Novel AI-Based Metaheuristic Optimization Approaches for Designing INS Navigation Systems
Subject Areas : electrical and computer engineeringعلی محمدی 1 * , Farid Sheikholeslam 2 , Mehdi Emami 3
1 -
2 -
3 - Yazd University
Keywords: Intelligent optimization, metaheuristic algorithms, soft computing, integrated INS/GNSS navigation, inclined planes system optimization,
Abstract :
Soft computing techniques in engineering sciences have covered a large amount of research. Among them is the design and optimization of navigation systems for use in land, sea, and air transportation systems. Therefore, in this paper, an attempt is made to take advantage of novel approaches of intelligent metaheuristic optimization for designing integrated navigation systems. For this purpose, the inclined planes system optimization algorithm with several modified and new versions have been used along with two well-known methods of genetic algorithm and particle swarm optimization. Considerations are made on an INS/GNSS problem with IMU MEMS inertia measurement modules. Process and measurement noise covariance matrices are considered as design variables and the sum of mean-squares-error as an objective function in the form of a single-objective minimization problem. Outputs are presented in terms of statistical and performance indicators such as runtime, fitness, convergences, angular-velocity accuracy, latitude, longitude, altitude, roll, pitch, yaw, and routing along with the ranking of algorithms. The overall assessment indicated the correctness of the performance and the relative superiority of the IPO and IIPO over the competitors and competitive performance of the assumed algorithms in comparison with the volume of considerations and calculations of the base problem.
[1] G. Minkler and J. Minkler, Theory and Application of Kalman Filtering, Magellan Book Company, 1993.
[2] V. Sathiya and M. Chinnadurai, "Evolutionary algorithms-based multi-objective optimal mobile robot trajectory planning," Robotica, vol. 37, no. 8, pp. 1363-1382, Aug. 2019.
[3] S. M. J. Jalali, A. Khosravi, P. M. Kebria, R. Hedjam, and S. Nahavandi, "Autonomous robot navigation system using the evolutionary multi-verse optimizer algorithm," in Proc. IEEE Int. Conf. on Systems, Man and Cybernetics, SMC’19, pp. 1221-1226, Bari, Italy, 6-9 Oct. 2019.
[4] Q. Liu, Y. Li, and L. Liu, "A 3D simulation environment and navigation approach for robot navigation via deep reinforcement learning in dense pedestrian environment," in Proc. IEEE 16th Int. Conf. on Automation Science and Engineering, CASE’20, pp. 1514-1519, Hong Kong, China, 20-21 Aug. 2020.
[5] L. Cong, S. Yue, H. Qin, B. Li, and J. Yao, "Implementation of a MEMS-based GNSS/INS integrated scheme using supported vector machine for land vehicle navigation," IEEE Sensors J., vol. 20, no. 23, pp. 14423-14435, Dec. 2020.
[6] J. H. Yi, M. Lu, and X. J. Zhao, "Quantum inspired monarch butterfly optimisation for UCAV path planning navigation problem," Int. J. Bio-Inspired Comput., vol. 15, no. 2, pp. 75-89, 2020.
[7] M. G. Bellemare, et al., "Autonomous navigation of stratospheric balloons using reinforcement learning," Nature, vol. 588, no. 7836, pp. 77-82, Dec. 2020.
[8] N. Al Bitar and A. I. Gavrilov, "Neural networks aided unscented kalman filter for integrated INS/GNSS systems," in Proc. 27th Saint Petersburg Int. Conf. on Integrated Navigation Systems, ICINS’20, 4 pp., 25-27 May 2020.
[9] J. Wang, Z. Ma, and X. Chen, "Generalized dynamic fuzzy NN model based on multiple fading factors SCKF and its application in integrated navigation," IEEE Sensors J., vol. 21, no. 3, pp. 3680-3693, Feb. 2021.
[10] F. Gul, W. Rahiman, S. S. N. Alhady, A. Ali, I. Mir, and A. Jalil, "Meta-heuristic approach for solving multi-objective path planning for autonomous guided robot using PSO-GWO optimization algorithm with evolutionary programming," J. Ambient Intell. Humaniz. Comput., vol. 12, no. 7, pp. 7873-7890, Jul. 2021.
[11] P. Zielinski and U. Markowska-Kaczmar, "3D robotic navigation using a vision-based deep reinforcement learning model," Appl. Soft Comput., vol. 110, Article ID: 107602, Oct. 2021.
[12] D. Gao, X. Lyu, F. Qin, L. Chang, and B. Hu, "A real time gravity compensation method for high precision INS based on neural network," in ¬Proc. 28th Saint Petersburg Int. Conf. on Integrated Navigation Systems, ICINS’21, 5 pp., Saint Petersburg, Russia, 31 May-2 Jun. 2021.
[13] S. Wen, Z. Wen, D. Zhang, H. Zhang, and T. Wang, "A multi-robot path-planning algorithm for autonomous navigation using meta-reinforcement learning based on transfer learning," Appl. Soft Comput., vol. 110, Article ID: 107605, Oct. 2021.
[14] N. Al Bitar and A. Gavrilov, "A novel approach for aiding unscented Kalman filter for bridging GNSS outages in integrated navigation systems," Navigation, vol. 68, no. 3, pp. 521-539, Fall. 2021.
[15] F. Yan, S. Li, E. Zhang, J. Guo, and Q. Chen, "An adaptive nonlinear filter for integrated navigation systems using deep neural networks," Neurocomputing, vol. 446, pp. 130-144, Jul. 2021.
[16] Y. Wu, "A survey on population-based meta-heuristic algorithms for motion planning of aircraft," Swarm Evol. Comput., vol. 62, Article ID: 100844, Apr. 2021.
[17] E. Pulido Herrera and H. Kaufmann, "Adaptive methods of Kalman filtering for personal positioning systems," in Proc. 23rd Int. Technical Meeting of the Satellite Division of The Institute of Navigation, pp. 584-589, Portland, OR, USA, 21-24 Sept. 2010.
[18] R. Gonzalez, J. I. Giribet, and H. D. Patino, "NaveGo: a simulation framework for low-cost integrated navigation systems," J. Control Eng. Appl. Informatics, vol. 17, no. 2, pp. 110-120, 2015.
[19] J. Georgy, Advanced Nonlinear Techniques for Low Cost Land Vehicle Navigation, Ph.D Thesis, Department of Electrical and Computer Engineering, Queen's University Kingston, Ontario, Canada, 2010.
[20] A. R. Khairi, Heading Drift Mitigation for Low-Cost Land Inertial Pedestrian Navigation, Ph.D Thesis, University of Nottingham, 2012.
[21] D. E. Goldberg, Genetic Algorithms, Addison Wesley, 1989.
[22] J. H. Holland, Adaptation in Natural and Artificial Systems, the University of Michigan Press, 1975.
[23] M. Dorigo, V. Maniezzo, and A. Colorni, "Ant system: optimization by a colony of cooperating agents," IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 26, no. 1, pp. 29-41, Feb. 1996.
[24] J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proc. Int. Conf. on Neural Networks, ICNN'95, vol. 4, pp. 1942-1948, Perth, Australia, 27 Nov.-1 Dec. 1995.
[25] M. H. Mozaffari, H. Abdy, and S. H. Zahiri, "IPO: an inclined planes system optimization algorithm," Computing & Informatics, vol. 35, no. 1, pp. 222-240, 2016.
[26] A. Mohammadi and S. H. Zahiri, "IIR model identification using a modified inclined planes system optimization algorithm," Artificial Intelligence Review, vol. 48, no. 2, pp. 237-259, 2017.
[27] S. Mohammadi-Esfahrood, A. Mohammadi, and S. H. Zahiri, "A simplified and efficient version of inclined planes system optimization algorithm," in Proc. 5th Conf. on Knowledge Based Engineering and Innovation, KBEI’19, pp. 504-509, Tehran, Iran, 28 Feb.-1 Mar. 2019.
[28] A. Mohammadi, F. Sheikholeslam, and S. Mirjalili, "Inclined planes system optimization: theory, literature review, and state-of-the-art versions for IIR system identification," Expert Systems with Applications, vol. 200, Article ID: 117127, Aug. 2022.
[29] A. Mohammadi, F. Sheikholeslam, and M. Emami, "Metaheuristic algorithms for integrated navigation systems," In: Ouaissa, M., Khan, I.U., Ouaissa, M., Boulouard, Z., Hussain Shah, S.B. (eds) Computational Intelligence for Unmanned Aerial Vehicles Communication Networks. Studies in Computational Intelligence, vol. 1033, pp. 45-72, Springer, 2022.
[30] A. Mohammadi, F. Sheikholeslam, M. Emami, and S. Mirjalili, "Designing INS/GNSS integrated navigation systems by using IPO algorithms," submitted to Neural Computing and Applications, 2022.