Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-25T05:17:42.082Z Has data issue: false hasContentIssue false
  • English
  • Français

Dynamic Window with Virtual Goal (DW-VG): a New Reactive Obstacle Avoidance Approach Based on Motion Prediction

Published online by Cambridge University Press:  04 March 2019

Yu Xinyi ,
Zhu Yichen Open the ORCID record for Zhu Yichen [Opens in a new window] ,
Lu Liang  and
Ou Linlin
Yu Xinyi
Affiliation:
Department of Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China. E-mails: [email protected], [email protected], [email protected]
Zhu Yichen
Affiliation:
Department of Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China. E-mails: [email protected], [email protected], [email protected]
Lu Liang
Affiliation:
Department of Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China. E-mails: [email protected], [email protected], [email protected]
Ou Linlin*
Affiliation:
Department of Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China. E-mails: [email protected], [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

This paper proposes a dynamic window with virtual goal (DW-VG) method for local collision avoidance in dynamic environments. Firstly, the debounce filter and polynomial curve-fitting algorithm are combined to predict the trajectory of the obstacles with timestamps. Based on the motion prediction of the obstacles, the virtual goal is proposed to replace the real goal, so that the robot can escape from the concave trap and avoid the dynamic obstacles. According to the timestamps and virtual goal, the optimal linear and angular velocities are selected from the dynamic window, which drive the robot toward its real goal. The simulation and experimental results show that the DW-VG method can not only escape the local minima and avoid dynamic obstacles but also is applicable to the dense environment. Furthermore, the simulation results also verify that the DW-VG method drives the robot to reach its goal faster and safer than other reactive obstacle avoidance methods.

Keywords

Mobile robotsMotion predictionObstacle avoidanceDynamic windowReactive navigation
Type
Articles
Information
Robotica , Volume 37 , Issue 8 , August 2019 , pp. 1438 - 1456
Copyright
© Cambridge University Press 2019 

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

Mujahed, M., Fischer, D. and Mertsching, B., “Tangential Gap Flow (TGF) navigation: A new reactive obstacle avoidance approach for highly cluttered environments,” Rob. Auton. Syst. 84, 15–30 (2016).CrossRefGoogle Scholar
Hirose, S. and Fukushima, E. F., “Snakes and strings: New robotic components for rescue operations,” Int. J. Rob. Res. 23, 341–349 (2004).CrossRefGoogle Scholar
Lumelsky, V. and Stepanov, A., “Dynamic path planning for a mobile automaton with limited information on the environment,” IEEE Trans. Autom. Control 31, 1058–1063 (1986).CrossRefGoogle Scholar
Kamon, I. and Rivlin, E., “Sensory-based motion planning with global proofs,” IEEE Trans. Rob. Autom. 13, 814–822 (1997).CrossRefGoogle Scholar
Kamon, I., Rimon, E. and Rivlin, E., “TangentBug: A range-sensor-based navigation algorithm,” Int. J. Rob. Res. 17, 934–953 (1998).CrossRefGoogle Scholar
Ng, J. and Bräunl, T., “Performance comparison of bug navigation algorithms,” J. Intell. Rob. Syst. 50, 73–84 (2007).CrossRefGoogle Scholar
Khatib, O., “Real-time obstacle avoidance for manipulators and mobile robots,” Int. J. Rob. Res. 5, 90–98 (1986).CrossRefGoogle Scholar
Minguez, J., Lamiraux, F. and Laumond, J.-P., “Motion Planning and Obstacle Avoidance,” In: Springer Handbook of Robotics (Springer, Switzerland, 2016) pp. 1177–1202.CrossRefGoogle Scholar
Koren, Y. and Borenstein, J., “Potential Field Methods and Their Inherent Limitations for Mobile Robot Navigation,” (IEEE, Sacramento, CA, USA, 1991) pp. 1398–1404.Google Scholar
Borenstein, J. and Koren, Y., “The vector field histogram-fast obstacle avoidance for mobile robots,” IEEE Trans. Rob. Autom. 7, 278–288 (1991).CrossRefGoogle Scholar
Babinec, A., Duchoň, F., Dekan, M., Pásztó, P. and Kelemen, M., “VFH* TDT (VFH* with Time Dependent Tree): A new laser rangefinder based obstacle avoidance method designed for environment with non-static obstacles,” Rob. Autonom. Syst. 62, 1098–1115 (2014).CrossRefGoogle Scholar
Ulrich, I. and Borenstein, J., “VFH+: Reliable Obstacle Avoidance for Fast Mobile Robots,” (IEEE, Leuven, Belgium, 1998) pp. 1572–1577.Google Scholar
Ren, J., McIsaac, K. A. and Patel, R. V., “Modified Newton’s method applied to potential field-based navigation for mobile robots,” IEEE Trans. Rob. 22, 384–391 (2006).Google Scholar
Ferreira, A., Pereira, F. G., Vassallo, R. F., Bastos Filho, T. F. and Sarcinelli Filho, M., “An approach to avoid obstacles in mobile robot navigation: The tangential escape,” Sba: Controle & Automação Sociedade Brasileira de Automatica 19, 395–405 (2008).Google Scholar
Chiang, H.-T., Malone, N., Lesser, K., Oishi, M. and Tapia, L., “Path-Guided Artificial Potential Fields with Stochastic Reachable Sets for Motion Planning in Highly Dynamic Environments,” (IEEE, Seattle, WA, USA, 2015) pp. 2347–2354.CrossRefGoogle Scholar
Fox, D., Burgard, W. and Thrun, S., “The dynamic window approach to collision avoidance,” IEEE Rob. Autom. Mag. 4, 23–33 (1997).CrossRefGoogle Scholar
Maroti, A., Szalóki, D., Kiss, D. and Tevesz, G., “Investigation of Dynamic Window Based Navigation Algorithms on a Real Robot,” (IEEE, 2013), pp. 95–100.CrossRefGoogle Scholar
Fiorini, P. and Shiller, Z., “Motion planning in dynamic environments using velocity obstacles,” Int. J. Rob. Res. 17, 760–772 (1998).CrossRefGoogle Scholar
Large, F., Laugier, C. and Shiller, Z., “Navigation among moving obstacles using the NLVO: Principles and applications to intelligent vehicles,” Autonom. Rob. 19, 159–171 (2005).CrossRefGoogle Scholar
Ali, M. A. H., Mailah, M. and Hing, T. H., “A novel approach for visibility search graph based path planning,” 13th International Conference on Robotics, Control and Manufacturing Systems (2013) pp. 44–49.Google Scholar
Faisal, M., Al-Mutib, K., Hedjar, R., Mathkour, H., Alsulaiman, M. and Mattar, E., “Multi modules fuzzy logic for mobile robots navigation and obstacle avoidance in unknown indoor dynamic environment,” In: Proceedings of the 2013 International Conference on Systems, Control and Informatics (2013) pp. 371–379.Google Scholar
Lavalle, S. M., “Rapidly-Exploring Random Trees: A New Tool for Path Planning,” TR 98-11 (Computer Science Department, Iowa State University, 1998).Google Scholar
Wang, Q., Wang, W. and Li, Y., “A Multi-RRT Based Hierarchical Path Planning Method,” 2012 IEEE 14th International Conference on Communication Technology (ICCT) (IEEE, 2012).Google Scholar
Pimentel, J. M., Alvim, M. S., Campos, M. F. M. and Macharet, D. G., “Information-driven rapidly-exploring random tree for efficient environment exploration,” J. Intell. Rob. Syst. 91(2) 1–19 (2017).Google Scholar
Kamil, F., Hong, T. S., Khaksar, W., Moghrabiah, M. Y., Zulkifli, N. and Ahmad, S. A., “New robot navigation algorithm for arbitrary unknown dynamic environments based on future prediction and priority behavior,” Expert Syst. Appl. 86, 274–291 (2017).CrossRefGoogle Scholar