Hostname: page-component-8448b6f56d-qsmjn Total loading time: 0 Render date: 2024-04-23T07:10:24.081Z Has data issue: false hasContentIssue false
  • English
  • Français

Estimated path information gain-based robot exploration under perceptual uncertainty

Published online by Cambridge University Press:  06 January 2022

Jie Liu ,
Chaoqun Wang ,
Wenzheng Chi Open the ORCID record for Wenzheng Chi [Opens in a new window] ,
Guodong Chen  and
Lining Sun
Jie Liu
Affiliation:
Robotics and Microsystems Center, School of Mechanical and Electric Engineering, Soochow University, Suzhou215021, China
Chaoqun Wang
Affiliation:
Department of Automation, Shandong University, Jinan, China
Wenzheng Chi*
Affiliation:
Robotics and Microsystems Center, School of Mechanical and Electric Engineering, Soochow University, Suzhou215021, China
Guodong Chen
Affiliation:
Robotics and Microsystems Center, School of Mechanical and Electric Engineering, Soochow University, Suzhou215021, China
Lining Sun
Affiliation:
Robotics and Microsystems Center, School of Mechanical and Electric Engineering, Soochow University, Suzhou215021, China
*
*Corresponding author. E-mail: wzchi@suda.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

At present, the frontier-based exploration has been one of the mainstream methods in autonomous robot exploration. Among the frontier-based algorithms, the method of searching frontiers based on rapidly exploring random trees consumes less computing resources with higher efficiency and performs well in full-perceptual scenarios. However, in the partially perceptual cases, namely when the environmental structure is beyond the perception range of robot sensors, the robot often lingers in a restricted area, and the exploration efficiency is reduced. In this article, we propose a decision-making method for robot exploration by integrating the estimated path information gain and the frontier information. The proposed method includes the topological structure information of the environment on the path to the candidate frontier in the frontier selection process, guiding the robot to select a frontier with rich environmental information to reduce perceptual uncertainty. Experiments are carried out in different environments with the state-of-the-art RRT-exploration method as a reference. Experimental results show that with the proposed strategy, the efficiency of robot exploration has been improved obviously.

Keywords

robot explorationfrontier detectionpath information gainperceptual uncertainty
Type
Research Article
Information
Robotica , Volume 40 , Issue 8 , August 2022 , pp. 2748 - 2764
Copyright
© The Author(s), 2022. 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.)

Footnotes

This project is partially supported by National Key R&D Program of China grant #2019YFB1310003, National Science Foundation of China grant #61903267 and China Postdoctoral Science Foundation grant #2020M681691 awarded to Wenzheng Chi.

References

Yamauchi, B., “A Frontier-based Approach for Autonomous Exploration,” Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA’97. ’Towards New Computational Principles for Robotics and Automation’ (1997) pp. 146–151.Google Scholar
Liu, J., Lv, Y., Yuan, Y., Chi, W., Chen, G. and Sun, L., “A Prior Information Heuristic Based Robot Exploration Method in Indoor Environment,” 2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) (2021) pp. 129–134.Google Scholar
Niroui, F., Zhang, K., Kashino, Z. and Nejat, G., “Deep reinforcement learning robot for search and rescue applications: Exploration in unknown cluttered environments,” IEEE Rob. Autom. Lett. 4(2), 610–617 (2019).CrossRefGoogle Scholar
Zhu, D., Li, T., Ho, D., Wang, C. and Meng, M. Q., “Deep Reinforcement Learning Supervised Autonomous Exploration in Office Environments,” 2018 IEEE International Conference on Robotics and Automation (ICRA) (2018) pp. 7548–7555.Google Scholar
Hussein, M. W., Fink, W. and Tripp, J. W., “3D Imaging Lidar for Lunar Robotic Exploration,” Proceedings of SPIE - The International Society for Optical Engineering, vol. 7331 (2009) p. 73310H.CrossRefGoogle Scholar
Bender, A., Williams, S. B. and Pizarro, O., “Autonomous Exploration of Large-Scale Benthic Environments,” 2013 IEEE International Conference on Robotics and Automation (2013) pp. 390–396.Google Scholar
Goldman, R. E., Bajo, A. and Simaan, N., “Algorithms for autonomous exploration and estimation in compliant environments,” Robotica 31(1), 71–87 (2013).CrossRefGoogle Scholar
Nieto-Granda, C., Rogers, J. G., and Christensen, H. I., “Coordination strategies for multi-robot exploration and mapping,” Int. J. Rob. Res. 33(4), 519–533 (2014).CrossRefGoogle Scholar
Reid, R., Cann, A., Meiklejohn, C., Poli, L., Boeing, A. and Braunl, T., “Cooperative Multi-robot Navigation, Exploration, Mapping and Object Detection with ROS,” 2013 IEEE Intelligent Vehicles Symposium (IV) (2013) pp. 1083–1088.Google Scholar
Couceiro, M. S., Portugal, D., Rocha, R. P. and Ferreira, N. M. F., “Marsupial teams of robots: Deployment of miniature robots for swarm exploration under communication constraints,” Robotica 32(7), 1017–1038 (2014).CrossRefGoogle 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).CrossRefGoogle Scholar
Lim, J. H. and Choo, D. W., “Sonar based systematic exploration method for an autonomous mobile robot operating in an unknown environment,” Robotica 16(6), 659–667 (1998).CrossRefGoogle Scholar
Julian, B. J., Karaman, S. and Rus, D., “On mutual information-based control of range sensing robots for mapping applications,” Int. J. Rob. Res. 33(10), 5156–5163 (2014).CrossRefGoogle Scholar
Francis, G., Ott, L., Marchant, R. and Ramos, F., “Occupancy map building through bayesian exploration,” Int. J. Rob. Res. 38(7), 769–792 (2019).CrossRefGoogle Scholar
Keidar, M. and Kaminka, G. A., “Efficient frontier detection for robot exploration,” Int. J. Rob. Res. 33(2), 215–236 (2011).CrossRefGoogle Scholar
Senarathne, N., Wang, D., Wang, Z. and Chen, Q., “Efficient frontier detection and management for robot exploration,” (2013) pp. 114–119.Google Scholar
Umari, H. and Mukhopadhyay, S., “Autonomous Robotic Exploration Based on Multiple Rapidly-Exploring Randomized Trees,” 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2017) pp. 1396–1402.Google Scholar
Lavalle, S. M., “Rapidly-exploring random trees: A new tool for path planning,” Computer Science Department, vol. 98 (1998).Google Scholar
Wang, J., Chi, W., Shao, M. and Meng, M. Q.-H., “Finding a high-quality initial solution for the RRTs algorithms in 2D environments,” Robotica 37(10), 1677–1694 (2019).CrossRefGoogle Scholar
Chi, W., Ding, Z., Wang, J., Chen, G. and Sun, L., “A generalized voronoi diagram based efficient heuristic path planning method for RRTs in mobile robots,” IEEE Trans. Ind. Electron., 1–1 (2021).Google 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., 1–1 (2021).CrossRefGoogle Scholar
Yuan, Y., Liu, J., Wang, J., Chi, W., Chen, G. and Sun, L., “A Knowledge-based Fast Motion Planning Method Through Online Environmental Feature Learning,” 2021 IEEE International Conference on Robotics and Automation (ICRA) (2021) pp. 8309–8315.Google Scholar
Wu, C. and Lin, H., “Autonomous Mobile Robot Exploration in Unknown Indoor Environments Based on Rapidly-Exploring Random Tree,” 2019 IEEE International Conference on Industrial Technology (ICIT) (2019) pp. 1345–1350.Google Scholar
Oriolo, G., Vendittelli, M., Freda, L. and Troso, G., “The SRT Method: Randomized Strategies for Exploration,” IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA’04. 2004, vol. 5 (2004) pp. 4688–4694.Google Scholar
El-Hussieny, H., Assal, S. F. M. and Abdellatif, M., “Improved Backtracking Algorithm for Efficient Sensor-based Random Tree Exploration,” 2013 Fifth International Conference on Computational Intelligence, Communication Systems and Networks (2013) pp. 19–24.Google Scholar
Qiao, W., Fang, Z. and Si, B., “Sample-based Frontier Detection for Autonomous Robot Exploration,” 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO) (2018) pp. 1165–1170.Google Scholar
Gao, W., Booker, M., Adiwahono, A., Yuan, M., Wang, J. and Yun, Y. W., “An Improved Frontier-based Approach for Autonomous Exploration,” 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) (2018) pp. 292–297.Google Scholar
Fang, B., Ding, J. and Wang, Z., “Autonomous robotic exploration based on frontier point optimization and multistep path planning,” IEEE Access 7, 46 104–46 113 (2019).Google Scholar
Lluvia, I., Lazkano, E. and Ansuategi, A., “Active mapping and robot exploration: A survey,” Sensors 21(7) (2021). [Online]. Available: https://www.mdpi.com/1424-8220/21/7/2445 CrossRefGoogle ScholarPubMed
Stachniss, C., Grisetti, G. and Burgard, W., “Information Gain-based Exploration Using Rao-Blackwellized Particle Filters,” In: Robotics: Science and Systems I, June 8–11, 2005 (Massachusetts Institute of Technology, Cambridge, Massachusetts, 2005).CrossRefGoogle Scholar
Thrun, S., Burgard, W. and Fox, D., Probabilistic Robotics, Intelligent Robotics and Autonomous Agents Series (The MIT Press, Cambridge, MA, 2005).Google Scholar
Comaniciu, D. and Meer, P., “Mean shift: A robust approach toward feature space analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002).CrossRefGoogle Scholar
Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., Berger, E., Wheeler, R. and Ng, A., “Ros: An Open-Source Robot Operating System,” ICRA Workshop on Open Source Software, vol. 3 (2009) pp. 1–6.Google Scholar