015).CrossRefGoogle Scholar
Marin, L., Valles, M., Soriano, A., Valera, A. and Albertos, P., “Event-based localization in ackermann steering limited resource mobile robots,” IEEE/ASME Trans. Mechatron . 19(4), 1171–1182 (2014).CrossRefGoogle Scholar
Wei, L., Cappelle, C. and Ruichek, Y., “Camera/laser/GPS fusion method for vehicle positioning under extended NIS-based sensor validation,” IEEE Trans. Instrum. Meas. 62(11), 3110–3122 (2013).CrossRefGoogle Scholar
Mujahed, M., Fischer, D. and Mertsching, B., “Admissible gap navigation: A new collision avoidance approach,” Rob. Auton. Syst. 103, 93–110 (2018).CrossRefGoogle Scholar
Tanveer, M. H., Recchiuto, C. T. and Sgorbissa, A., “Analysis of path following and obstacle avoidance for multiple wheeled robots in a shared workspace,” Robotica 37(1), 80–108 (2019).CrossRefGoogle Scholar
Gualda, D., Urena, J., Garcia, J. C., Garcia, E., Ruiz, D. and Lindo, A., “Fusion of data from ultrasonic LPS and isolated beacons for improving MR navigation,” IEEE International Conference on Instrumentation and Measurement Technology Conference (IMTC), (2014) pp. 1552–1555.Google Scholar
Khan, F., Alakberi, A., Almaamari, S. and Beig, A. R., “Navigation Algorithm for Autonomous Mobile Robots in Indoor Environments,” Advances in Science and Engineering Technology International Conferences (ASET), Abu Dhabi, United Arab Emirates (2018) pp. 1–6.Google Scholar
Da Mota, F. A., Rocha, M. X., Rodrigues, J. J., De Albuquerque, V. H. C. and De Alexandria, A. R., “Localization and navigation for autonomous mobile robots using petri nets in indoor environments,” IEEE Access 6, 31665–31676 (2018).Google Scholar
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).CrossRefGoogle Scholar
Toth, M., Stojcsics, D., Domozi, Z. and Lovas, I., “Fuzzy Based Indoor Navigation for Mobile Robots,” IEEE 16th International Symposium on Intelligent Systems and Informatics (SISY), Subotica, Serbia (2018) pp. 333–338Google Scholar
Xu, J., Guo, H. and Wu, S., “Indoor Multi-Sensory Self-Supervised Autonomous Mobile Robotic Navigation,” IEEE International Conference on Industrial Internet (ICII), Seattle, WA, USA (2018) pp. 119–128Google Scholar
Cho, S., Park, J. and Lee, J., “A dynamic localization algorithm for a high-speed mobile robot using indoor GPS,” Robotica 30(4), 681–690 (2012).CrossRefGoogle Scholar
Lee, J., Lim, J. and Lee, J., “Compensated heading angles for outdoor mobile robots in magnetically disturbed environment,” IEEE Trans. Ind. Electron. 65(2), 1408–1419 (2018).CrossRefGoogle Scholar
Simanek, J., Reinstein, M. and Kubelka, V., “Evaluation of the EKF-based estimation architectures for data fusion in mobile robots,” IEEE/ASME Trans. Mechatron . 20(2), 985–990 (2015).CrossRefGoogle Scholar
Bai, J., Lian, S., Liu, Z., Wang, K. and Liu, D., “Deep learning based robot for automatically picking up garbage on the grass,” IEEE Trans. Consum. Electron. 64(3), 382–389 (2018).CrossRefGoogle Scholar
Speck, D., Dornhege, C. and Burgard, W., “Shakey 2016—How much does it take to redo shakey the robot?,” IEEE Rob. Autom. Lett. 2(2), 1203–1209 (2017).Google Scholar
Kunze, L., Hawes, N., Duckett, T., Hanheide, M. and Krajník, T., “Artificial intelligence for long-term robot autonomy: A survey,” IEEE Rob. Autom. Lett. 3(4),4023–4030 (2018).CrossRefGoogle Scholar
Lui, L. and Sukhatme, G. S., “A solution to time-varying markov decision processes,” IEEE Rob. Autom. Lett. 3(3), 1631–1638 (2018).Google Scholar
Pierson, A., Wang, Z. and Schwager, M., “Intercepting rogue robots: An algorithm for capturing multiple evaders with multiple pursuers,” IEEE Rob. Autom. Lett. 2(2), 530–537 (2017).CrossRefGoogle Scholar
Zhang, H., Wang, Y., Zheng, J. and Yu, J., “Path planning of industrial robot based on improved RRT algorithm in complex environments,” IEEE Access 6, 53296–53306 (2018).CrossRefGoogle Scholar
Luo, X., Li, S., Liu, S. and Liu, G., “An optimal trajectory planning method for path tracking of industrial robots,” Robotica, 37(3), 502–520 (2019).CrossRefGoogle Scholar
Arango, D. G., Leal, H. V., Hernandez, L. M., Pascual, M. T. S. and Sandoval, M. H., “Homotopy path planning for terrestrial robots using spherical algorithm,” IEEE Rob. Autom. Sci. Eng. 15(2), 567–585 (2018).CrossRefGoogle Scholar
Cherroun, L. and Boumehraz, M., “Fuzzy behavior based navigation approach for mobile robot in unknown environment,” J. Electr. Eng. 13(4), 1–8 (2013).Google Scholar
Al-Mayyahi, A., Wang, W. and Birch, P., “Adaptive neuro-fuzzy technique for autonomous ground vehicle navigation,” Robotics 3(4), 349–370 (2014).CrossRefGoogle Scholar