Abstract
Autonomous systems have achieved great success over the last couple of decades. They have bring the revolutionary change in the world, either its ground vehicles, aerial systems or underground vehicles. Number of research papers have been written on the importance of autonomous systems and their applications in different field. Keeping in view the pattern of research done by authors, an effort has been made to provide a single platform for readers to familiarize themselves with applications involved in terrestrial, aerial and undersea systems along with different sets of dimensions involved in achieving these applications. Therefore, the article provides a summary of the main communication methods used by terrestrial, aerial, and undersea space research vehicles. In addition to providing an exhaustive summary of the difficulties encountered in trajectory planning, space exploration, optimization, and other areas, the research also presents optimization methods applicable to aerial, undersea, and terrestrial applications. As the literature lacks extensive studies like this one, hence an effort has been made to fill the gap for readers interested in path design. This study tackles numerical, bioinspired, and hybrid techniques for each of the dimensions given. With this study, we attempted to provide a single repository for a plethora of research on autonomous land vehicles, their trajectory optimization, as well as research on aerial and undersea vehicles. The article ends with the most practical directions for future research.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abhishek, B., Ranjit, S., Shankar, T., Eappen, G., Sivasankar, P., Rajesh, A.: Hybrid PSO-HSA and PSO-GA algorithm for 3D path planning in autonomous UAVs. SN Appl. Sci. 2(11), 1–16 (2020)
Abualigah, L., Abd Elaziz, M., Sumari, P., Geem, Z.W., Gandomi, A.H.: Reptile search algorithm (RSA): a nature-inspired meta-heuristic optimizer. Expert Syst. Appl. 191, 116158 (2022)
Arnold, M., Burgermeister, B., Führer, C., Hippmann, G., Rill, G.: Numerical methods in vehicle system dynamics: state of the art and current developments. Veh. Syst. Dyn. 49(7), 1159–1207 (2011)
Ben-Moshe, B., Landau, Y., Marbel, R., Mishiner, A.: Bio-inspired micro drones. In: 2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE), pp. 1–5. IEEE (2018)
Czyż, Z., Suwala, S., Karpiński, P., Skiba, K.: Numerical analysis of the support platform for an unmanned aerial vehicle. In: Journal of Physics: Conference Series, vol. 2130, p. 012029. IOP Publishing (2021)
Erke, S., Bin, D., Yiming, N., Qi, Z., Liang, X., Dawei, Z.: An improved a-star based path planning algorithm for autonomous land vehicles. Int. J. Adv. Robot. Syst. 17(5), 1729881420962263 (2020)
Gill, P.E., Murray, W., Saunders, M.A., Wright, M.H.: User’s guide for NPSOL (version 4.0): A fortran package for nonlinear programming. Technical report, STANFORD UNIV CA SYSTEMS OPTIMIZATION LAB (1986)
Gul, F., et al.: A centralized strategy for multi-agent exploration. IEEE Access 10, 126871–126884 (2022)
Gul, F., Mir, I., Abualigah, L., Sumari, P., Forestiero, A.: A consolidated review of path planning and optimization techniques: technical perspectives and future directions. Electronics 10(18), 2250 (2021)
Gul, F., Mir, S., Mir, I.: Coordinated multi-robot exploration: Hybrid stochastic optimization approach. In: AIAA SCITECH 2022 Forum, p. 1414 (2022)
Gul, F., Rahiman, W., Alhady, S.N., Ali, A., Mir, I., Jalil, A.: 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. 12(1), 7873–7890 (2021)
Herman, P.: Numerical test of underwater vehicle dynamics using velocity controller. In: 2019 12th International Workshop on Robot Motion and Control (RoMoCo), pp. 26–31. IEEE (2019)
Hu, Y., Zhao, W., Wang, L.: Vision-based target tracking and collision avoidance for two autonomous robotic fish. IEEE Trans. Industr. Electron. 56(5), 1401–1410 (2009)
Hull, T., Enright, W.H., Jackson, K.: Runge-kutta research at toronto. Appl. Numer. Math. 22(1–3), 225–236 (1996)
Ilango, H.S., Ramanathan, R.: A performance study of bio-inspired algorithms in autonomous landing of unmanned aerial vehicle. Procedia Comput. Sci. 171, 1449–1458 (2020)
Kim, Y., Park, J., Son, W., Yoon, T.: Modified turn algorithm for motion planning based on clothoid curve. Electron. Lett. 53(24), 1574–1576 (2017)
Le, A.V., Nhan, N.H.K., Mohan, R.E.: Evolutionary algorithm-based complete coverage path planning for tetriamond tiling robots. Sensors 20(2), 445 (2020)
Lim, H.S., Chin, C.K., Chai, S., Bose, N.: Online AUV path replanning using quantum-behaved particle swarm optimization with selective differential evolution. Comput. Model. Eng. Sci. 125(1), 33–50 (2020)
Lim, H.S., Fan, S., Chin, C.K., Chai, S., Bose, N., Kim, E.: Constrained path planning of autonomous underwater vehicle using selectively-hybridized particle swarm optimization algorithms. IFAC-PapersOnLine 52(21), 315–322 (2019)
Mansury, E., Nikookar, A., Salehi, M.E.: Artificial bee colony optimization of ferguson splines for soccer robot path planning. In: 2013 First RSI/ISM International Conference on Robotics and Mechatronics (ICRoM), pp. 85– 89. IEEE (2013)
Mir, I., Eisa, S.A., Taha, H.E., Maqsood, A., Akhtar, S., Islam, T.U.: A stability perspective of bio-inspired UAVs performing dynamic soaring opti mally. Bioinspiration Biomimetics (2021)
Mir, I., Maqsood, A., Akhtar, S.: Dynamic modeling & stability analysis of a generic UAV in glide phase. In: MATEC Web of Conferences, vol. 114, p. 01007. EDP Sciences (2017)
Ni, J., Wu, L., Fan, X., Yang, S.X.: Bioinspired intelligent algorithm and its applications for mobile robot control: a survey. Comput. Intell. Neurosci. 2016, 1–1 (2016)
Noor, M.A., Noor, K.I., Al-Said, E., Waseem, M.: Some new iterative methods for nonlinear equations. Math. Probl. Eng. 2010 (2010)
Owen, M.P., Duffy, S.M., Edwards, M.W.: Unmanned aircraft sense and avoid radar: Surrogate flight testing performance evaluation. In: 2014 IEEE Radar Conference, pp. 0548–0551. IEEE (2014)
Poudel, S., Moh, S.: Hybrid path planning for efficient data collection in UAV-aided WSNs for emergency applications. Sensors 21(8), 2839 (2021)
Saied, M., Slim, M., Mazeh, H., Francis, C., Shraim, H.: Unmanned aerial vehicles fleet control via artificial bee colony algorithm. In: 2019 4th Con ference on Control and Fault Tolerant Systems (SysTol), pp. 80–85. IEEE (2019)
Sánchez-Ferreira, C., Coelho, L., Ayala, H.V., Farias, M.C., Llanos, C.H.: Bio-inspired optimization algorithms for real underwater image restoration. Signal Process. Image Commun. 77, 49–65 (2019)
Sanchez-Lopez, J.L., Wang, M., Olivares-Mendez, M.A., Molina, M., Voos, H.: A real-time 3D path planning solution for collision-free navigation of multirotor aerial robots in dynamic environments. J. Intell. Rob. Syst. 93(1–2), 33–53 (2019)
Shen, C., Shi, Y., Buckham, B.: Model predictive control for an AUV with dynamic path planning. In: 2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), pp. 475–480. IEEE (2015)
Szczepanski, R., Erwinski, K., Tejer, M., Bereit, A., Tarczewski, T.: Optimal scheduling for palletizing task using robotic arm and artificial bee colony algorithm. Eng. Appl. Artif. Intell. 113, 104976 (2022)
Szczepanski, R., Tarczewski, T., Erwinski, K.: Energy efficient local path planning algorithm based on predictive artificial potential field. IEEE Access 10, 39729–39742 (2022)
Usman, M.R., Usman, M.A., Yaq, M.A., Shin, S.Y.: UAV reconnaissance using bio-inspired algorithms: Joint PSO and penguin search optimization algorithm (PESOA) attributes. In: 2019 16th IEEE Annual Consumer Com- munications & Networking Conference (CCNC), pp. 1–6. IEEE (2019)
Verbeke, J., Cools, R.: The newton-raphson method. Int. J. Math. Educ. Sci. Technol. 26(2), 177–193 (1995)
Wood, G.R.: The bisection method in higher dimensions. Math. Program. 55(1–3), 319–337 (1992)
Yilmaz, N.K., Evangelinos, C., Lermusiaux, P.F., Patrikalakis, N.M.: Path planning of autonomous underwater vehicles for adaptive sampling using mixed integer linear programming. IEEE J. Oceanic Eng. 33(4), 522–537 (2008)
Zhang, K., Sprinkle, J., Sanfelice, R.G.: A hybrid model predictive controller for path planning and path following. In: Proceedings of the ACM/IEEE Sixth International Conference on Cyber-Physical Systems, pp. 139–148 (2015)
Zhang, X., Xia, S., Zhang, T., Li, X.: Hybrid FWPS cooperation algorithm based unmanned aerial vehicle constrained path planning. Aerosp. Sci. Technol. 118, 107004 (2021)
Zhang, Y., Guan, G., Pu, X.: The robot path planning based on improved artificial fish swarm algorithm. Mathematical Problems in Engineering 2016 (2016)
Zhao, Y.J.: Optimal patterns of glider dynamic soaring. Op- timal control applications and methods 25(2), 67–89 (2004). https://doi.org/10.1002/oca.739
Zhu, D., Li, W., Yan, M., Yang, S.X.: The path planning of AUV based on ds information fusion map building and bio-inspired neural network in unknown dynamic environment. Int. J. Adv. Rob. Syst. 11(3), 34 (2014)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Gul, F., Mir, I., Gul, U., Forestiero, A. (2023). A Review of Space Exploration and Trajectory Optimization Techniques for Autonomous Systems: Comprehensive Analysis and Future Directions. In: Comito, C., Talia, D. (eds) Pervasive Knowledge and Collective Intelligence on Web and Social Media. PerSOM 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 494. Springer, Cham. https://doi.org/10.1007/978-3-031-31469-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-31469-8_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-31468-1
Online ISBN: 978-3-031-31469-8
eBook Packages: Computer ScienceComputer Science (R0)