The speed control system of the unmanned ground vehicle (UGV) is propose. It utilizes mathematical model of UGV, the
trajectory control system and Monte-Carlo localization algorithm. The control system converts the planned trajectory
into control signals, which surves as inputs for the UGV mathematical model. The output of the model is the predicted
trajectory of the vehicle. Predicted trajectory is applied to each particle – hypothesis about the vehicle. pose Based
on the particles trajectories prediction the collision probability is calculated and the safe speed is chosen. The
proposed algorithm was tested on the real UGV. Experimental results demonstrait that the developed dynamic model allows
to accurately predict the UGV trajectory and the speed control system reduces the speed to safe values while performing
maneuvers and traversing narrow passages. The behavior of the system is analogous to the one of the driver reducing
speed in the complex and ambiguous situations on the road.

*Key words:*
motion safety, motion safety, collision probability, risk evaluation, unmanned vehicle, dynamic model, localization of
Monte-Carlo, particle filter

DOI: 10.1134/S0235009219030041

*Cite:*
Kibalov V. I., Shipitko O. S., Korobov N. S., Grigoryev A. S..
Bezopasnoe upravlenie skorostyu nazemnogo bespilotnogo transportnogo sredstva v usloviyakh neopredelennosti sobstvennogo polozheniya
[Safe speed control of unmanned ground vehicle under ego-position uncertainty].
*Sensornye sistemy* [Sensory systems].
2019.
V. 33(3).
P. 222-237
(in Russian). doi: 10.1134/S0235009219030041

## References:

- Abramov M.P., Shipitko O.S., Lukoyanov A.S., Panfilova E.I., Kunina I.A., Grigoryev A.S. Sistema pozitsionirovaniya vnutri zdanii mobilnoi robototekhnicheskoi platformy na osnove detektsii kraev [Edge detection based mobile robot indoor localization]. Sensornye sistemy [Sensory systems]. 2019. V. 33 (1). P. 30–43 (in Russian). DOI: 10.1134/S0235009219010025
- Annell S., Gratner A., Svensson L. Probabilistic collision estimation system for autonomous vehicles. IEEE 19th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2016. P. 473–478
- Bopardikar S.D., Englot B., Speranzon A. Multiobjective path planning: Localization constraints and collision probability. IEEE Transactions on Robotics. 2015. V. 31 (3). P. 562–577.
- Broadhurst A., Baker S., Kanade T. Monte Carlo road safety reasoning. IEEE Proceedings. Intelligent Vehicles Symposium. IEEE, 2005. P. 319–324.
- Du Toit N.E., Burdick J.W. Probabilistic collision checking with chance constraints. IEEE Transactions on Robotics. 2011. V. 27 (4). P. 809–815.
- Harper C.D., Hendrickson C.T., Samaras C. Cost and benefit estimates of partially-automated vehicle collision avoidance technologies. Accident Analysis & Prevention. 2016. V. 95. P. 104–115.
- Hoffmann G.M.,Tomlin C.J., Montemerlo M., Thrun S. Autonomous automobile trajectory tracking for offroad driving: Controller design, experimental validation and racing. American Control Conference. IEEE, 2007. P. 2296–2301.
- Houénou A., Bonnifait P., Cherfaoui V. Risk assessment for collision avoidance systems. 17th International IEEE Conference on Intelligent Transportation Systems (ITSC). IEEE, 2014. P. 386–391.
- Hu X., Chen L., Tang B., Cao D., He H. Dynamic path planning for autonomous driving on various roads with avoidance of static and moving obstacles. Mechanical Systems and Signal Processing. 2018. V. 100. P. 482–500
- Janson L., Schmerling E., Pavone M. Monte Carlo motion planning for robot trajectory optimization under uncertainty. Robotics Research. Springer, Cham, 2018. P.343–361.
- Kuffner Jr J.J., LaValle S.M. RRT-connect: An efficient approach to single-query path planning. ICRA. 2000. V.2.
- Lambert A., Gruyer D., Saint Pierre G.A fast Monte Carlo algorithm for collision probability estimation. 10th International Conference on Control, Automation, Robotics and Vision. IEEE, 2008 . P. 406–411.
- Lambert A., Gruyer D., Pierre G.S., Ndjeng A.N. Collision probability assessment for speed control. 11th International IEEE Conference on Intelligent Transportation Systems. IEEE, 2008b. P. 1043–1048.
- Liu P., Yang R., Xu Z. How Safe Is Safe Enough for Self-Driving Vehicles? Risk analysis. 2018. V. 39(2). P. 315–325.
- Liu W., Ang M.H. Incremental sampling-based algorithm for risk-aware planning under motion uncertainty. IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2014. P. 2051–2058.
- Mehta D., Ferrer G., Olson E. C-MPDM: Continuously-parameterized risk-aware MPDM by quickly discovering contextual policies. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018. P. 7547–7554
- Patil S., Van Den Berg J., Alterovitz R. Estimating probability of collision for safe motion planning under Gaussian motion and sensing uncertainty. IEEE International Conference on Robotics and Automation. IEEE, 2012. P. 3238–3244
- Schmerling E., Pavone M. Evaluating trajectory collision probability through adaptive importance sampling for safe motion planning. arXiv preprint arXiv:1609.05399. 2016
- Schreier M., Willert V., Adamy J. An integrated approach to maneuver-based trajectory prediction and criticality assessment in arbitrary road environments. IEEE Transactions on Intelligent Transportation Systems. 2016. V. 17 (10). P. 2751–2766
- Thrun S. Particle filters in robotics. Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc., 2002. P.511–518.
- Thrun S., Montemerlo M., Dahlkamp H., et al. Stanley: The robot that won the DARPA Grand Challenge. Journal of field Robotics. 2006. V. 23 (9). P. 661–692.
- Van Den Berg J., Abbeel P., Goldberg K. LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information. The International Journal of Robotics Research. 2011. V. 30 (7). P. 895–913.
- Vitus M.P., Tomlin C.J. Closed-loop belief space planning for linear, Gaussian systems. IEEE International Conference on Robotics and Automation. IEEE, 2011. P.2152–2159.