Local trajectory planning for mobile robot in cluttered environment based on Model Predictive Control

Local trajectory planning for mobile robot in cluttered environment based on Model Predictive Control

Muhammad Alhaddad
Moscow Institute of Physics and Technology (MIPT), Junior Research Scientist, 9, Institutskiy per., Dolgoprudny, Moscow Region, 141701, Russia, tel.: +7(926)877-32-68, This email address is being protected from spambots. You need JavaScript enabled to view it., ORCID: 0000-0002-6801-5503

Konstantin V. Mironov
PhD in Technical Sciences, MIPT, Senior Research Scientist, 9, Institutskiy per., Dolgoprudny, Moscow Region, 141701, Russia; AIRI Research Institute, 32-1, Kutuzovsky pr., Moscow, 121165, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it., ORCID: 0000-0002-4828-1345

Stepan A. Dergachev
HSE University, PhD student, 20, Myasnitskaya ul., Moscow, 101000, Russia; Federal Research Center «Computer Science and Control» of the Russian Academy of Sciences (FRC CSC RAS), Research Engineer, 44-2, ul. Vavilova, Moscow, 119333, Russia, tel.: +7(903)530-58-38, This email address is being protected from spambots. You need JavaScript enabled to view it., ORCID: 0000-0001-8858-2831

Kirill F. Mouraviev
FRC CSC RAS, Junior Research Scientist, 44-2, ul. Vavilova, Moscow, 119333, Russia, tel.: +7(987)411-39-21, This email address is being protected from spambots. You need JavaScript enabled to view it., ORCID: 0000-0001-5897-0702

Aleksandr I. Panov
PhD in Physics and Mathematics, FRC CSC RAS, Leading Research Scientist, 44-2, ul. Vavilova, Moscow, 119333, Russia, ORCID: 0000-0002-9747-3837

Received March 5, 2023

Abstract
The task of local trajectory planning for an autonomous wheeled robotic platform in cluttered indoor environment is considered. Such environment might include narrow passages, which width is less than the length of the platform. Therefore, it is not possible to apply standard approach, when the obstacles are inflated with the maximum radius of the platform. We propose a novel approach based on numerical solution of nonlinear model predictive control task. Oblong shape of the platform is approximated with a high-order ellipse. We define differentiable sigmoid potential function, which may be computed for any point of the workspace given position and orientation of the platform. This function is small far from the platform, and very high inside the platform; it increases when moving towards the robot. The value of this potential function is computed for the set of the support obstacle points and added to the cost function. This function serve as a penalty for collision with obstacles or coming too close to them. We develop an algorithm for the mapping support points onto occupancy grid, which provide collision avoidance. We apply Acados open library, which implement numerical solution of nonlinear model predictive task with sequential quadratic programming. Our approach is implemented as a local planner for the collaborative mobile platform. The experiments were made in artificial maze and in real office environment with narrow passages. Proposed approached allowed the robot to come through the passages that were 10-20 cm wider than the platform. Computation time was around 20 milliseconds.

Key words
Mobile robot, trajectory planning, collision avoidance, model predictive control.

Acknowledgements
This work was partially supported by the Analytical Center for the Government of the Russian Federation in accordance with the subsidy agreement (agreement identifier 000000D730321P5Q0002; grant No. 70-2021-00138).

DOI
10.31776/RTCJ.11306

Bibliographic description
Muhammad Alhaddad et al. (2023). "Local trajectory planning for mobile robot in cluttered environment based on Model Predictive Control". Robotics and Technical Cybernetics, vol. 11, no. 3, pp. 205-214, DOI: 10.31776/RTCJ.11306. (in Russian).

UDC identifier:
004.896

References

  1. Vogel, J. et al. (2021), “An Ecosystem for Heterogeneous Robotic Assistants in Caregiving: Core Functionalities and Use Cases”, In: IEEE Robotics Automation Magazine, pp. 12–28, DOI: 10.1109/MRA.2020.3032142.
  2. Commercial offices, BrainCorp, (2023), available at: https://braincorp.com/applications/shelf-scanning/ (accessed 18 April 2023).
  3. Automated Inventory Management, BrainCorp, (2023), available at: https:// brain-corp.com/ industries/ commercial-offices/ (accessed 18 April 2023).
  4. Gonz ́alez. D. et al. (2016), “A Review of Motion Planning Techniques for Automated Vehicles”, In: IEEE Transactions on Intelligent Transportation Systems, pp. 1135–1145, DOI: 10.1109 /TITS.2015.2498841.
  5. Hart, P.E., Nilsson, N.J., and Bertram Raphael (1968), “A Formal Basis for the Heuristic Determination of Minimum Cost Paths”, In: IEEE Transactions on Systems Science and Cybernetics, pp. 100–107, DOI: 10.1109/TSSC.1968.300136.
  6. Steven M. LaValle and Jr. James J. Kuffner (2001), “Randomized Kinodynamic Planning”, In: The International Journal of Robotics Research, pp. 378–400, DOI: 10.1177 /02783640122067453.
  7. Kavraki, L.E. et al. (1996), “Probabilistic roadmaps for path planning in high-dimensional configuration spaces”, In: IEEE Transactions on Robotics and Automation, pp. 566–580, DOI: 10.1109/70.508439.
  8. Khatib, O. (1985), “Real-time obstacle avoidance for manipulators and mobile robots”, In: Proceedings. 1985 IEEE International Conference on Robotics and Automation, vol. 2, pp. 500–505, DOI: 10.1109/ROBOT.1985.1087247.
  9. Tachkov, A.A. et al. (2021), “Implementation of the trajectory controller based on model predictive control for unmanned ground vehicle”, In: Robotics and Technical Cybernetics, pp. 43–54, DOI: 10.31776/RTCJ.10105.
  10. Ziegler, J. et al. (2014), “Trajectory planning for Bertha — A local, continuous method”, In: 2014 IEEE Intelligent Vehicles Symposium Proceedings, pp. 450–457, DOI: 10.1109/IVS.2014.6856581.
  11. Adamkiewicz, M. et al. (2022), “Vision-Only Robot Navigation in a Neural Radiance World”, In: IEEE Robotics and Automation Letters, pp. 4606–4613, DOI: 10.1109/LRA.2022.3150497.
  12. Schoels, T. et al. (2020), “An NMPC Approach using Convex Inner Approximations for Online Motion Planning with Guaranteed Collision Avoidance”, In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3574–3580, DOI: 10.1109/ICRA40945.2020.9197206.
  13. Schoels, T. et al. (2020), “CIAO*: MPC-based Safe Motion Planning in Predictable Dynamic Environments”, In: IFAC-PapersOnLine 53.2, 21st IFAC World Congress, pp. 6555–6562, DOI: 10.1016/j.ifacol.2020.12.072, available at: https://www.sciencedirect.com/science/article/pii/S2405896320303281 (accessed 18 April 2023).
  14. Thirugnanam Akshay, Jun Zeng and Koushil Sreenath (2022), “Safety-Critical Control and Planning for Obstacle Avoidance between Polytopes with Control Barrier Functions”, In: International Conference on Robotics and Automation (ICRA), pp. 286–292, DOI: 10.1109/ICRA46639.2022.9812334.
  15. Kurenkov, M. et al. (2022), “NFOMP: Neural Field for Optimal Motion Planner of Differential Drive Robots With Nonholonomic Constraints”, In: IEEE Robotics and Automation Letters, pp. 10991–10998. DOI: 10.1109/LRA.2022.3196886.
  16. Williams, G. et al. (2016), “Aggressive driving with model predictive path integral control”, In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1433–1440.
  17. Williams, G. et al. (2017), “Information theoretic MPC for model-based reinforcement learning”. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1714–1721.
  18. Ihab S Mohamed, Guillaume Allibert, and Philippe Martinet (2020), “Model predictive path integral control framework for partially observable navigation: A quadrotor case study”, In: 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), IEEE, pp. 196–203.
  19. Verschueren, R. et al. (2020), “acados: a modular open-source framework for fast embedded optimal control”, arXiv: 1910.13753 [math.OC].
  20. Makarov, D., Panov, A. and Yakovlev, K. (2016), “STRL: multilevel control system for intelligent agents”, In: Fifteenth National Conference on Artificial Intelligence with International Participation KII-2016, October 3-7, Smolensk, Russia, Universum, Smolensk, Russia, vol. 1., pp. 179–188. (in Russian).
  21. Emel’yanov, S. et al. (2016), “Multilayer cognitive architecture for UAV control”, In: Cognitive Systems Research, pp. 58–72, ISSN: 1389-0417, DOI: 10.1016/j.cogsys.2015.12.008.
  22. Panov, A.I. (2019), “Goal Setting and Behavior Planning for Cognitive Agents”, In: Scientific and Technical Information Processing, pp. 404–415, DOI: 10.3103 /S0147688219060066.
  23. Panov, A.I. (2022), “Simultaneous planning and learning in a hierarchical control system of a cognitive agent”, Avtomatika i telemehanika, pp. 869–883, DOI: 10.1134 /S0005117922060054. (in Russian).
  24. Husky UGV - Outdoor Field Research Robot by Clearpath (2023), available at: https:// clearpath robotics.com/ husky-unmanned-ground-vehicle-robot/ (accessed 18 April 2023).
  25. Nash, A. et al. (2007), “Thetaˆ*: Any-angle path planning on grids”, In: AAAI, vol. 7, pp. 1177–1183.
  26. Edsger W Dijkstra et al. (1959), “A note on two problems in connexion with graphs”, Numerische mathematic, pp. 269–271.