Abstract
Exploration and mapping of unknown environments is a fundamental task in applications for autonomous robots. In this article, we present a complete framework for deploying Micro Aerial Vehicles (MAVs) in autonomous exploration missions in unknown subterranean areas. The main motive of exploration algorithms is to depict the next best frontier for the MAV such that new ground can be covered in a fast, safe yet efficient manner. The proposed framework uses a novel frontier selection method that also contributes to the safe navigation of autonomous MAVs in obstructed areas such as subterranean caves, mines, and urban areas. The framework presented in this work bifurcates the exploration problem in local and global exploration. The proposed exploration framework is also adaptable according to computational resources available onboard the MAV which means the trade-off between the speed of exploration and the quality of the map can be made. Such capability allows the proposed framework to be deployed in subterranean exploration and mapping as well as in fast search and rescue scenarios. The performance of the proposed framework is evaluated in detailed simulation studies with comparisons made against a high-level exploration-planning framework developed for the DARPA Sub-T challenge as it will be presented in this article.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Agha, A., Otsu, K., Morrell, B., Fan, D.D., Thakker, R., Santamaria-Navarro, A., Kim, S.K., Bouman, A., Lei, X., Edlund, J., et al.: Nebula: Quest for robotic autonomy in challenging environments; team costar at the darpa subterranean challenge. arXiv:2103.11470 (2021)
Ahmad, S., Mills, A.B., Rush, E.R., Frew, E.W., Humbert, J.S.: 3d reactive control and frontier-based exploration for unstructured environments. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2289–2296. IEEE (2021)
Akash, P.: Mars lava tube world. https://github.com/LTU-RAI/MarsLavaTubeWorld.git (2021)
Bircher, A., Kamel, M., Alexis, K., Oleynikova, H., Siegwart, R.: Receding horizon "next-best-view" planner for 3d exploration. In: 2016 IEEE international conference on robotics and automation (ICRA), pp. 1462–1468. IEEE (2016)
Brunel, A., Bourki, A., Demonceaux, C., Strauss, O.: Splatplanner: Efficient autonomous exploration via permutohedral frontier filtering. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 608–615. IEEE (2021)
Bucki, N., Lee, J., Mueller, M.W.: Rectangular pyramid partitioning using integrated depth sensors (rappids): A fast planner for multicopter navigation. IEEE Robotics and Automation Letters 5(3), 4626–4633 (2020)
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 (IROS), pp. 2135–2142. IEEE (2017)
Dai, A., Papatheodorou, S., Funk, N., Tzoumanikas, D., Leutenegger, S.: Fast frontier-based information-driven autonomous exploration with an mav. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 9570–9576. IEEE (2020)
Dang, T., Mascarich, F., Khattak, S., Nguyen, H., Nguyen, H., Hirsh, S., Reinhart, R., Papachristos, C., Alexis, K.: Autonomous search for underground mine rescue using aerial robots. In: 2020 IEEE Aerospace Conference, pp. 1–8. IEEE (2020)
Dang, T., Mascarich, F., Khattak, S., Papachristos, C., Alexis, K.: Graph-based path planning for autonomous robotic exploration in subterranean environments. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3105–3112. IEEE (2019)
DARPA: DARPA Subterranean (SubT) challenge (2020). https://www.darpa.mil/program/darpa-subterranean-challenge. Accessed: February 2021
Dharmadhikari, M., Dang, T., Solanka, L., Loje, J., Nguyen, H., Khedekar, N., Alexis, K.: Motion primitives-based path planning for fast and agile exploration using aerial robots. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 179–185. IEEE (2020)
Dirik, M., Kocamaz, A.F., Dönmez, E.: Visual servoing based control methods for non-holonomic mobile robot. Journal of Engineering Research 8(2) (2020)
Dönmez, E., Kocamaz, A.F.: Design of mobile robot control infrastructure based on decision trees and adaptive potential area methods. Iranian Journal of Science and Technology, Transactions of Electrical Engineering 44(1), 431–448 (2020)
Dönmez, E., Kocamaz, A.F., Dirik, M.: Bi-rrt path extraction and curve fitting smooth with visual based configuration space mapping. In: 2017 international artificial intelligence and data processing symposium (IDAP), pp. 1–5. IEEE (2017)
Dönmez, E., Kocamaz, A.F., Dirik, M.: A vision-based real-time mobile robot controller design based on gaussian function for indoor environment. Arabian Journal for Science and Engineering 43(12), 7127–7142 (2018)
Faria, M., Maza, I., Viguria, A.: Applying frontier cells based exploration and lazy theta* path planning over single grid-based world representation for autonomous inspection of large 3d structures with an uas. J. Intell. Robotic Syst. 93(1–2), 113–133 (2019)
Florence, P.R., Carter, J., Ware, J., Tedrake, R.: Nanomap: Fast, uncertainty-aware proximity queries with lazy search over local 3d data. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 7631–7638. IEEE (2018)
Fraundorfer, F., Heng, L., Honegger, D., Lee, G.H., Meier, L., Tanskanen, P., Pollefeys, M.: Vision-based autonomous mapping and exploration using a quadrotor mav. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4557–4564. IEEE (2012)
Furrer, F., Burri, M., Achtelik, M., Siegwart, R.: Robot Operating System (ROS): The Complete Reference (Volume 1), chap. RotorS—A Modular Gazebo MAV Simulator Framework, pp. 595–625. Springer International Publishing, Cham (2016). 10.1007/978-3-319-26054-9_23
Holz, D., Basilico, N., Amigoni, F., Behnke, S.: Evaluating the efficiency of frontier-based exploration strategies. In: ISR 2010 (41st International Symposium on Robotics) and ROBOTIK 2010 (6th German Conference on Robotics), pp. 1–8. VDE (2010)
Hornung, A., Wurm, K.M., Bennewitz, M., Stachniss, C., Burgard, W.: Octomap: An efficient probabilistic 3d mapping framework based on octrees. Autonomous robots 34(3), 189–206 (2013)
Juliá, M., Gil, A., Reinoso, O.: A comparison of path planning strategies for autonomous exploration and mapping of unknown environments. Autonomous Robots 33(4), 427–444 (2012)
Kanellakis, C., Mansouri, S.S., Castaño, M., Karvelis, P., Kominiak, D., Nikolakopoulos, G.: Where to look: a collection of methods formav heading correction in underground tunnels. IET Image Processing 14(10) (2020)
Karlsson, S., Koval, A., Kanellakis, C., Nikolakopoulos, G.: \(d^{*}_{+}\): A risk aware platform agnostic heterogeneous path planner. Expert systems with applications p. 119408 (2022)
Kim, S.K., Bouman, A., Salhotra, G., Fan, D.D., Otsu, K., Burdick, J., Agha-mohammadi, A.a.: Plgrim: Hierarchical value learning for large-scale exploration in unknown environments. In: Proceedings of the International Conference on Automated Planning and Scheduling, vol. 31, pp. 652–662 (2021)
Koval, A., Kanellakis, C., Vidmark, E., Haluska, J., Nikolakopoulos, G.: A subterranean virtual cave world for gazebobased on the darpa subt challenge. http://arxiv.org/abs/2004.08452 (2020)
Lindqvist, B., Agha-mohammadi, A.a., Nikolakopoulos, G.: Exploration-rrt: A multi-objective path planning and exploration framework for unknown and unstructured environments. arXiv:2104.03724 (2021)
Lindqvist, B., Haluska, J., Kanellakis, C., Nikolakopoulos, G.: An adaptive 3d artificial potential field for fail-safe uav navigation. In: 2022 30th Mediterranean Conference on Control and Automation (MED), pp. 362–367. IEEE (2022)
Lindqvist, B., Kanellakis, C., Mansouri, S.S., akbar Agha-mohammadi, A., Nikolakopoulos, G.: Compra: A compact reactive autonomy framework for subterranean mav based search-and-rescue operations (2021)
Lindqvist, B., Mansouri, S.S., Agha-mohammadi, A.a., Nikolakopoulos, G.: Nonlinear mpc for collision avoidance and control of uavs with dynamic obstacles. IEEE Robotics and Automation Letters 5(4), 6001–6008 (2020)
Lindqvist, B., Mansouri, S.S., Haluška, J., Nikolakopoulos, G.: Reactive navigation of an unmanned aerial vehicle with perception-based obstacle avoidance constraints. IEEE Transactions on Control Systems Technology (2021)
Mansouri, S.S., Kanellakis, C., Fresk, E., Kominiak, D., Nikolakopoulos, G.: Cooperative uavs as a tool for aerial inspection of the aging infrastructure. In: Field and Service Robotics, pp. 177–189. Springer (2018)
Matthies, L., Brockers, R., Kuwata, Y., Weiss, S.: Stereo vision-based obstacle avoidance for micro air vehicles using disparity space. In: 2014 IEEE international conference on robotics and automation (ICRA), pp. 3242–3249. IEEE (2014)
Moravec, H., Elfes, A.: High resolution maps from wide angle sonar. In: Proceedings. 1985 IEEE international conference on robotics and automation, vol. 2, pp. 116–121. IEEE (1985)
Nikolakopoulos, G., Agha, A.: Pushing the limits of autonomy for enabling the next generation of space robotics exploration missions. Computer 54(11), 100–103 (2021)
Özaslan, T., Loianno, G., Keller, J., Taylor, C.J., Kumar, V., Wozencraft, J.M., Hood, T.: Autonomous navigation and mapping for inspection of penstocks and tunnels with mavs. IEEE Robotics and Automation Letters 2(3), 1740–1747 (2017)
Patel, A., Banerjee, A., Lindqvist, B., Kanellakis, C., Nikolakopoulos, G.: Design and model predictive control of mars coaxial quadrotor (2021)
Patel, A., Banerjee, A., Lindqvist, B., Kanellakis, C., Nikolakopoulos, G.: Design and model predictive control of mars coaxial quadrotor. arXiv:2109.06810 (2021)
Patel, A., Lindqvist, B., Kanellakis, C., Nikolakopoulos, G.: Fast planner for mav navigation in unknown environments based on adaptive search of safe look-ahead poses. In: 2022 30th Mediterranean Conference on Control and Automation (MED), pp. 545–550 (2022). 10.1109/MED54222.2022.9837293
Peltzer, O., Bouman, A., Kim, S.K., Senanayake, R., Ott, J., Delecki, H., Sobue, M., Kochenderfer, M., Schwager, M., Burdick, J., et al.: Fig-op: Exploring large-scale unknown environments on a fixed time budget. arXiv:2203.06316 (2022)
Pito, R.: A solution to the next best view problem for automated surface acquisition. IEEE Transactions on pattern analysis and machine intelligence 21(10), 1016–1030 (1999)
Reinhart, R., Dang, T., Hand, E., Papachristos, C., Alexis, K.: Learning-based path planning for autonomous exploration of subterranean environments. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 1215–1221. IEEE (2020)
Ryll, M., Ware, J., Carter, J., Roy, N.: Efficient trajectory planning for high speed flight in unknown environments. In: 2019 International conference on robotics and automation (ICRA), pp. 732–738. IEEE (2019)
Selin, M., Tiger, M., Duberg, D., Heintz, F., Jensfelt, P.: Efficient autonomous exploration planning of large-scale 3-d environments. IEEE Robotics and Automation Letters 4(2), 1699–1706 (2019)
Shen, S., Michael, N., Kumar, V.: Autonomous indoor 3d exploration with a micro-aerial vehicle. In: 2012 IEEE international conference on robotics and automation, pp. 9–15. IEEE (2012)
Small, E., Sopasakis, P., Fresk, E., Patrinos, P., Nikolakopoulos, G.: Aerial navigation in obstructed environments with embedded nonlinear model predictive control. In: 2019 18th European Control Conference (ECC), pp. 3556–3563. IEEE (2019)
Sopasakis, P., Fresk, E., Patrinos, P.: Open: Code generation for embedded nonconvex optimization. IFAC-PapersOnLine 53(2), 6548–6554 (2020)
Tordesillas, J., Lopez, B.T., Everett, M., How, J.P.: Faster: Fast and safe trajectory planner for navigation in unknown environments. IEEE Transactions on Robotics (2021)
Viswanathan, V.K., Satpute, S.G., Lindqvist, B., Kanellakis, C., Nikolakopoulos, G.: Experimental evaluation of a geometry-aware aerial visual inspection framework in a constrained environment. In: 2022 30th Mediterranean Conference on Control and Automation (MED), pp. 468–474. IEEE (2022)
Warren, C.W.: Global path planning using artificial potential fields. In: 1989 IEEE International Conference on Robotics and Automation, pp. 316–317. IEEE Computer Society (1989)
Williams, J., Jiang, S., O’Brien, M., Wagner, G., Hernandez, E., Cox, M., Pitt, A., Arkin, R., Hudson, N.: Online 3d frontier-based ugv and uav exploration using direct point cloud visibility. In: 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 263–270. IEEE (2020)
Xu, Z., Deng, D., Shimada, K.: Autonomous uav exploration of dynamic environments via incremental sampling and probabilistic roadmap. IEEE Robotics and Automation Letters 6(2), 2729–2736 (2021)
Yamauchi, B.: A frontier-based approach for autonomous exploration. In: Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA’97.’Towards New Computational Principles for Robotics and Automation’, pp. 146–151. IEEE (1997)
Yamauchi, B.: Frontier-based exploration using multiple robots. In: Proceedings of the second international conference on Autonomous agents, pp. 47–53 (1998)
Zhang, J., Hu, C., Chadha, R.G., Singh, S.: Maximum likelihood path planning for fast aerial maneuvers and collision avoidance. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2805–2812. IEEE (2019)
Zhou, B., Zhang, Y., Chen, X., Shen, S.: Fuel: Fast uav exploration using incremental frontier structure and hierarchical planning. IEEE Robotics and Automation Letters 6(2), 779–786 (2021)
Zhu, C., Ding, R., Lin, M., Wu, Y.: A 3d frontier-based exploration tool for mavs. In: 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 348–352. IEEE (2015)
Funding
This work has been partially funded by the European Unions Horizon 2020 Research and Innovation Programme under the Grant Agreement No. 869379 illuMINEation. Open access funding provided by Lulea University of Technology.
Author information
Authors and Affiliations
Contributions
Akash Patel: Development, implementation, and system integration, relating to all presented sub-modules and developments, main manuscript contributors. Björn Lindqvist: Control and obstacle avoidance modules advisory. Christoforos Kanellakis: Software integration and high-level advisory. Ali-Akbar Agha-Mohammadi: Advisory, development lead for Team CoSTAR in DARPA Sub-T Challenge. George Nikolakopoulos: Advisory, manuscript contributions, head of the Luleå University of Technology Robotics & AI Team. All authors have read and approved the manuscript.
Corresponding author
Ethics declarations
Ethics Approval
Not applicable.
Consent to Participate
Not applicable.
Consent to Publish
All authors comply with the consent to publish.
Conflict of Interest
The authors have no conflicts of interest with any related parties.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The REF exploration framework code will be publicly available at https://github.com/LTU-RAI/REF.git for the community.
The REF exploration framework code will be publicly available at https://github.com/LTU-RAI/REF.git for the community.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Patel, A., Lindqvist, B., Kanellakis, C. et al. REF: A Rapid Exploration Framework for Deploying Autonomous MAVs in Unknown Environments. J Intell Robot Syst 108, 35 (2023). https://doi.org/10.1007/s10846-023-01836-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10846-023-01836-z