Journal "Software Engineering"
a journal on theoretical and applied science and technology
ISSN 2220-3397

Issue N7 2025 year

DOI: 10.17587/prin.16.323-333
Path Planning of Inboard Space Flying Robot in a Virtual 3D Environment
E. V. Strashnov, Senior Researcher, strashnov_evg@mail.ru, I. N. Mironenko, Researcher, vstudio@niisi.ras.ru, L. A. Finagin, Researcher, antifin@mail.ru, Scientific Research Institute for System Analysis of the National Research Centre "Kurchatov Institute", Moscow, 117218, Russian Federation
Corresponding author: Evgeny V. Strashnov, Senior Researcher, Scientific Research Institute for System Analysis of the National Research Centre "Kurchatov Institute", Moscow, 117218, Russian Federation, E-mail: strashnov_evg@mail.ru
Received on March 27, 2025
Accepted on April 22, 2025

The paper considers the task of motion path planning for inboard space flying robots in a virtual environment with static and dynamic obstacles. This task involves a three-dimensional map construction of the virtual scene, as well as finding the robot's path based on this map. An approach was developed for mapping that uses bounding volumes surrounding the geometry of objects, as well as bounding boxes aligned along the world coordinate system axes. Based on this approach, original algorithms have been developed for constructing and updating a scene map with the implementation of fast intersection tests between containers and map cells. In turn, pathfinding of robot's motion with an adaptation of the heuristic algorithm A* for the three-dimensional case is proposed that is based on the representation of map in the form of a regular decomposition graph. In the proposed solution, path graph nodes are formed dynamically, and obstacle nodes are processed using special sets with their updating taking into account map changes. The approbation of the developed algorithms and approaches was carried out in virtual environment complex and showed their effectiveness and adequacy for real time inboard space flying robots motion simulation without collision with obstacles.

Keywords: virtual space flying robot, intravehicular activity, path planning, three-dimensional map, bounding volume, graph, virtual environment systems
pp. 323—333
For citation:
Strashnov E. V., Mironenko I. N., Finagin L. A. Path Planning of Inboard Space Flying Robot in a Virtual 3D Environment, Programmnaya Ingeneria, 2025, vol. 16, no. 7, pp. 323—333. DOI: 10.17587/prin.16.323-333 (in Russian).
The publication is made within the state task of Scientific Research Institute for System Analysis of the National Research Centre "Kurchatov Institute" on topic No. FNEF-2024-0002 "Mathematical modeling of multiscale dynamic processes and virtual environment systems".
References:
  1. Fong T., Micire M., Morse T. et al. Smart SPHERES: a telerobotic free-flyer for intravehicular activities in space, Proceedings of AIAA Space 2013, San Diego, California, 2013, article 5338. DOI: 10.2514/6.2013-5338.
  2. Smith T., Barlow J., Bualat M. et al. Astrobee: A new platform for free-flying robotics research on the international space station, Proceedings of International Symposium on Artificial Intelligence, Robotics and Automation in Space, 2016, available at: https://www.researchgate.net/publication/305936171_Astrobee_A_New_Platform_for_Free-Flying_Robotics_Research_on_the_International_Space_Station.
  3. Bualat M., Smith T., Fong T. W. et al. Astrobee: A new tool for ISS operations, 15th Int. Conference on Space Operations, 2018, article 2517. DOI: 10.2514/6.2018-2517.
  4. Zhang H. Y., Lin W. M., Chen A. X. Path planning for the mobile robot: a review, Symmetry, 2018, vol. 10, no. 10, article 450. DOI: 10.3390/sym10100450.
  5. Coltin B., Fusco J., Moratto Z. et al. Localization from visual landmarks on a free-flying robot, IEEE/RSJ IROS, 2016, pp. 4377—4382. DOI: 10.1109/IROS.2016.7759644.
  6. Yakovlev K. S., Khitkov V. V., Loginov M. I., Petrov A. V. Navigation system based on markers for UAV group, Robototehnika i tehnicheskaja kibernetika, 2014, no. 4, pp. 44—48 (in Russian).
  7. Hornung A., Wurm K. M., Bennewitz M. et al. OctoMap: An efficient probabilistic 3D mapping framework based on octrees, Autonomous Robots, 2013, vol. 34, no. 3, pp. 189—206. DOI: 10.1007/s10514-012-9321-0.
  8. Muravyev K., Yakovlev K. Evaluation of topological map­ping methods in indoor environments, IEEE Access, 2023, no. 11, pp. 132683—132698. DOI: 10.1109/ACCESS.2023.3335818.
  9. Guruji A. K., Agarwal H., Parsediya D. K. OctoMap: Time-efficient A* algorithm for robot path planning, Procedia Technology, 2013, vol. 23, pp. 144—149. DOI: 10.1016/j.protcy.2016.03.010.
  10. Daniel K., Nash A., Koenig S., Felner A. Theta*: Any-angle path planning on grids, Artificial Intelligence Research, 2010, vol. 39, pp. 533-579. DOI: 10.1613/jair.2994.
  11. Phillips M., Likhachev M. SIPP: Safe interval path planning for dynamic environments, Proceedings of The 2011 IEEE International Conference on Robotics and Automation (ICRA 2011), 2011, pp. 5628—5635. DOI: 10.1109/ICRA.2011.5980306.
  12. Yakovlev K. S. AA-SIPP: any-angle path finding amidst static and dynamic obstacles, Iskusstvennyj intellekt i prinjatie reshenij, 2020, no. 1, pp. 49—59 (in Russian). DOI: 10.14357/20718594200105.
  13. Andreychuk A. A. Algorithm for planning and coordination of a set of trajectories for a group of intelligent agents, Iskusstven-nyj intellekt i prinjatie reshenij, 2018, no. 4, pp. 72—85 (in Russian). DOI: 10.14357/20718594180407.
  14. Lamini C., Benhlima S., Elbekri A. Genetic algorithm based approach for autonomous mobile robot path planning, Procedia Computer Science, 2018, vol. 127, pp. 180—189. DOI: 10.1016/j.procs.2018.01.113.
  15. Brand M., Masuda M., Wehner N. et al. Ant colony optimization algorithm for robot path planning, International Conference on Computer Design and Applications ICCDA, 2010, vol. 3, pp. 436—439. DOI: 10.1109/ICCDA.2010.5541300.
  16. Yusof T. S. T., Toha S., Yusof H. Path planning for visually impaired people in an unfamiliar environment using particle swarm optimization, Procedia Computer Science, 2015, vol. 76, pp. 80—86. DOI: 10.1016/j.procs.2015.12.281.
  17. Ericson C. Real-Time Collision Detection, Boston, MA, CRC Press, 2004, 593 p.
  18. Mikhaylyuk M. V., Maltsev A. V., Timokhin P. Yu. et al. The VirSim virtual environment system for the simulation complexes of cosmonaut training, Pilotiruemye polety v kosmos, 2020, vol. 37, no. 4, pp. 72—95 (in Russian).
  19. Russell S., Norvig P. Artificial intelligence: A modern approach (4th ed.), Boston, Pearson, 2020, 1136 p.
  20. Mikhaylyuk M. V., Torgashev M. A. The visual editor and cal­culation module of block diagrams for simulation and training complexes, Programmnye produkty i sistemy, 2014, no. 4, pp. 10—15 (in Russian).
  21. Mikhaylyuk M. V., Omelchenko D. V., Strashnov E. V. Command and supervisory modes for virtual robot control, Vestnik kibernetiki, 2016, no. 4, pp. 67—72 (in Russian).