Dynamic path planning algorithm for autonomous mobile robot with a minimum number of turns in unknown environment

Grigorij E. Rego; Roman V. Voronov

doi:10.21638/11701/spbu10.2023.211

Authors

Grigorij E. Rego Petrozavodsk State University, 33, Lenina pr., Petrozavodsk, 185910, Russian Federation
Roman V. Voronov Petrozavodsk State University, 33, Lenina pr., Petrozavodsk, 185910, Russian Federation

DOI:

https://doi.org/10.21638/11701/spbu10.2023.211

Abstract

The article is devoted to the problem of reactive navigation of a mobile robot with limited information about the environment. An algorithm for finding a path from source to the target with a minimum number of turns is described. The idea of the algorithm is based on the bug family of algorithms for reactive navigation. The mobile robot remembers the boundaries of obstacles and calculates the angle of rotation depending on the surrounding situation. The difference from bug algorithms is that the robot does not move “along the obstacle”, but turns only in a limited number of cases. The results of testing the algorithm on simulated polygons are presented. Models of fallen trees, stumps and swamps were considered as obstacles. The performance of the algorithm is evaluated by comparing the minimum possible number of turns with the number of turns in the path obtained using the algorithm.

Keywords:

path planning, mobile robot, reactive navigation

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References

References

Alatise M. B., Hancke G. P. A review on challenges of autonomous mobile robot and sensor fusion methods. IEEE Access, 2020, vol. 8, pp. 39830-39846.

Tang L., Yuta S. Indoor navigation for mobile robots using memorized omni-directional images and robot’s motion. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002, vol. 1, pp. 269-274.

Sukkarieh S., Nebot E. M., Durrant-Whyte H. F. A high integrity IMU/GPS navigation loop for autonomous land vehicle applications. IEEE Transactions on Robotics and Automation, 1999, vol. 15, no. 3, pp. 572-578.

Pardhasaradhi B., Reddy Y. R., Cenkeramaddi L. R. Machine learning-based screening and measurement to measurement association for navigation in GNSS spoofing environment. IEEE Sensors Journal, 2022, vol. 22, no. 23, pp. 23423-23435.

Li X., Song B., Shen Z., Zhou Y., Lyu H., Qin Z. Consistent localization for autonomous robots with inter-vehicle GNSS information fusion. IEEE Communications Letters, 2022, pp. 120-124.

Smith R. C., Cheeseman P. On the representation and estimation of spatial uncertainty. The International Journal of Robotics Research, 1986, vol. 5, no. 4, pp. 56-68.

Smith R. C., Self M., Cheeseman P. Estimating uncertain spatial relationships in robotics. Autonomous robot vehicles. New York, Springer Pubh, 1990, pp. 167-193.

Leonard J. J., Durrant-Whyte H. F. Simultaneous map building and localization for an autonomous mobile robot. Proceedings of Intelligent Robots and Systems. Intelligence for Mechanical Systems, 1991, vol. 3, pp. 1442-1447.

Chen Y., Zhou Y., Lv Q., Deveerasetty К. K. A review of V-SLAM. IEEE International Conference on Information and Automation (ICIA), 2018, pp. 603-608.

Huang L. Review on LiDAR-based SLAM techniques. International Conference on Signal Processing and Machine Learning (CONF-SPML), 2021, pp. 163-168.

Hidalgo F., Braunl T. Review of underwater SLAM techniques. 6th International Conference on Automation, Robotics and Applications (ICARA), 2015, pp. 306-311.

Lumelsky V. J., Stepanov A. A. Dynamic path planning for a mobile automaton with limited information on the environment. IEEE Transactions on Automatic Control, 1986, vol. 31, pp. 1058-1063.

Ng J., Braunl T. Performance comparison of bug navigation algorithms. Journal of Intelligent and Robotic Systems, 2007, vol. 50, no. 1, pp. 73-84.

Galaktionov O., Zavyalov S., Shchegoleva L., Korzun D. Features of building a forestry intelligent robotic system. Proceedings oh th1’1 Conference of Open Innovations Association (FRUCT), 2021, pp. 433436.

Schonauer M., Prinz R., Vaatainen K., Astrup R., Pszenny D., Lindeman H., Jaeger D. Spatio-temporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals. International Journal of Applied Earth Observation and Geoinformation, 2022, vol. 108, pp. 102730.

Galaktionov O. N., Kuznetsov A. V. Reduction of negative impact of skidders on the forest environment. Astra Salvensis, 2018, pp. 381-390.

Mitchell J. S. B., Rote G. Minimum-link paths among obstacles in the plane. Proceedings of the Sixth annual Symposium on Computational Geometry, 1990, pp. 63-72.