Hostname: page-component-848d4c4894-wzw2p Total loading time: 0 Render date: 2024-05-27T14:13:40.155Z Has data issue: false hasContentIssue false
  • English
  • Français

SLAM-based maneuverability strategy for unmanned car-like vehicles

Published online by Cambridge University Press:  07 March 2013

Fernando A. Auat Cheein
Fernando A. Auat Cheein*
Affiliation:
Department of Electronics Engineering, Universidad Técnica Federico Santa María, Av. España 1680, Valparaíso, Chile
*
*Corresponding author. E-mail: fernando.auat@usm.cl
Get access Rights & Permissions [Opens in a new window]

Summary

In this work, an optimal maneuverability strategy for car-like unmanned vehicles operating in restricted environments is presented. The maneuverability strategy is based on a path planning algorithm that uses the environment information to plan a safe, feasible and optimum path for the unmanned mobile robot. The environment information is obtained by means of a simultaneous localization and mapping (SLAM) algorithm. The SLAM algorithm uses the sensors' information to build a map of the surrounding environment. A Monte Carlo sampling technique is used to find an optimal and safe path within the environment based on the SLAM information. The objective of the planning is to safely reach a desired orientation in a bounded space. Theoretical demonstrations and real-time experimental results (in indoor and outdoor environments) are also presented in this work.

Keywords

Path planningSLAMUnmanned vehicles
Type
Articles
Information
Robotica , Volume 31 , Issue 6 , September 2013 , pp. 905 - 921
Copyright
Copyright © Cambridge University Press 2013 

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.)

References

1.Whyte, H. D. and Bailey, T., “Simultaneous localization and mapping (SLAM): Part I. Essential algorithms,” IEEE Robot. Autom. Mag. 13, 99–108 (2006).CrossRefGoogle Scholar
2.Whyte, H. and Bailey, T., “Simultaneous localization and mapping (SLAM): Part II. State of the art,” IEEE Robot. Autom. Mag. 13, 108–117 (2006).Google Scholar
3.Thrun, S., Burgard, W. and Fox, D., Probabilistic Robotics (MIT Press, Cambridge, 2005).Google Scholar
4.Siegwart, R. and Nourbahsh, I., Introduction to Autonomous Mobile Robots (MIT Press, Cambridge, 2004).Google Scholar
5.Theodoridis, S. and Koutroumbas, K., Pattern Recognition (Elsevier Academic Press, USA, 2003).Google Scholar
6.Howarth, B., Katupitiya, J., Guivant, J. and Szwec, A., “Novel Robotic 3D Surface Mapping Using Range and Vision Fusion,” In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Taipei, Taiwan (Oct. 2010) pp. 1539–1544.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, 242–257 (2001).CrossRefGoogle Scholar
8.Garulli, A., Giannitrapani, A., Rossi, A. and Vicino, A., “Mobile Robot SLAM for Line-Based Environment Representation,” In: 44th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC'05) (Dec. 2005) pp. 2041–2046.Google Scholar
9.Jaradat, M. A. K. and Langari, R., “Line Map Construction Using a Mobile Robot with a Sonar Sensor,” In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics (July 2005) pp. 1251–1256.Google Scholar
10.Navarro, D., Benet, G. and Martinez, M., “Line Based Robot Localization Using a Rotary Sonar,” In: IEEE Conference on Emerging Technologies and Factory Automation (ETFA) (Sep. 2007), pp. 896–899.Google Scholar
11.Zhow, W., Miro, J. Valls and Dissanayake, G., “Information-efficient 3D visual SLAM for unstructured domains,” IEEE Trans. Robot. 24 (5), 1078–1087 (2008).Google Scholar
12.Munguia, R. and Grau, A., “Closing loops with a virtual sensor based on monocual slam,” IEEE Trans. Instrum. Meas. 58 (8), 2377–2384 (2009).CrossRefGoogle Scholar
13.Masson, F., Guivant, J. and Nebot, E., “Hybrid Architecture for Simultaneous Localization and Map Building in Large Outdoor Areas,” In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2002), vol. 1, pp. 570–575.Google Scholar
14.Hutchinson, S., Kantor, G., Burgard, W., Kavraki, L. E., Choset, H., Lynch, K. M. and Thrun, S., Principles of Robot Motion: Theory, Algorithms and Implementations (MIT Press, Cambridge, 2005).Google Scholar
15.An, S., Kang, J., Choi, W. and Oh, S., “Results for Outdoor-SLAM Using Sparse Extended Information Filters,” In: IEEE/RSJ International Conference on Robotics and Automation (ICRA) (2003) pp. 1227–1233.Google Scholar
16.Sunderhauf, N., Lange, S. and Protzel, P., “Using the Unscented Kalman Filter in Mono-SLAM with Inverse Depth Parametrization for Autonomous Airship Control,” In: Proceedings of the IEEE International Workshop on Safety Security and Rescue Robotics (2007) pp. 1–6.Google Scholar
17.Welle, J., Schulz, D., Bachram, T. and Cremers, A., “Optimization Techniques for Laser-Based 3D Particle Filter SLAM,” In: IEEE/RSJ International Conference on Robotics and Automation (ICRA) (2010) pp. 3525–3530.Google Scholar
18.Eade, E., Fong, P. and Munich, M., “Monocular Graph Slam with Complexity Reduction,” In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2010) pp. 3017–3024.Google Scholar
19.Arkin, R. C., Behavior-based Robotics (MIT Press, Cambridge, 1998).Google Scholar
20.Gonzalez, M. and Recio, T., “Path tracking in motion planning,” Comput. J. 3, 515–524 (1993).CrossRefGoogle Scholar
21.Ferreira, A., Celeste, W., Cheein, F. Auat, Filho, T. Bastos, Filho, M. Sarcinelli and Carelli, R., “Human–machine interfaces based on EMG and EEG applied to robotic systems,” J. NeuroEng. Rehabil. 5, 5–10 (2008).CrossRefGoogle ScholarPubMed
22.Sun, F., Tao, T., Huang, Y. and Wang, T., “Motion Planning for SLAM Based on Frontier Exploration,” In: International Conference on Mechatronics and Automation (2007) vol. 3, pp. 2907–2912.Google Scholar
23.Cheein, F. A. Auat, De la Cruz, C., Freira-Bastos, T. and Carelli, R., “SLAM-based cross-a-door solution approach for a robotic wheelchair,” Int. J. Adv. Robot. Syst. 6, 239–248 (2010).Google Scholar
24.Lavalle, S., Planning Algorithms (Cambridge University Press, Cambridge, 2006).CrossRefGoogle Scholar
25.Pothal, J. K., Parhi, D. R. and Singh, M. K., “Navigation of Multiple Mobile Robots Using Swarm Intelligence,” World Congress on Nature Biologically Inspired Computing (2009).Google Scholar
26.Tian, Y., Yang, M. and Yin, X., “Distributed Optimal Control Based on Internal Average Kinetic Energy for Multi-robot System,” 4th International Conference on Autonomous Robots and Agents (2009).Google Scholar
27.Becked, M., Macek, K. and Siegwart, R., “Motion Planning for Car-like Vehicles in Dynamic Urban Scenarios,” In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2006).Google Scholar
28.Fan, D. and Shi, P., “Improvement of Dijkstra's Algorithm and Its Application in Route Planning,” In: Seventh International Conference on Fuzzy Systems and Knowledge Discovery (2010).CrossRefGoogle Scholar
29.Qu, Y.-H., Pan, Q. and Yan, J.-G., “Flight Path Planning of UAV Based on Heuristically Search and Genetic Algorithms,” In: 31st Annual Conference of IEEE on Industrial Electronics Society (IECON 2005) (Nov. 2005) pp. 5.Google Scholar
30.Hwang, J. Y., Kim, J. S., Lim, S. S. and Park, K. H., “A fast path planning by path graph optimization,” IEEE Trans. Syst. Man Cybern. 33 (1), 121–129 (2003).CrossRefGoogle Scholar
31.Dubins, L. E., “On curves of minimal length with a constraint on average curvature, and with prescribed initial and terminal positions and tangents,” Am. J. Math. 79, 497–516 (1957).CrossRefGoogle Scholar
32.Lim, C.-W., Park, S., Ryoo, C.-K., Choi, K. and Cho, J.-H., “A Path Planning Algorithm for Surveillance UAVS with Timing Mission Constraints,” In: International Conference on Control Automation and Systems (ICCAS), (Oct. 2010) pp. 2371–2375.CrossRefGoogle Scholar
33.Ferranti, E. and Trioni, N., “Practical issues in deploying mobile agents to explore a sensor-instrumented environment,” Comput. J. 54, 309–320 (2011).CrossRefGoogle Scholar
34.Lee, S. K., Lee, S., Nam, C. and Doh, N. L., “Local Path Planning Scheme for Car-like Vehicle's Shortest Turning Motion Using Geometric Analysis,” In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (Oct. 2010) pp. 4761–4768.Google Scholar
35.Suzuki, Y., Kagami, S. and Kuffner, J. J., “Path Planning with Steering Sets for Car-like Robots and Finding an Effective Set,” In: IEEE International Conference on Robotics and Biomimetics (ROBIO'06), (Dec. 2006) pp. 1221–1226.CrossRefGoogle Scholar
36.Lee, Z. and Chen, X., “Path Planning Approach Based on Probabilistic Roadmap for Sensor Based Car-like Robot in Unknown Environments,” In: IEEE International Conference on Systems, Man and Cybernetics (Oct. 2004) vol. 3, pp. 2907–2912.Google Scholar
37.Linker, R. and Blass, T., “Optimal Path Planning for Car-like Off-road Vehicles,” In: IEEE Conference on Robotics, Automation and Mechatronics (Sep. 2008) pp. 150–154.CrossRefGoogle Scholar
38.Rezaei, S., Guivant, J. and Nebot, E., “Car-like Robot Path Following in Large Unstructured Environments,” In: IEEE Proceedings of the International Conference on Intelligent Robots and Systems (Oct. 2003), pp. 2468–2473.Google Scholar
39.Cheein, F. Auat, Scaglia, G., di Sciascio, F. and Carelli, R., “Towards features updating selection based on the covariance matrix of the SLAM system state,” Robotica 29, 271–282 (2011).CrossRefGoogle Scholar
40.Cheein, F. Auat and Carelli, R., “Analysis of different feature selection criteria based on a covariance convergence perspective for a slam algorithm,” Sensors (Basel) 11, 62–89 (2011).CrossRefGoogle Scholar
41.Cheein, F. Auat, Carelli, R., De la Cruz, C. and Bastos-Filho, T. F., “SLAM-based Turning Strategy in Restricted Environments for Car-like Mobile Robots,” In: IEEE International Conference on Industrial Technology (ICIT) (Mar. 2010) pp. 602–607.CrossRefGoogle Scholar
42.De la Cruz, C., Bastos, T. F., Cheein, F. A. A. and Carelli, R., “SLAM-based Robotic Wheelchair Navigation System Designed for Confined Spaces,” In: IEEE International Symposium on Industrial Electronics (ISIE) (July 2010) pp. 2331–2336.CrossRefGoogle Scholar
43.Cheein, F. Auat, Steiner, G., Perez, G. and Carelli, R., “Optimized EIF-SLAM algorithm for precision agriculture mapping based on visual stems detection,” Comput. Electron. Agric. 78, 195–207 (2011).CrossRefGoogle Scholar