Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-27T11:22:46.476Z Has data issue: false hasContentIssue false
  • English
  • Français

Artificial moment method using attractive points for the local path planning of a single robot in complicated dynamic environments

Published online by Cambridge University Press:  07 June 2013

Wang-bao Xu ,
Jie Zhao ,
Xue-bo Chen  and
Ying Zhang
Wang-bao Xu
Affiliation:
School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, P. R. China School of Electronics and Information Engineering, Liaoning University of Science and Technology, Anshan 114051, P. R. China
Jie Zhao
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150001, P. R. China
Xue-bo Chen*
Affiliation:
School of Electronics and Information Engineering, Liaoning University of Science and Technology, Anshan 114051, P. R. China
Ying Zhang
Affiliation:
School of Electronics and Information Engineering, Liaoning University of Science and Technology, Anshan 114051, P. R. China
*
*Corresponding author. E-mail: xuebochen@126.com
Get access Rights & Permissions [Opens in a new window]

Summary

A novel path planner is presented for the local path planning of a single robot (represented with R) in a complicated dynamic environment. Here a series of attractive points are computed based on attractive segments for guiding R to move along a shorter path. Each attractive segment is obtained by using the full environmental knowledge and will be used for several sampling times in general. A motion controller, which is designed based on artificial moments and a robot model that has a principal motion direction line(PMDline), makes R move closely to attractive points while away from obstacles. Attractive and repulsive moments are designed, which only make R's PMDline face toward attractive points and opposite to obstacles in general, as in most cases, R will move along its PMDline with its full speed. Because of the guidance of attractive points and R's full-speed motion, the global convergence is guaranteed. Simulations indicate that the proposed path planner meets the requirements of real-time property while can optimize R's traveling path.

Keywords

Path planningAutonomous robotsMotion controlArtificial moment method
Type
Articles
Information
Robotica , Volume 31 , Issue 8 , December 2013 , pp. 1263 - 1274
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.Hsu, D., Latombe, J. C. and Motwani, R., “Path planning in expansive configuration spaces,” Int. J. Comput. Geom. Appl. 9 (4&5), 495–512 (1999).CrossRefGoogle Scholar
2.Nearchou, A. C., “Path planning of a mobile robot using genetic heuristics,” Robotica 16, 575–588 (1998).CrossRefGoogle Scholar
3.Undeger, C. and Polat, F., “Real-time edge follow: A real-time path search approach,” IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 37 (5), 860–872 (2007).CrossRefGoogle Scholar
4.Ge, S. S., Lai, X. and Mamum, A. A., “Boundary following and globally convergent path using instant goals,” IEEE Trans. Syst. Man Cybern. Part B: Cybern. 35 (2), 240–254 (2005).CrossRefGoogle ScholarPubMed
5.Salichs, M. A. and Moreno, L., “Navigation of mobile robots: Open questions,” Robotica 18, 227–234 (2000).CrossRefGoogle Scholar
6.Huang, H. P. and Chung, S. Y., “Dynamic Visibility Graph for Path Planning,” In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan (2004) pp. 2813–2818.Google Scholar
7.Takahashi, O. and Schilling, R. J., “Motion planning in a plane using generalized Voronoi diagrams”, IEEE Trans. Robot. Autom. 5 (2), 143–150 (1989).CrossRefGoogle Scholar
8.Sud, A., Andersen, E., Curtis, S., Lin, M. C. and Manocha, D., “Real-time path planning in dynamic virtual environments using multiagent navigation graphs,” IEEE Trans. Vis. Comput. Graph. 14 (3), 526–538 (2008).CrossRefGoogle ScholarPubMed
9.Liu, Y. H. and Arimoto, S., “Proposal of Tangent Graph and Extended Tangent Graph for Path Planning of Mobile Robots,” In: Proceedings of IEEE International Conference on Robotics and Automation, Sacramento, CA (1991) pp. 312–317.Google Scholar
10.Kamon, I., Rimon, E. and Rivlin, E., “Tangentbug: A range-sensor-based navigation algorithm,” Int. J. Robot. Res. 17 (9), 934–953 (1998).CrossRefGoogle Scholar
11.Kavraki, L. E., Svestka, P., Latombe, J. C. and Overmars, M. H., “Probabilistic roadmaps for path planning in high-dimensional configuration spaces,” IEEE Trans. Robot. Autom. 12 (4), 566–580 (1996).CrossRefGoogle Scholar
12.Cheng, P. and Lavalle, S. M., “Resolution Complete Rapidly-Exploring Random Trees,” In: Proceedings of IEEE International Conference on Robotics and Automation, Washington, America (2002) pp. 267–272.Google Scholar
13.Yakey, J., Lavalle, S. M. and Kavraki, L. E., “Randomized path planning for linkages with closed kinematic chains,” IEEE Trans. Robot. Autom. 17 (6), 951–958 (2001).CrossRefGoogle Scholar
14.Rimon, E. and Koditschek, D. E., “Exact robot navigation using artificial potential functions,” IEEE Trans. Robot. Autom. 8 (5), 501–518 (1992).CrossRefGoogle Scholar
15.Oustaloup, A., Orsoni, B., Melchior, P. and Linarès, H., “Path planning by fractional differentiation,” Robotica 21 (1), 59–69 (2003).CrossRefGoogle Scholar
16.Ge, S. S. and Cui, Y. J.. “New potential functions for mobile robot path planning,” IEEE Trans. Robot. Autom. 16 (5), 615–620 (2000).CrossRefGoogle Scholar
17.Yin, L., Yin, Y. X. and Lin, C. J., “A new potential field method for mobile robot path planning in the dynamic environments”, Asian J. Control 11 (2), 214–225 (2009).CrossRefGoogle Scholar
18.Kim, D. H., “Escaping route method for a trap situation in local path planning,” Int. J. Control Autom. Syst. 7 (3), 495–500 (2009).CrossRefGoogle Scholar
19.Zhu, Y., Zhang, T. and Song, J. Y., “An Improved Wall Following Method for Escaping From Local Minimum in Artificial Potential Field Based Path Planning,” In: Proceedings of Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, Shanghai, China (2009) pp. 6017–6022.Google Scholar
20.Ng, J. and Bräunl, T., “Performance comparison of bug navigation algorithms,” J. Intell. Robot. Syst. 50, 73–84 (2007).CrossRefGoogle Scholar
21.Besada-Portas, E., Torre, L., Cruz, J. M. and Andrés-Toro, B., “Evolutionary trajectory planner for multiple UAVs in realistic scenarios,” IEEE Trans. Robot. 26 (4), 619–634 (2010).CrossRefGoogle Scholar
22.Zhang, C. G. and Xi, Y. G., “Robot path planning in globally unknown environments based on rolling windows,” Sci. China-Ser. E 44 (2), 131–139 (2001).Google Scholar
23.Zhang, C. G. and Xi, Y. G., “Rolling path planning and safety analysis of mobile robot in dynamic uncertain environment,” Control Theory Appl. (in Chinese) 20 (1), 37–44 (2003).Google Scholar
24.Zhang, C. G. and Xi, Y. G., “A real-time path planning method for mobile robot avoiding oscillation and dead circulation,” Acta Autom. Sin. 29 (2), 197–205 (2003).Google Scholar
25.Zhang, C. G. and Xi, Y. G., “Sub-optimality analysis of mobile robot rolling path planning,” Sci. China-Series F Inf. Sci. 46 (2), 116–125 (2003).CrossRefGoogle Scholar
26.Xu, W. B. and Chen, X. B., “Artificial moment method for swarm-robot formation control,” Sci. China-Ser. F: Inf. Sci. 51 (10), 1521–1531 (2008).Google Scholar
27.Xu, W. B. and Chen, X. B., “A dynamical formation control approach based on artificial moments,” Control Theory Appl. (in Chinese) 26 (11), 1232–1238 (2009).Google Scholar