Manipulator control in an unknown static environment

Manipulator control in an unknown static environment

Pavel K. Lopatin
PhD in Technical Sciences, Reshetnev Siberian State University of Science and Technology (Reshetnev University), Associate Professor, Assistant Professor of of the In-formatics and Computing Techniques Department, 31, Krasnoyarsky Rabochy pr., Krasnoyarsk, 660014,  Russia, tel.: +7(913)575-16-78, This email address is being protected from spambots. You need JavaScript enabled to view it.

Received 01 February 2019

An algorithm for solving the following Problem is given: an n-link manipulating robot (MR), moving from a start configuration in an environment with unknown static obstacles in a finite number of steps should either grasp a given object by its gripper in an allowed configuration or come to the proved conclusion that the object can not be grasped because of the obstacles’ disposition. The algorithm works in the continuous configuration space. The obstacles may have arbitrary number shapes, dimensions and disposition. MR has a sensor system, which may supply reliable or not reliable information about the environment. It is shown, that the Problem solution is reduced to a solution of a finite number of path planning problems in an environment with known forbidden states with its subsequent execution.

Key words
Robot, manipulator, path planning, trajectory, control, sensor system, unknown environment, object grasping, obstacles, reachability, unreachability.


Bibliographic description
Lopatin, P. (2019). Manipulator control in an unknown static environment. Robotics and Technical Cybernetics, 7(1), pp.58-64.

UDC identifier:


  1. Zenkevich, S. and Yushchenko, A. (2004). Foundations of Manipulating Robots Control. 2nd ed. Moscow: MSTU Press, p.480. (In Russ.).
  2. Pritykin, F. and Nefedov, D. (2017). Investigation of points of spaces, defining position of the center of a final link and generalized coordinates. In: Omsk scientists – to the region. Proceedings of the II Regional scientific and technical conference. Omsk state technical university press, pp.139-144. (In Russ.).
  3. Lopatin, P. (2017). An algorithm for a manipulator motion in an unknown static environment. Science bulletin of the Novosibirsk state technical university, [online] 4(69), pp.33–46. Available at: [Accessed 18 Feb. 2019].
  4. Canny, J. (1988). The Complexity of Robot Motion Planning. Cambridge, Massachusetts: The MIT Press.
  5. Nilsson, N. (1971). Problem-Solving Methods in Artificial Intelligence. New York: McGraw-Hill Book Company.
  6. Choset, H. and et al. (2005). Principles of Robot Motion: Theory, Algorithms and Implementations. A Bradford Book, The MIT Press.
  7. LaValle, S. (2006). Planning Algorithms / Motion Planning. [online] Available at: [Accessed 18 Feb. 2019].
  8. Goritov, A. (2009). Construction of the Plan of a Trajectory of the Industrial Robot in the Conditions of the Incomplete Information on an Environment. Mechatronics, Automation, Control, 10, pp.25-29. (In Russ.).
  9. Ilyin, V. (1995). Intelligent Robots: Theory and Algorithms. Krasnoyarsk, Russia: SAA Press. (In Russ.).
  10. Lopatin, P. (2016). Investigation of a Target Reachability by a Manipulator in an Unknown Environment. In: Proceedings of 2016 IEEE International Conference on Mechatronics and Au-tomation. pp.37-42.
  11. LaValle, S. (2011). Motion planning: The essentials. IEEE Robotics and Automation Society Magazine, 18(1), pp.79-89.
  12. Vasilopoulos, V., Vega-Brown, W., Arslan, O., Roy, N. and Koditschek, D. (2018). Sensor-Based Reactive Symbolic Planning in Partially Known Environments. 2018 IEEE International Conference on Robotics and Automation (ICRA).
  13. Kazakov, K. and Semenov, V. (2016). An overview of modern methods for motion planning. Trudy ISP RAN [Proc. ISP RAS], 28(4), pp.241-294.
  14. Makarov, I. and Lokhin, V. ed., (2009). Automatization of Synthesis and Teaching of Intelligent Control Systems. Мoscow: Science Publ., p.228.
  15. Plotnikova, N. (2010). Manipulating robots’ control based on fuzzy logic. In: Proceedings of the 62 scientific conference - Science of South-Ural State University - vol.2. Chelyabinsk, Russia: SUSU Press, pp.170-174.
  16. Raheem Firas, A. (2008). Neuro-fuzzy structure for on-line planning of robot manipulator in unknown dynamic environment. Scientific-educational and applied journal University news. North-Caucasian region. Technical sciences series, 6, pp.41-49.


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