Abstract
This paper proposes an improved autonomous exploration method for complex environments. Firstly, based on SLAM, a hierarchical strategy is used to incrementally construct frontiers in a 3D occupancy map, and the mean-shift algorithm is used to cluster these frontiers to obtain candidate target points. Simultaneously, based on the input of the submap point cloud, the visibility graph is dynamically updated, and its vertices and edges are simplified. Secondly, an evaluation function that includes expected information gain and movement cost is adopted to select the target point. This function can better balance the relationship between information acquisition and cost consumption in different scenes through nonlinear adjustment. Furthermore, paths are planned on the visibility graph to guide the robot to explore the unknown environment quickly and avoid duplicate paths. Simulation experiment results show that compared with NBVP, our method reduces running time by 68% and travel distance by 38.6%, with the completion rate of NBVP being only 0.6. In addition, in the real-world scene, our method can also efficiently complete the exploration task. These results indicate that the algorithm effectively addresses the problems of overlooking local narrow areas and high path redundancy, thereby improving the efficiency of robot autonomous exploration.
This work was supported in part by the National Natural Science Foundation of China under Grant 62373221, in part by the Shandong Provincial Natural Science Foundation for Distinguished Young Scholars under Grant ZR2022JQ28, in part by the Tianjin Science and Technology Plan Project under Grant 23ZGCXQY00030 and in part by the Enterprise Project under Grant DQJ-2022-A03.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Delmerico, J., Mintchev, S., Giusti, A., et al.: The current state and future outlook of rescue robotics. J. Field Robot. 36(7), 1171–1191 (2019)
Shen, C.S., Zhang, Y.Z., Li, Z.M., et al.: Collaborative air-ground target searching in complex environments. In: 2017 IEEE International Symposium on Safety, Security and Rescue Robotics, pp. 230–237 (2017)
Dai, A. N., Papatheodorou, S., Funk, N., et al.: Fast frontier-based information-driven autonomous exploration with an MAV. In: 2020 IEEE International Conference on Robotics and Automation, pp. 9570–9576 (2020)
Yamauchi, B.: A frontier-based approach for autonomous exploration. In: Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 146–151 (1997)
Cieslewski, T., Kaufmann, E., Scaramuzza, D.: Rapid exploration with multi-rotors: a frontier selection method for high-speed flight. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2135–2142 (2017)
Bircher, A., Kamel, M., Alexis, K., et al.: Receding horizon next-best-view planner for 3D exploration. In: 2016 IEEE International Conference on Robotics and Automation, pp. 1462–1468 (2016)
Wang, C.Q., Ma, H., Chen, W.N., et al.: Efficient autonomous exploration with incrementally built topological map in 3-D environments. IEEE Trans. Instrum. Meas. 69(12), 9853–9865 (2020)
Meng, Z.H., Qin, H.L., Chen, Z.Y., et al.: A two-stage optimized next-view planning framework for 3-D unknown environment exploration, and structural reconstruction. IEEE Robot. Autom. Lett. 2(3), 1680–1687 (2017)
Selin, M., Tiger, M., Duberg, D., et al.: Efficient autonomous exploration planning of large-scale 3-D environments. IEEE Robot. Autom. Lett. 4(2), 1699–1706 (2019)
Shan, T., Englot, B.: LeGO-LOAM: lightweight and ground-optimized Lidar Odometry and mapping on variable terrain. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4758–4765 (2018)
Gao, H.M., Zhang, X.B., Wen, J., et al.: Autonomous indoor exploration via polygon map construction and graph-based SLAM using directional endpoint features. IEEE Trans. Autom. Sci. Eng. 16(4), 1531–1542 (2019)
Yu, X., Wang, J., Chen, W., et al.: A map accessibility analysis algorithm for mobile robot navigation in outdoor environment. In: 2019 IEEE International Conference on Real-time Computing and Robotics, pp. 475–480 (2019)
Suzuki, S., Abe, K.: Topological structural analysis of digitized binary images by border following. In: Computer Vision, Graphics, and Image Processing, pp. 32–46 (1985)
Xie, Z., Wang, H., Wu, L.: The improved Douglas-Peucker algorithm based on the contour character. In: 2011 IEEE 19th International Conference on Geoinformatics, pp. 1–5 (2011)
Zhu, C., Ding, R., Lin, M., et al.: A 3D frontier-based exploration tool for MAVs. In: 2015 IEEE 27th International Conference on Tools with Artificial Intelligence, pp. 348–352 (2015)
Bircher, A., Kamel, M., Alexis, K., et al.: Three-dimensional coverage path planning via viewpoint resampling and tour optimization for aerial robots. Auton. Robot. 40(6), 1059–1078 (2016)
Xidias, E., Zissis, D.: Real time autonomous maritime navigation using dynamic visibility graphs. In: 2018 IEEE/OES Autonomous Underwater Vehicle Workshop, pp. 1–6 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Cao, L., Zhou, L., Dai, X., Liu, Y., Li, Y. (2025). Efficient Autonomous Exploration of Complex Environments Based on the Mobile Robot. In: Lan, X., Mei, X., Jiang, C., Zhao, F., Tian, Z. (eds) Intelligent Robotics and Applications. ICIRA 2024. Lecture Notes in Computer Science(), vol 15202. Springer, Singapore. https://doi.org/10.1007/978-981-96-0774-7_24
Download citation
DOI: https://doi.org/10.1007/978-981-96-0774-7_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-96-0773-0
Online ISBN: 978-981-96-0774-7
eBook Packages: Computer ScienceComputer Science (R0)