SpringerLink
Log in

Machine Learning and Evolutionary Techniques in Interplanetary Trajectory Design

  • Chapter
  • First Online:
Modeling and Optimization in Space Engineering

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 144))

Abstract

After providing a brief historical overview on the synergies between artificial intelligence research, in the areas of evolutionary computations and machine learning, and the optimal design of interplanetary trajectories, we propose and study the use of deep artificial neural networks to represent, on-board, the optimal guidance profile of an interplanetary mission. The results, limited to the chosen test case of an Earth–Mars orbital transfer, extend the findings made previously for landing scenarios and quadcopter dynamics, opening a new research area in interplanetary trajectory planning.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (Canada)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 129.99
Price excludes VAT (Canada)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Addis, B., Cassioli, A., Locatelli, M., Schoen, F.: A global optimization method for the design of space trajectories. Comput. Optim. Appl. 48(3), 635–652 (2011)

    Article  MathSciNet  Google Scholar 

  2. Ampatzis, C., Izzo, D.: Machine learning techniques for approximation of objective functions in trajectory optimisation. In: Proceedings of the IJCAI-09 Workshop on Artificial Intelligence in Space, pp. 1–6 (2009)

    Google Scholar 

  3. Biesbroek, R.G., Ancarola, B.P.: Optimization of launcher performance and interplanetary trajectories for pre-assessment studies. In: IAF Abstracts, 34th COSPAR Scientific Assembly (2002)

    Google Scholar 

  4. Cassioli, A., Di Lorenzo, D., Locatelli, M., Schoen, F., Sciandrone, M.: Machine learning for global optimization. Comput. Optim. Appl. 51(1), 279–303 (2012)

    Article  MathSciNet  Google Scholar 

  5. Ceriotti, M., Vasile, M.: MGA trajectory planning with an ACO-inspired algorithm. Acta Astronaut. 67(9), 1202–1217 (2010)

    Article  Google Scholar 

  6. Dachwald, B.: Low-thrust trajectory optimization and interplanetary mission analysis using evolutionary neurocontrol. Ph.D. thesis, Doctoral thesis, Universität der Bundeswehr München Fakultät für Luft-und Raumfahrttechnik (2004)

    Google Scholar 

  7. Dachwald, B., Ohndorf, A.: Global optimization of continuous-thrust trajectories using evolutionary neurocontrol. In: Fasano, G., Pinter, J. (eds.) Modeling and Optimization in Space Engineering - 2018. Springer, Basel (2019)

    Google Scholar 

  8. de Croon, G., Izzo, D.: Real-time landing based on optimality principles and vision. In: 23rd International Symposium on Space Flight Dynamics (ISSFD) (2012)

    Google Scholar 

  9. Deb, K., Padhye, N., Neema, G.: Interplanetary trajectory optimization with swing-bys using evolutionary multi-objective optimization. In: International Symposium on Intelligence Computation and Applications, pp. 26–35. Springer, Berlin (2007)

    Google Scholar 

  10. Di Lizia, P., Radice, G.: Advanced global optimisation for mission analysis and design. Final Report Ariadna id 04/4101 (2004)

    Google Scholar 

  11. Dueri, D., Açıkmeşe, B., Scharf, D.P., Harris, M.W.: Customized real-time interior-point methods for onboard powered-descent guidance. J. Guid. Control. Dyn. 40, 197–212 (2016)

    Article  Google Scholar 

  12. Elsayed, S.M., Sarker, R.A., Essam, D.L.: GA with a new multi-parent crossover for solving IEEE-CEC2011 competition problems. In: IEEE Congress on Evolutionary Computation (CEC), 2011, pp. 1034–1040. IEEE, Piscataway (2011)

    Google Scholar 

  13. Englander, J.: Automated trajectory planning for multiple-flyby interplanetary missions. University of Illinois at Urbana-Champaign (2013)

    Google Scholar 

  14. Gad, A., Abdelkhalik, O.: Hidden genes genetic algorithm for multi-gravity-assist trajectories optimization. J. Spacecr. Rocket. 48(4), 629–641 (2011)

    Article  Google Scholar 

  15. Gage, P., Braun, R., Kroo, I.: Interplanetary trajectory optimization using a genetic algorithm. J. Astronaut. Sci. 43(1), 59–76 (1995)

    Google Scholar 

  16. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256 (2010)

    Google Scholar 

  17. Grigoriev, I., Zapletin, M.: Choosing promising sequences of asteroids. Autom. Remote. Control. 74(8), 1284–1296 (2013)

    Article  MathSciNet  Google Scholar 

  18. Hennes, D., Izzo, D.: Interplanetary trajectory planning with Monte Carlo tree search. In: IJCAI, pp. 769–775 (2015)

    Google Scholar 

  19. Hennes, D., Izzo, D., Landau, D.: Fast approximators for optimal low-thrust hops between main belt asteroids. In: IEEE Symposium Series on Computational Intelligence (SSCI), 2016, pp. 1–7. IEEE, Piscataway (2016)

    Google Scholar 

  20. 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 Trans. Syst. Man Cybern. B Cybern. 42(2), 482–500 (2012)

    Article  Google Scholar 

  21. Izzo, D.: Global optimization and space pruning for spacecraft trajectory design. Spacecr. Trajectory Optim. 1, 178–200 (2010)

    Article  Google Scholar 

  22. 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. J. Glob. Optim. 38(2), 283–296 (2007)

    Article  MathSciNet  Google Scholar 

  23. Izzo, D., Simões, L.F., Märtens, M., De Croon, G.C., 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, pp. 1301–1308. ACM, New York (2013)

    Google Scholar 

  24. Izzo, D., Hennes, D., Riccardi, A.: Constraint handling and multi-objective methods for the evolution of interplanetary trajectories. J. Guid. Control. Dyn. 38, 792–800 (2014)

    Article  Google Scholar 

  25. Izzo, D., Simoes, 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 8, 45–55 (2014)

    Google Scholar 

  26. 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, pp. 1207–1214. ACM, New York (2015)

    Google Scholar 

  27. Izzo, D., Hennes, D., Simões, L.F., Märtens, M.: Designing complex interplanetary trajectories for the global trajectory optimization competitions. In: Space Engineering, pp. 151–176. Springer, Berlin (2016)

    Google Scholar 

  28. Janin, G., Gomez-Tierno, M.: The genetic algorithms for trajectory optimization. In: Stockholm International Astronautical Federation Congress (1985)

    Google Scholar 

  29. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014). CoRR abs/1412.6980. http://arxiv.org/abs/1412.6980, 1412.6980

  30. Lavagna, M.R.: Multi-objective pso for interplanetary trajectory design. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 175–175. ACM, New York (2007)

    Google Scholar 

  31. Lee, S., von Allmen, P., Fink, W., Petropoulos, A., Terrile, R.: Multi-objective evolutionary algorithms for low-thrust orbit transfer optimization. In: Genetic and Evolutionary Computation Conference (GECCO 2005) (2005)

    Google Scholar 

  32. Luo, Y.Z., Tang, G.J., Zhou, L.N.: Simulated annealing for solving near-optimal low-thrust orbit transfer. Eng. Optim. 37(2), 201–216 (2005)

    Article  Google Scholar 

  33. Mereta, A., Izzo, D., Wittig, A.: Machine learning of optimal low-thrust transfers between near-earth objects. In: International Conference on Hybrid Artificial Intelligence Systems, pp. 543–553. Springer, Berlin (2017)

    Google Scholar 

  34. Myatt, D., Becerra, V.M., Nasuto, S.J., Bishop, J.: Advanced global optimisation for mission analysis and design. Final Report Ariadna id 04/4101 (2004)

    Google Scholar 

  35. Olds, A.D., Kluever, C.A., Cupples, M.L.: Interplanetary mission design using differential evolution. J. Spacecr. Rocket. 44(5), 1060–1070 (2007)

    Article  Google Scholar 

  36. Pan, B., Chen, Z., Lu, P., Gao, B.: Reduced transversality conditions in optimal space trajectories. J. Guid. Control. Dyn. 36, 1289–1300 (2013)

    Article  Google Scholar 

  37. Pontani, M., Conway, B.A.: Particle swarm optimization applied to space trajectories. J. Guid. Control. Dyn. 33(5), 1429–1441 (2010)

    Article  Google Scholar 

  38. Pontryagin, L.S., Boltyanskii, V., Gamkrelidze, R., Mishchenko, E.F.: The Mathematical Theory of Optimal Processes. Interscience, New York (1962)

    MATH  Google Scholar 

  39. Radice, G., Olmo, G.: Ant colony algorithms for two-impulse interplanetary trajectory optimization. J. Guid. Control. Dyn. 29(6), 1440 (2006)

    Article  Google Scholar 

  40. Rauwolf, G.A., Coverstone-Carroll, V.L.: Near-optimal low-thrust orbit transfers generated by a genetic algorithm. J. Spacecr. Rocket. 33(6), 859–862 (1996)

    Article  Google Scholar 

  41. Rogata, P., Di Sotto, E., Graziano, M., Graziani, F.: Guess value for interplanetary transfer design through genetic algorithms. Adv. Astronaut. Sci. 114, 613–627 (2003)

    Google Scholar 

  42. Sánchez-Sánchez, C., Izzo, D.: Real-time optimal control via deep neural networks: study on landing problems (2016). ar**v preprint ar**v:161008668

    Google Scholar 

  43. Sánchez-Sánchez, C., Izzo, D., Hennes, D.: Learning the optimal state-feedback using deep networks. In: IEEE Symposium Series on Computational Intelligence (SSCI), 2016, pp. 1–8. IEEE, Piscataway (2016)

    Google Scholar 

  44. Schiavone, G., Izzo, D., Simões, L.F., De Croon, G.C.: Autonomous spacecraft landing through human pre-attentive vision. Bioinspir. Biomim. 7(2), 025,007 (2012)

    Article  Google Scholar 

  45. Schlueter, M.: MIDACO software performance on interplanetary trajectory benchmarks. Adv. Space Res. 54(4), 744–754 (2014)

    Article  Google Scholar 

  46. Schlueter, M., Erb, S.O., Gerdts, M., Kemble, S., Rückmann, J.J.: MIDACO on MINLP space applications. Adv. Space Res. 51(7), 1116–1131 (2013)

    Article  Google Scholar 

  47. Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)

    Article  Google Scholar 

  48. Sentinella, M.R., Casalino, L.: Hybrid evolutionary algorithm for the optimization of interplanetary trajectories. J. Spacecr. Rocket. 46(2), 365 (2009)

    Article  Google Scholar 

  49. Simões, L.F., Izzo, D., Haasdijk, E., Eiben, A.E.: Self-adaptive genotype-phenotype maps: neural networks as a meta-representation. In: International Conference on Parallel Problem Solving from Nature, pp. 110–119. Springer (2014)

    Google Scholar 

  50. Simões, L.F., Izzo, D., Haasdijk, E., Eiben, A.: Multi-rendezvous spacecraft trajectory optimization with beam P-ACO. In: European Conference on Evolutionary Computation in Combinatorial Optimization, pp. 141–156. Springer, Berlin (2017)

    Google Scholar 

  51. Stracquadanio, G., La Ferla, A., De Felice, M., Nicosia, G.: Design of robust space trajectories. In: SGAI Conference, pp. 341–354 Springer, Berlin (2011)

    Google Scholar 

  52. Vasile, M., Minisci, E., Locatelli, M.: Analysis of some global optimization algorithms for space trajectory design. J. Spacecr. Rocket. 47(2), 334 (2010)

    Article  Google Scholar 

  53. Vinkó, T., Izzo, D.: Global optimisation heuristics and test problems for preliminary spacecraft trajectory design. Eur Space Agency, Adv Concepts Team, ACT Tech Rep, id: GOHTPPSTD (2008)

    Google Scholar 

  54. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)

    Article  Google Scholar 

  55. Yao, W., Luo, J., Macdonald, M., Wang, M., Ma, W.: Improved differential evolution algorithm and its applications to orbit design. J. Guid. Control. Dyn. 41, 1–8 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. European Space Agency, Noordwijk, The Netherlands

    Dario Izzo & Dharmesh Vijay Tailor

  2. KTH Royal Institute of Technology, Stockholm, Sweden

    Christopher Iliffe Sprague

Authors
  1. Dario Izzo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christopher Iliffe Sprague
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dharmesh Vijay Tailor
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dario Izzo .

Editor information

Editors and Affiliations

  1. Thales Alenia Space, Turin, Italy

    Giorgio Fasano

  2. Department of Industrial and Systems Engineering, Lehigh University , Bethlehem, PA, USA

    János D. Pintér

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Izzo, D., Sprague, C.I., Tailor, D.V. (2019). Machine Learning and Evolutionary Techniques in Interplanetary Trajectory Design. In: Fasano, G., Pintér, J. (eds) Modeling and Optimization in Space Engineering . Springer Optimization and Its Applications, vol 144. Springer, Cham. https://doi.org/10.1007/978-3-030-10501-3_8

Download citation

Publish with us

Policies and ethics

Search

Navigation