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Active exploration using a scheme for autonomous allocation of landmarks†

Published online by Cambridge University Press:  03 December 2013

Jing Yuan ,
Yalou Huang ,
Fengchi Sun  and
Tong Tao
Jing Yuan*
Affiliation:
Department of Automation, Nankai University, Tianjin 300071, P. R. China
Yalou Huang
Affiliation:
Department of Automation, Nankai University, Tianjin 300071, P. R. China
Fengchi Sun
Affiliation:
Department of Automation, Nankai University, Tianjin 300071, P. R. China
Tong Tao
Affiliation:
Department of Automation, Nankai University, Tianjin 300071, P. R. China
*
*Corresponding author. Email: [email protected]
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Summary

In this paper, we focus on the unknown environments without artificial landmarks and features, such as disaster situations and polar regions. An approach to active exploration based on an on-line scheme for autonomous allocation of landmarks is proposed. Specifically, the robot carries along with itself some landmarks which are to be allocated during the exploration according to some heuristic rules. The utility of landmark allocation is analyzed and calculated. Then the active exploration is converted into a problem of multi-objective optimization. The objective function includes three weighted terms: the accuracy of localization and mapping, the coverage rate of the unknown environment and the utility of the allocated landmarks. By solving this optimization problem, control inputs of the robot are computed to guarantee that accurate localization, high-quality mapping and complete exploration can be achieved simultaneously. Moreover, supplementation and redundancy elimination of the allocated landmarks are executed to make a complete and non-redundant coverage for the environment. Finally, some landmarks, together with a device for allocating these landmarks, are developed. Both experiment and simulation results are presented to demonstrate the effectiveness of the proposed approach.

Keywords

Active explorationLandmark allocationEKFMulti-objective optimization
Type
Articles
Information
Robotica , Volume 32 , Issue 5 , August 2014 , pp. 757 - 782
Copyright
Copyright © Cambridge University Press 2013 

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Footnotes

†

This paper was partially presented at the 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan, May 12–17.

References

1.Smith, R., Self, M. and Cheeseman, P., “Estimating Uncertain Spatial Relationships in Robotics,” In: Autonomous Robot Vehicle (Cox, I. J. and Wilfon, G. T., eds.), (Springer-Verleg, New York, NY, 1990) pp. 167–193.CrossRefGoogle Scholar
2.Dissanayake, G., Newman, P., Clark, S., Durrant-Whyte, H. and Csorba, M., “A solution to the simultaneous localization and map building (SLAM) problem,” IEEE Trans. Robot. Autom. 17 (3), 229–241 (2001).CrossRefGoogle Scholar
3.Chatterjee, A., “Differential evolution tuned fuzzy supervisor adapted extended Kalman filtering for SLAM problems in mobile robots,” Robotica 27 (3), 411–423 (2009).CrossRefGoogle Scholar
4.Cheein, F. A., di Sciascio, F., Scaglia, G. and Carelli, R., “Towards features updating selection based on the covariance matrix of the SLAM system state,” Robotica 29 (2), 271–282 (2011).CrossRefGoogle Scholar
5.Andrade-Cetto, J., Vidal-Calleja, T. and Sanfeliu, A., “Unscented Transformation of Vehicle States in SLAM,” In: Proceedings of the IEEE International Conference on Robotics and Automation, Barcelona, Spain (2005) pp. 324–329.Google Scholar
6.Knight, J., Davison, A. and Reid, I., “Toward Constant Time SLAM Using Postponement,” In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Maui, HI, USA (2001) pp. 406–412.Google Scholar
7.Guivant, J. E. and Nebot, E. M., “Optimization of the simultaneous localization and map-building algorithm for real-time implementation,” IEEE Trans. Robot. Autom. 17 (3), 242–257 (2001).CrossRefGoogle Scholar
8.Thrun, S., Liu, Y., Koller, D., Ng, A. Y., Ghahramani, Z. and Durrant-Whyte, H., “Simultaneous localization and mapping with sparse extended information filters,” Int. J. Robot. Res. 23 (7), 693–716 (2004).CrossRefGoogle Scholar
9.Martinelli, A., Tomatics, N. and Siegwart, R., “Open Challenges in SLAM: An Optimal Solution Based on Shift and Rotation Invariants,” In: Proceedings of the IEEE International Conference on Robotics and Automation, New Orleans, LA, USA (2004) pp. 1327–1332.Google Scholar
10.Paz, L. M., Tardos, J. D. and Neira, J., “Divide and conquer: EKF SLAM in O(n),” IEEE Trans. Robot. 24 (5), 1107–1120 (2008).CrossRefGoogle Scholar
11.Montemerlo, M., Thrun, S. and Koller, D., “FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem,” Proceedings of the AAAI National Conference on Artificial Intelligence, Edmonton, Canada (2002).Google Scholar
12.Montemerlo, M., Thrun, S., Koller, D. and Wegbreit, B., “FastSLAM 2.0: An Improved Particle Filtering Algorithm for Simultaneous Localization and Mapping that Provably Converges,” Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, Acapulco, Mexico (2003).Google Scholar
13.Montemerlo, M. and Thrun, S., “Simultaneous Localization and Mapping with Unknown Data Association Using FastSLAM,” In: Proceedings of the IEEE International Conference on Robotics and Automation, Taipei, Taiwan (2003) pp. 1985–1991.Google Scholar
14.Kim, C., Sakthivel, R. and Chung, W. K., “Unscented FastSLAM: a robust and efficient solution to the SLAM problem,” IEEE Trans. Robot. 24 (4), 808–820 (2008).CrossRefGoogle Scholar
15.Kim, C., Kim, H. and Chung, W. K., “Exactly Rao–Blackwellized Unscented Particle Filters for SLAM,” In: Proceedings of the IEEE International Conference on Robotics and Automation, Shanghai, China (2010) pp. 3589–3594.Google Scholar
16.Lee, H. C., Lee, S. H., Choi, M. H. and Lee, B.H., “Probabilistic map merging for multi-robot RBPF-SLAM with unknown initial poses,” Robotica 30 (2), 205–220 (2012).CrossRefGoogle Scholar
17.Thrun, S. and Montemerlo, M., “The GraphSLAM algorithm with applications to large-scale mapping of urban structures,” Int. J. Robot. Res. 25 (5/6), 403–430 (2005).CrossRefGoogle Scholar
18.Levinson, J. and Thrun, S., “Robust Vehicle Localization in Urban Environments Using Probabilistic Maps,” In: Proceedings of IEEE International Conference on Robotics and Automation, Anchorage, AK, USA (2010) pp. 4372–4378.Google Scholar
19.Choset, H. and Nagatani, K., “Topological simultaneous localization and mapping (SLAM): Toward exact localization without explicit localization,” IEEE Trans. Robot. Autom. 17 (2), 125–137 (2001).CrossRefGoogle Scholar
20.Tully, S., Kantor, G., Choset, H. and Werner, F., “A Multi-Hypothesis Topological SLAM Approach for Loop Closing on Edge-Ordered Graphs,” In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, USA (2009) pp. 4943–4948.Google Scholar
21.Tao, T., Tully, S., Kantor, G. and Choset, H., “Incremental Construction of the Saturated-GVG for Multi-Hypothesis Topological SLAM,” In: Proceedings of IEEE International Conference on Robotics and Automation, Shanghai, China (2011) pp. 3072–3077.Google Scholar
22.Thrun, S., Burgard, W. and Fox, D., “A Real-Time Algorithm for Mobile Robot Mapping with Applications to Multi-Robot and 3D Mapping,” In: Proceedings of IEEE International Conference on Robotics and Automation, San Francisco, CA, USA (2000) pp. 321–328.Google Scholar
23.Chang, H. J., Lee, C. S. George, Lu, Y. H. and Hu, Y. Charlie, “P-SLAM: Simultaneous localization and mapping with environmental-structure prediction,” IEEE Trans. Robot. 23 (2), 281–293 (2007).CrossRefGoogle Scholar
24.Kleiner, A., Prediger, J. and Nebel, B., “RFID Technology-Based Exploration and SLAM for Search and Rescue,” In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China (2006) pp. 4054–4059.Google Scholar
25.Feder, H., Leonard, J. and Smith, C., “Adaptive mobile robot navigation and mapping,” Int. J. Robot. Res. 18 (7), 650–668 (1999).CrossRefGoogle Scholar
26.Bourgault, F., Makarenko, A. and Williams, S., “Information-Based Adaptive Robotic Exploration,” In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Lausanne, Switzerland (2002) pp. 540–545.Google Scholar
27.Huang, S., Wang, Z. and Dissanayake, G., “Time Optimal Robot Motion Control in Simultaneous Localization and Map Building (SLAM) Problem,” In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan (2004) pp. 3110–3115.Google Scholar
28.Huang, S., Kwok, N. M., Dissanayake, G., Ha, Q. P. and Fang, G., “Multi-Step Look-Ahead Trajectory Planning in SLAM: Possibility and Necessity,” In: Proceedings of the IEEE International Conference on Robotics and Automation, Barcelona, Spain (2005) pp. 1103–1108.Google Scholar
29.Fairfield, N. and Wettergreen, D., “Active SLAM and Loop Prediction with the Segmented Map Using Simplified Models,” In: Proceedings of the 7th International Conference on Field and Service Robotics, Cambridge, MA, USA (2009) pp. 173–182.Google Scholar
30.Leung, C., Huang, S. and Dissanayake, G., “Active SLAM in Structured Environments,” Proceedings of the IEEE International Conference on Robotics and Automation, Pasadena, CA, USA (2008) pp. 1898–1903.Google Scholar
31.Yuan, J., Huang, Y., Sun, F. and Tao, T., “Active Exploration Using Scheme of Autonomous Distribution for Landmarks,” In: Proceedings of the IEEE International Conference on Robotics and Automation, Kobe, Japan (2009) pp. 4169–4174.Google Scholar
32.Thrun, S., Burgard, W. and Fox, D., Probabilistic Robotics (MIT Press, Cambridge, MA, 2005).Google Scholar
33.Choset, H., Lynch, K., Hutchinson, S., Kantor, G., Burgard, W., Kavraki, L. and Thrun, S., Principles of Robotic Motion: Theory, Algorithms and Implementation (MIT Press, Cambridge, MA, 2004).Google Scholar
34.Yamauchi, B., “A Frontier-Based Approach for Autonomous Exploration,” In: Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation, Monterey, CA, USA (1997) pp. 146–151.Google Scholar
35.Jiang, J., Fang, L., Zhang, H. and Dou, W., “An algorithm for minimal connected cover set problem in wireless sensor networks,” J. Softw. 17 (2), 175–184 (2006).CrossRefGoogle Scholar
36.Hochbaum, D., Approximation Algorithms for NP-Hard Problem (PWS, Cambridge, MA, 1995).Google Scholar