The rapid developments of artificial intelligence in the last decade are influencing aerospace engineering to a great extent and research in this context is proliferating. We share our observations on the recent developments in the area of spacecraft guidance dynamics and control, giving selected examples on success stories that have been motivated by mission designs. Our focus is on evolutionary optimisation, tree searches and machine learning, including deep learning and reinforcement learning as the key technologies and drivers for current and future research in the field. From a high-level perspective, we survey various scenarios for which these approaches have been successfully applied or are under strong scientific investigation. Whenever possible, we highlight the relations and synergies that can be obtained by combining different techniques and projects towards future domains for which newly emerging artificial intelligence techniques are expected to become game changers.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Girimonte, D., Izzo, D. Artificial intelligence for space applications. Intelligent Computing Everywhere, 2007, 235–253.
Lary, D. J. Artificial intelligence in aerospace. Aerospace Technologies Advancements, 2010.
Zhu, X. X., Tuia, D., Mou, L. L., Xia, G. S., Zhang, L. P., Xu, F., Fraundorfer, F. Deep learning in remote sensing: a comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 2017, 5(4): 8–36.
Izzo, D., Sprague, C., Tailor, D. Machine learning and evolutionary techniques in interplanetary trajectory design. arXiv preprint arXiv:1802.00180, 2018.
Li, S., Huang, X. X., Yang, B. Review of optimization methodologies in global and China trajectory optimization competitions. Progress in Aerospace Sciences, 2018, 102: 60–75.
Russell, S. J., Norvig, P. Artificial Intelligence: A Modern Approach, 3rd ed. Pearson Education, Inc. 2009.
Campelo, F., Aranha, C. EC Bestiary: a bestiary of evolutionary, swarm and other metaphor-based algorithms. Zenodo, 2018, DOI: 10.5281/zenodo.1293352.
Vinkó, T., Izzo, D. Global optimisation heuristics and test problems for preliminary spacecraft trajectory design. Act Technical Report, Act-TNT-Mad-GOHTPPSTD, European Space Agency, the Advanced Concepts Team, 2008.
Stracquadanio, G., La Ferla, A., De Felice, M., Nicosia, G. Design of robust space trajectories. In: Proceedings of the 31st International Conference on Innovative Techniques and Applications of Artificial Intelligence, 2011, 341–354.
Addis, B., Cassioli, A., Locatelli, M., Schoen, F. A global optimization method for the design of space trajectories. Computational Optimization and Applications, 2011, 48(3): 635–652.
Schlueter, M. MIDACO software performance on interplanetary trajectory benchmarks. Advances in Space Research, 2014, 54(4): 744–754.
Islam, S. M., Das, S., Ghosh, S., Roy, S., Suganthan, P. N. An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2012, 42(2): 482–500.
Cassioli, A., Di Lorenzo, D., Locatelli, M., Schoen, F., Sciandrone, M. Machine learning for global optimization. Computational Optimization and Applications, 2012, 51(1): 279–303.
Simões, L. F., Izzo, D., Haasdijk, E., Eiben, A. E. Self-adaptive genotype-phenotype maps: neural networks as a meta-representation. In: Proceedings of the 13th International Conference on Parallel Problem Solving from Nature, 2014, 110–119.
Elsayed, S. M., Sarker, R. A., Essam, D. L. GA with a new multi-parent crossover for solving IEEE-CEC2011 competition problems. In: Proceedings of 2011 IEEE Congress of Evolutionary Computation, 2011, 1034–1040.
Myatt, D. R., Becerra, V. M., Nasuto, S. J., Bishop, J. M. Advanced global optimisation for mission analysis and design. Ariadna Final Report 03-4101a, ESA Ariadna, 2004.
Izzo, D., Becerra, V. M., Myatt, D. R., Nasuto, S. J., Bishop, J. M. Search space pruning and global optimisation of multiple gravity assist spacecraft trajectories. Journal of Global Optimization, 2007, 38(2): 283–296.
Olds, A. D., Kluever, C. A., Cupples, M. L. Interplanetary mission design using differential evolution. Journal of Spacecraft and Rockets, 2007, 44(5): 1060–1070.
Izzo, D., Simões, L. F., Märtens, M., De Croon, G. C. H. E., Heritier, A., Yam, C. H. Search for a grand tour of the Jupiter Galilean moons. In: Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, 2013, 1301–1308.
Yao, W., Luo, J. J., Macdonald, M., Wang, M. M., Ma, W. H. Improved differential evolution algorithm and its applications to orbit design. Journal of Guidance, Control, and Dynamics, 2018, 41(4): 935–942.
Vasile, M., Minisci, E., Locatelli, M. An inflationary differential evolution algorithm for space trajectory optimization. IEEE Transactions on Evolutionary Computation, 2011, 15(2): 267–281.
Pontani, M., Conway, B. A. Particle swarm optimization applied to space trajectories. Journal of Guidance, Control, and Dynamics, 2010, 33(5): 1429–1441.
Vasile, M., Minisci, E., Locatelli, M. Analysis of some global optimization algorithms for space trajectory design. Journal of Spacecraft and Rockets, 2010, 47(2): 334–344.
Wolpert, D. H., Macready, W. G. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 67–82.
Englander, J. A., Conway, B. A., Williams, T. Automated mission planning via evolutionary algorithms. Journal of Guidance, Control, and Dynamics, 2012, 35(6): 1878–1887.
Sentinella, M. R., Casalino, L. Hybrid evolutionary algorithm for the optimization of interplanetary trajectories. Journal of Spacecraft and Rockets, 2009, 46(2): 365–372.
Izzo, D., Hennes, D., Riccardi, A. Constraint handling and multi-objective methods for the evolution of interplanetary trajectories. Journal of Guidance, Control, and Dynamics, 2015, 38(4): 792–800.
Radice, G., Olmo, G. Ant colony algorithms for two impluse interplanetary trajectory optimization. Journal of Guidance, Control, and Dynamics, 2006, 29(6): 1440–1444.
Schlueter, M., Erb, S. O., Gerdts, M., Kemble, S., Rückmann, J. J. MIDACO on MINLP space applications. Advances in Space Research, 2013, 51(7): 1116–1131.
Coello Coello, C. A. Evolutionary multi-objective optimization: a historical view of the field. IEEE Computational Intelligence Magazine, 2006, 1(1): 28–36.
Deb, K., Padhye, N., Neema, G. Interplanetary trajectory optimization with swing-bys using evolutionary multi-objective optimization. In: Proceedings of the 2nd International Symposium on Intelligence Computation and Applications, 2007, 26–35.
Schütze, O., Vasile, M., Junge, O., Dellnitz, M., Izzo, D. Designing optimal low-thrust gravity-assist trajectories using space pruning and a multi-objective approach. Engineering Optimization, 2009, 41(2): 155–181.
Märtens, M., Izzo, D. The asynchronous island model and NSGA-II: study of a new migration operator and its performance. In: Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, 2013, 1173–1180.
Zotes, F. A., Penãs, M. S. Particle swarm optimisation of interplanetary trajectories from Earth to Jupiter and Saturn. Engineering Applications of Artificial Intelligence, 2012, 25(1): 189–199.
Lee, S., Von Allmen, P., Fink, W. O., Petropoulos, A. E., Terrile, R. J. Multi-objective evolutionary algorithms for low-thrust orbit transfer optimization. In: Proceedings of Genetic and Evolutionary Computation Conference, 2005.
Montanõ, A. A., Coello Coello, A. C., Schütze, O. Multiobjective optimization for space mission design problems. Computational Intelligence in Aerospace Sciences, 2014, 1–46.
Vasile, M., Ricciardi, L. A direct memetic approach to the solution of multi-objective optimal control problems. In: Proceedings of 2016 IEEE Symposium Series on Computational Intelligence, 2016, 1–8.
Chai, R. Q., Savvaris, A., Tsourdos, A., Chai, S. C., Xia, Y. Q. Unified multiobjective optimization scheme for aeroassisted vehicle trajectory planning. Journal of Guidance, Control, and Dynamics, 2018, 41(7): 1521–1530.
Izzo, D., Märtens, M. The Kessler run: on the design of the GTOC9 challenge. Acta Futura, 2018, 11: 11–24.
Petropoulos, A., Grebow, D., Jones, D., Lantoine, G., Nicholas, A., Roa, J., Senent, J., Stuart, J., Arora, N., Pavlak, T. et al. GTOC9: results from the jet propulsion laboratory (team JPL). Acta Futura, 2018, 11: 25–35.
Luo, Y. Z., Zhu, Y. H., Zhu, H., Yang, Z., Sun, Z. J., Zhang, J. GTOC9: results from the national university of defense technology (team NUDT). Acta Futura, 2018, 11: 37–47.
Shen, H. X., Zhang, T. J., Huang, A. Y., Li, Z. GTOC 9: results from the Xi’an satellite control center (team XSCC). Acta Futura, 2018, 11: 49–55.
Ceriotti, M., Vasile, M. MGA trajectory planning with an ACO-inspired algorithm. Acta Astronautica, 2010, 67(9–10): 1202–1217.
Englander, J. A., Conway, B. A. Automated solution of the low-thrust interplanetary trajectory problem. Journal of Guidance, Control, and Dynamics, 2016, 40(1): 15–27.
Yam, C. H., Lorenzo, D. D., Izzo, D. Low-thrust trajectory design as a constrained global optimization problem. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2011, 225(11): 1243–1251.
Izzo, D., Simões, L. F., Yam, C. H., Biscani, F., Di Lorenzo, D., Addis, B., Cassioli, A. GTOC5: results from the European Space Agency and University of Florence. Acta Futura, 2014, 8: 45–55.
Abdelkhalik, O., Darani, S. Hidden genes genetic algorithms for systems architecture optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference 2016, 2016, 629–636.
Izzo, D., Getzner, I., Hennes, D., Simões, L. F. Evolving solutions to TSP variants for active space debris removal. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, 2015, 1207–1214.
Wilt, C. M, Thayer, J. T., Ruml, W. A comparison of greedy search algorithms. In: Proceedings of the 3rd Annual Symposium on Combinatorial Search, 2010.
Hennes, D., Izzo, D. Interplanetary trajectory planning with Monte Carlo Tree search. In: Proceedings of the 24th International Conference on Artificial Intelligence, 2015, 769–775.
Browne, C. B., Powley, E., Whitehouse, D., Lucas, S. M., Cowling, P. I., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S., Colton, S. A survey of Monte Carlo tree search methods. IEEE Transactions on Computational Intelligence and AI in games, 2012, 4(1): 1–43.
Simões, L. F., Izzo, D., Haasdijk, E., Eiben, A. E. Multi-rendezvous spacecraft trajectory optimization with beam P-ACO. In: Proceedings of the 17th European Conference on Evolutionary Computation in Combinatorial Optimization, 2017, 141–156.
Basu, K., Melton, R. G., Aguasvivas-Manzano, S. Time-optimal reorientation using neural network and particle swarm formulation. In: Proceedings of 2017 AAS/AIAA Astrodynamics Specialist Conference, 2017.
Ampatzis, C., Izzo, D. Machine learning techniques for approximation of objective functions in trajectory optimisation. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) 2009, Workshop on Artificial Intelligence in Space, 2009.
Hennes, D., Izzo, D., Landau, D. Fast approximators for optimal low-thrust hops between main belt asteroids. In: Proceedings of 2016 IEEE Symposium Series on Computational Intelligence, 2016, 1–7.
Mereta, A., Izzo, D., Wittig, A. Machine learning of optimal low-thrust transfers between near-earth objects. In: Proceedings of the 12th International Conference on Hybrid Artificial Intelligence Systems, 2017, 543–553.
Izzo, D., Hennes, D., Simões, L. F., Märtens, M. Designing complex interplanetary trajectories for the global trajectory optimization competitions. Space Engineering, 2016, 151–176.
Izzo, D. Global optimization and space pruning for spacecraft trajectory design. Spacecraft Trajectory Optimization, 2010, 178–201.
Dachwald, B. Low-thrust trajectory optimization and interplanetary mission analysis using evolutionary neurocontrol. Ph.D. Dissertation, DLR-Universität der Bundeswehr München, München, 2004.
Dachwald, B., Ohndorf, A. Global optimization of continuous-thrust trajectories using evolutionary neurocontrol. Modeling and Optimization in Space Engineering, 2019.
Schmidhuber, J. Deep learning in neural networks: an overview. Neural Networks, 2015, 61: 85–117.
Sánchez-Sánchez, C., Izzo, D., Hennes, D. Learning the optimal state-feedback using deep networks. In: Proceedings of 2016 IEEE Symposium Series on Computational Intelligence, 2016, 1–8.
Sánchez-Sánchez, C., Izzo, D. Real-time optimal control via Deep Neural Networks: study on landing problems. arXiv preprint arXiv:1610.08668, 2016.
Izzo, D., Tailor, D., Vasileiou, T. On the stability analysis of deep neural network representations of an optimal state-feedback. arXiv preprint arXiv:1812.02532, 2018.
Furfaro, R., Bloise, I., Orlandelli, M., Di Lizia, P., Topputo, F., Linares, R. A recurrent deep architecture for quasi-optimal feedback guidance in planetary landing. In: Proceedings of IAA SciTech Forum on Space Flight Mechanics and Space Structures and Materials, 2018, 1–24.
Franceschini, N. Small brains, smart machines: from fly vision to robot vision and back again. Proceedings of the IEEE, 2014, 102(5): 751–781.
Krizhevsky, A., Sutskever, I., Hinton, G. E. Imagenet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, 2012, 1097–1105.
Furfaro, R., Bloise, I., Orlandelli, M., Di Lizia, P., Topputo, F., Linares, R. Deep learning for autonomous lunar landing. In: Proceedings of 2018 AAS/AIAA Astrodynamics Specialist Conference, 2018.
Shang, H. B., Wu, X. Y., Qiao, D., Huang, X. Y. Parameter estimation for optimal asteroid transfer trajectories using supervised machine learning. Aerospace Science and Technology, 2018, 79: 570–579.
Shah, V., Beeson, R. Rapid approximation of invariant manifolds using machine learning methods. In: Proceedings of 2017 AAS/AIAA Astrodynamics Specialist Conference, 2017.
Hammer, B., Gersmann, K. A note on the universal approximation capability of support vector machines. Neural Processing Letters, 2003, 17(1): 43–53.
Li, W. P., Huang, H., Peng, F. J. Trajectory classification in circular restricted three-body problem using support vector machine. Advances in Space Research, 2015, 56(2): 273–280.
Peng, H., Bai, X. Exploring capability of support vector machine for improving satellite orbit prediction accuracy. Journal of Aerospace Information Systems, 2018, 15(6): 366–381.
Peng, H., Bai, X. L. Artificial neural network-based machine learning approach to improve orbit prediction accuracy. Journal of Spacecraft and Rockets, 2018, 55(5): 1248–1260.
Gaudet, B., Furfaro, R. Robust spacecraft hovering near small bodies in environments with unknown dynamics using reinforcement learning. In: Proceedings of AIAA/AAS Astrodynamics Specialist Conference, 2012, 5072.
Willis, S., Izzo, D., Hennes, D. Reinforcement learning for spacecraft maneuvering near small bodies. In: Proceedings of AAS/AIAA Space Flight Mechanics Meeting, 2016, 16–277.
Pellegrini, E., Russell, R. P. A multiple-shooting differential dynamic programming algorithm. In: Proceedings of AAS/AIAA Space Flight Mechanics Meeting, 2017.
Ozaki, N., Campagnola, S., Yam, C. H., Funase, R. Differential dynamic programming approach for robust-optimal low-thrust trajectory design considering uncertainty. In: Proceedings of the 25th International Symposium on Space Flight Dynamics, 2015.
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M. et al. Mastering the game of Go with deep neural networks and tree search. Nature, 2016, 529(7587): 484–489.
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A. et al. Mastering the game of Go without human knowledge. Nature, 2017, 550(7676): 354–359.
Chu, X., Alfriend, K. T., Zhang, J., Zhang, Y. Q-learning algorithm for path-planning to maneuver through a satellite cluster. In: Proceedings of 2018 AAS/AIAA Astrodynamics Specialist Conference, 2018.
Gaudet, B., Linares, R., Furfaro, R. Deep reinforcement learning for six degree-of-freedom planetary powered descent and landing. arXiv preprint arXiv:1810.08719, 2018.
Dario Izzo graduated as a doctor of aeronautical engineering from the University Sapienza of Rome (Italy). He then took his second master in Satellite Platforms at the University of Cranfield in the United Kingdom and completed his Ph.D. in mathematical modelling at the University Sapienza of Rome where he lectured classical mechanics and space flight mechanics. Dario Izzo later joined the European Space Agency and became the scientific coordinator of its Advanced Concepts Team. He devised and managed the Global Trajectory Optimization Competitions events, the ESAs Summer of Code in Space and the Kelvins innovation and competition platform for space problems. He published more than 170 papers in international journals and conferences making key contributions to the understanding of flight mechanics and spacecraft control and pioneering techniques based on evolutionary and machine learning approaches. Dario Izzo received the Humies Gold Medal and led the team winning the 8th edition of the Global Trajectory Optimization Competition.
Marcus Martens graduated from the University of Paderborn (Germany) with a master degree in computer science. He joined the European Space Agency as a Young Graduate Trainee in artificial intelligence where he worked on multi-objective optimization of spacecraft trajectories. He was part of the winning team of the 8th edition of the Global Trajectory Optimization Competition (GTOC) and received a HUMIES gold medal for developing algorithms achieving human competitive results in trajectory design. The Delft University of Technology awarded him a Ph.D. for his thesis on information propagation in complex networks. After his time at the network architectures and services group in Delft (Netherlands), Marcus rejoined the European Space Agency, where works as a research follow in the Advanced Concepts Team. While his main focus is on applied artificial intelligence and evolutionary optimization, Marcus has worked together with experts from different fields and authored works related to neuroscience, cyber-security and gaming.
Binfeng Pan received his Ph.D. degree in aerospace engineering from Northwestern Polytechnical University, China, in 2010. He is an associate professor at School of Astronautics, Northwestern Polytechnical University. His research interests are in the area of trajecotry optimizations, computational guidance and control, and applications of AI in aerospace engineering. He is the principal investigator (PI) or co-PI of several research grants on the aforementioned topics from the National Natural Science Foundation of China (NSFC), and the Chinese industry.
About this article
Cite this article
Izzo, D., Märtens, M. & Pan, B. A survey on artificial intelligence trends in spacecraft guidance dynamics and control. Astrodyn 3, 287–299 (2019). https://doi.org/10.1007/s42064-018-0053-6
- deep learning
- machine learning
- evolutionary computing
- genetic algorithms