Hostname: page-component-6bb9c88b65-s7dlb Total loading time: 0 Render date: 2025-07-25T03:46:23.720Z Has data issue: false hasContentIssue false
  • English
  • Français

SEAC: a simultaneous exploration and coverage planner for online aerial 3-D modeling

Published online by Cambridge University Press:  22 July 2025

Shiyong Zhang Open the ORCID record for Shiyong Zhang [Opens in a new window] ,
Xuebo Zhang ,
Qianli Dong ,
Tianyi Li ,
Haobo Xi ,
Ziyu Wang ,
Chaoqun Wang  and
Jingjin Yu Open the ORCID record for Jingjin Yu [Opens in a new window]
Shiyong Zhang
Affiliation:
Institute of Robotics and Automatic Information System, Nankai University, Tianjin, China
Xuebo Zhang*
Affiliation:
Institute of Robotics and Automatic Information System, Nankai University, Tianjin, China
Qianli Dong
Affiliation:
Institute of Robotics and Automatic Information System, Nankai University, Tianjin, China
Tianyi Li
Affiliation:
Institute of Robotics and Automatic Information System, Nankai University, Tianjin, China
Haobo Xi
Affiliation:
Institute of Robotics and Automatic Information System, Nankai University, Tianjin, China
Ziyu Wang
Affiliation:
Institute of Robotics and Automatic Information System, Nankai University, Tianjin, China
Chaoqun Wang
Affiliation:
School of Control Science and Engineering, Shandong University, Jinan, China
Jingjin Yu
Affiliation:
Department of Computer Science, Rutgers, the State University of New Jersey, Piscataway, NJ, USA
*
Corresponding author: Xuebo Zhang; Email: zhangxuebo@nankai.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

In this paper, we propose a novel online informative path planner for 3-D modeling of unknown structures using micro aerial vehicles. Different from the explore-then-exploit strategy, our planner can cope with exploration and coverage simultaneously and thus obtain complete and high-quality 3-D models. We first devise a set of evaluation metrics considering the perception constraints of the sensor for efficiently evaluating the coverage quality of the reconstructed surfaces. Then, the coverage quality is utilized to guide the subsequent informative path planning. Specifically, our hierarchical planner consists of two planning stages – a local coverage stage for inspecting surfaces with low coverage quality and a global exploration stage for transiting the robot to unexplored regions at the global scale. The local coverage stage computes the coverage path that takes into account both the exploration and coverage objectives based on the estimated coverage quality and frontiers, and the global exploration stage maintains a sparse roadmap in the explored space to achieve fast global exploration. We conduct both simulated and real-world experiments to validate the proposed method. The results show that our planner outperforms the state-of-the-art algorithms and especially decreases the reconstruction error (at least 12.5% lower on average).

Keywords

micro aerial vehicleinformative path planningautonomous explorationonline 3-D modeling

Information

Type
Research Article
Information
Robotica , First View , pp. 1 - 17
Copyright
© The Author(s), 2025. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Zhang, S., Zhang, X., Li, T., Yuan, J. and Fang, Y., “Fast active aerial exploration for traversable path finding of ground robots in unknown environments,” IEEE Trans. Instrum. Meas. 71, 1–13 (2022).Google Scholar
Li, H., Savkin, A. V. and Vucetic, B., “Autonomous area exploration and mapping in underground mine environments by unmanned aerial vehicles,” Robotica 38(3), 442–456 (2020).10.1017/S0263574719000754CrossRefGoogle Scholar
Tabib, W., Goel, K., Yao, J., Boirum, C. and Michael, N., “Autonomous cave surveying with an aerial robot,” IEEE Trans. Robot. 38(2), 1016–1032 (2022).10.1109/TRO.2021.3104459CrossRefGoogle Scholar
Yuan, J., Huang, Y., Sun, F. and Tao, T., “Active exploration using a scheme for autonomous allocation of landmarks,” Robotica 32(5), 757–782 (2014).10.1017/S0263574713001033CrossRefGoogle Scholar
Jing, W., Deng, D., Xiao, Z., Liu, Y. and Shimada, K.. “Coverage Path Planning Using Path Primitive Sampling and Primitive Coverage Graph for Visual Inspection.” In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2019) pp. 1472–1479.Google Scholar
Song, H., Yu, J., Qiu, J., Sun, Z., Lang, K., Luo, Q., Shen, Y. and Wang, Y.. “>Multi-UAV Disaster Environment Coverage Planning with Limited-Endurance.” In: 2022 International Conference on Robotics and Automation (ICRA) (2022) pp. 10760–10766.Multi-UAV+Disaster+Environment+Coverage+Planning+with+Limited-Endurance.”+In:+2022+International+Conference+on+Robotics+and+Automation+(ICRA)+(2022)+pp.+10760–10766.>Google Scholar
Tang, J. and Ma, H., “Mixed integer programming for time-optimal multi-robot coverage path planning with efficient heuristics,” IEEE Robot. Autom. Lett. 8(10), 6491–6498 (2023).10.1109/LRA.2023.3306996CrossRefGoogle Scholar
Delmerico, J., Mintchev, S., Giusti, A., Gromov, B., Melo, K., Horvat, T., Cadena, C., Hutter, M., Ijspeert, A., Floreano, D., Gambardella, L. M., Siegwart, R. and Scaramuzza, D., “The current state and future outlook of rescue robotics,” J. Field Robot. 36(7), 1171–1191 (2019).10.1002/rob.21887CrossRefGoogle Scholar
Bircher, A., Kamel, M., Alexis, K., Oleynikova, H. and Siegwart, R., “Receding horizon path planning for 3D exploration and surface inspection,” Auton. Robot. 42(2), 291–306 (2018).10.1007/s10514-016-9610-0CrossRefGoogle Scholar
Respall, V. M., Devitt, D., Fedorenko, R. and Klimchik, A.. “Fast Sampling-based Next-Best-View Exploration Algorithm for an MAV.” In: 2021 IEEE International Conference on Robotics and Automation (ICRA) (2021) pp. 89–95.Google Scholar
Tao, Y., Wu, Y., Li, B., Cladera, F., Zhou, A., Thakur, D. and Kumar, V.. “SEER: Safe Efficient Exploration for Aerial Robots Using Learning to Predict Information Gain.” In: 2023 IEEE International Conference on Robotics and Automation (ICRA) (2023) pp. 1235–1241.Google Scholar
Zhao, Y., Yan, L., Xie, H., Dai, J. and Wei, P., “Autonomous exploration method for fast unknown environment mapping by using UAV equipped with limited FOV sensor,” IEEE Trans. Ind. Electron. 71(5), 4933–4943 (2024).10.1109/TIE.2023.3285921CrossRefGoogle Scholar
Zhou, B., Zhang, Y., Chen, X. and Shen, S., “FUEL: Fast UAV exploration using incremental frontier structure and hierarchical planning,” IEEE Robot. Autom. Lett. 6(2), 779–786 (2021).10.1109/LRA.2021.3051563CrossRefGoogle Scholar
Yu, J., Shen, H., Xu, J. and Zhang, T., “ECHO: An efficient heuristic viewpoint determination method on frontier-based autonomous exploration for quadrotors,” IEEE Robot. Autom. Lett. 8(8), 5047–5054 (2023).10.1109/LRA.2023.3282783CrossRefGoogle Scholar
Sharma, M. and Voruganti, H. K., “Multi-objective optimization approach for coverage path planning of mobile robot,” Robotica 42(7), 2125–2149 (2024).10.1017/S0263574724000377CrossRefGoogle Scholar
Wang, H., Zhang, S., Zhang, X., Zhang, X. and Liu, J., “Near-optimal 3-D visual coverage for quadrotor unmanned aerial vehicles under photogrammetric constraints,” IEEE Trans. Ind. Electron. 69(2), 1694–1704 (2022).10.1109/TIE.2021.3060643CrossRefGoogle Scholar
Roberts, M., Shah, S., Dey, D., Truong, A., Sinha, S., Kapoor, A., Hanrahan, P. and Joshi, N.. “Submodular Trajectory Optimization for Aerial 3D Scanning.” In: 2017 IEEE International Conference on Computer Vision (ICCV) (2017) pp. 5334–5343.Google Scholar
Liu, J., Wang, C., Chi, W., Chen, G. and Sun, L., “Estimated path information gain-based robot exploration under perceptual uncertainty,” Robotica 40(8), 2748–2764 (2022).10.1017/S0263574721001946CrossRefGoogle Scholar
Chi, W., Wang, J., Ding, Z., Chen, G. and Sun, L., “A reusable generalized voronoi diagram-based feature tree for fast robot motion planning in trapped environments,” IEEE Sens. J. 22(18), 17615–17624 (2022).10.1109/JSEN.2021.3054888CrossRefGoogle Scholar
Cao, C., Zhu, H., Choset, H. and Zhang, J.. “TARE: A hierarchical framework for efficiently exploring complex 3D environments,” In: Paper presented at the Robotics: Science and Systems Conference (RSS), Virtual, 12 to 16 July 2021 (2021).Google Scholar
Heng, L., Gotovos, A., Krause, A. and Pollefeys, M.. “Efficient Visual Exploration and Coverage with a Micro Aerial Vehicle in Unknown Environments.” In: 2015 IEEE International Conference on Robotics and Automation (ICRA) (2015) pp. 1071–1078.Google Scholar
Vutetakis, D. G. and Xiao, J.. “An Autonomous Loop-closure Approach for Simultaneous Exploration and Coverage of Unknown Infrastructure Using MAVs.” In: 2019 International Conference on Robotics and Automation (ICRA) (2019) pp. 2988–2994.Google Scholar
Song, S. and Jo, S.. “Surface-based Exploration for Autonomous 3D Modeling.” In: 2018 IEEE International Conference on Robotics and Automation (ICRA) (2018) pp. 4319–4326.Google Scholar
Feng, C., Li, H., Gao, F., Zhou, B. and Shen, S.. “PredRecon: A Prediction-boosted Planning Framework for Fast and High-quality Autonomous Aerial Reconstruction.” In: 2023 IEEE International Conference on Robotics and Automation (ICRA) (2023) pp. 1207–1213.Google Scholar
Zhang, X., Xu, X., Liu, Y., Wang, H., Zhang, X. and Zhuang, Y., “FGIP: A frontier-guided informative planner for UAV exploration and reconstruction,” IEEE Trans. Ind. Inform. 20(4), 6155–6166 (2024).10.1109/TII.2023.3342457CrossRefGoogle Scholar
Schmid, L., Pantic, M., Khanna, R., Ott, L., Siegwart, R. and Nieto, J., “An efficient sampling-based method for online informative path planning in unknown environments,” IEEE Robot. Autom. Lett. 5(2), 1500–1507 (2020).10.1109/LRA.2020.2969191CrossRefGoogle Scholar
Zhang, M., Feng, C., Li, Z., Zheng, G., Luo, Y., Wang, Z., Zhou, J., Shen, S. and Zhou, B.. “SOAR: Simultaneous exploration and photographing with heterogeneous UAVs for fast autonomous reconstruction.” In: 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2024) pp. 10975–10982.Google Scholar
Faessler, M., Franchi, A. and Scaramuzza, D., “Differential flatness of quadrotor dynamics subject to rotor drag for accurate tracking of high-speed trajectories,” IEEE Robot. Autom. Lett. 3(2), 620–626 (2018).10.1109/LRA.2017.2776353CrossRefGoogle Scholar
Jiang, F., Zhang, X., Chen, X. and Fang, Y., “Distributed optimization of visual sensor networks for coverage of a large-scale 3-D scene,” IEEE/ASME Trans. Mechatron. 25(6), 2777–2788 (2020).10.1109/TMECH.2020.2993573CrossRefGoogle Scholar
Oleynikova, H., Taylor, Z., Fehr, M., Siegwart, R. and Nieto, J.. “Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-board MAV Planning.” In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2017) pp. 1366–1373.Google Scholar
Lee, T., Leok, M. and McClamroch, N. H.. “Geometric Tracking Control of a Quadrotor UAV on SE(3).” In: 49th IEEE Conference on Decision and Control (CDC) (2010) pp. 5420–5425.Google Scholar
Jung, S., Song, S., Youn, P. and Myung, H.. “Multi-layer Coverage Path Planner for Autonomous Structural Inspection of High-rise Structures.” In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2018) pp. 1–9.Google Scholar
Vansteenwegen, P., Souffriau, W. and Oudheusden, D. V., “The orienteering problem: A survey,” Eur. J. Oper. Res. 209(1), 1–10 (2011).10.1016/j.ejor.2010.03.045CrossRefGoogle Scholar
Hollinger, G. A. and Sukhatme, G. S., “Sampling-based robotic information gathering algorithms,” Int. J. Robot. Res. 33(9), 1271–1287 (2014).10.1177/0278364914533443CrossRefGoogle Scholar
Gazebo. [Online]. http://gazebosim.org/. Accessed: May, 2025.Google Scholar
Furrer, F., Burri, M., Achtelik, M. and Siegwart, R., “Rotors—A Modular Gazebo Mav Simulator Framework,” In: Robot Operating System (ROS): The Complete Reference, vol. 1 (Springer, Cham, 2016) pp. 595–625.10.1007/978-3-319-26054-9_23CrossRefGoogle Scholar