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Real-time motion planning for robot manipulators in unknown environments using infrared sensors

Published online by Cambridge University Press:  01 March 2007

Shuguo Wang ,
Jin Bao  and
Yili Fu
Shuguo Wang
Affiliation:
Robotics Institute, Harbin Institute of Technology, Harbin 150001, P. R. China
Jin Bao*
Affiliation:
Robotics Institute, Harbin Institute of Technology, Harbin 150001, P. R. China
Yili Fu
Affiliation:
Robotics Institute, Harbin Institute of Technology, Harbin 150001, P. R. China
*
*Corresponding author. E-mail: jinbao@hit.edu.cn
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Summary

This paper deals with sensor-based motion planning method for a robot arm manipulator operating among unknown obstacles of arbitrary shape. It can be applied to online collision avoidance with no prior knowledge of the obstacles. Infrared sensors are used to build a description of the robot's surroundings. This approach is based on the configuration space but the construction of the C-obstacle surface is avoided. The point automation is confined on some planes with square grids in the C-space. A path-searching algorithm based on square grids is used to guide the automation maneuvering around the C-obstacles on the selected planes. To avoid the construction of the C-obstacle surface, the robot geometry model is expanded, and the static collision detection method is used. Hence, the computation time is reduced and the algorithm can work in real time. The effectiveness of the proposed method is verified by a series of experiments.

Keywords

Motion planningInfrared sensorsRobot manipulatorsStatic collision detectionsConfiguration space (C-space)
Type
Article
Information
Robotica , Volume 25 , Issue 2 , March 2007 , pp. 201 - 211
Copyright
Copyright © Cambridge University Press 2007

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References

1.Lozano-Perez, T., “Spatial Planning: A Configuration Space Approach,” IEEE Trans. Comput. 32 (2), 108–120 (1983).CrossRefGoogle Scholar
2.Park, Y. S. and Cho, H. S., “Task Oriented Optimum Positioning of a Mobile Manipulator Base in a Cluttered Environment,” J. Intell. Robot. Syst.: Theory Appl. 18 (2), 147–168 (1997).CrossRefGoogle Scholar
3.Ting, Y., Lei, W. I. and Jar, H. C., “A Path Planning Algorithm for Industrial Robots,” Comput. Indus. Eng. 42 (2–4), 299–308 (1983).CrossRefGoogle Scholar
4.Tso, S. K. and Liu, K. P., “A Fast Motion Planner Base on Configuration Space,” Proceedings of the IEEE International Conference on Intelligent Robots and Systems (1993) pp. 1401–1408.Google Scholar
5.Branicky, M. S. and Newman, W. S., “Rapid Computation of Configuration Space Obstacles,” Proceedings of the IEEE Intelligent Conference on Robotics and Automation (1990) pp. 304–310.CrossRefGoogle Scholar
6.Fox, J. J. and Maciejewski, A. A., “Utilizing the Topology of Configuration Space in Real-Time Multiple Manipulator Path Planning,” Proceedings of the IEEE International Conference on Intelligent Robots and Systems 1 (1994) pp. 665–672.Google Scholar
7.Maciejewski, A. A. and Fox, J. J., “Path Planning and the Topology of Configuration Space,” IEEE Trans. Robot. Autom. 9 (4), 444–456 (1993).CrossRefGoogle Scholar
8.Kawarazaki, N. and Taguchi, K., “Collision-Free Path Planning for a Manipulator Using Free Form Surface,” Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Pittsburgh, Pennsylvania 2 (Aug. 1995 pp. 130–137.Google Scholar
9.Um, D., “Sensor Based Randomized Lattice Diffusion Planner for Unknown Environment Manipulation,” Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Beijing, China (2006) pp. 5382–5387.Google Scholar
10.Um, D. and Park, H., “A Novel Infrared Proximity Array Sensor for 3D Visual Sensing: Modeling and Applications,” Proceedings of IEEE Conference on Robotics, Automation and Mechatronics, Bangkok, Thailand (2006) pp. 1–6.Google Scholar
11.Cheung, E. and Lumelsky, V., “Motion Planning for A Whole-Sensitive Robot Arm Manipulator,” Proceedings of the IEEE International Conference on Robotics and Automation (1990) pp. 344–349.CrossRefGoogle Scholar
12.Lumelsky, V. and Cheung, E., “Real-Time Collision Avoidance in Teleoperated Whole-Sensitive Robot arm Manipulators,” IEEE Trans. Syst., Man Cybern. 23 (1), 194–203 (1993).CrossRefGoogle Scholar
13.Sun, K. and Lumelsky, V., “Path Planning Among Unknown Obstacles: The Case of a Three-Dimensional Cartesian Arm,” IEEE Trans. Robot. Autom. 8 (6), 776–786 (1992).CrossRefGoogle Scholar
14.Um, D. et al. ., “A Modularized Sensitive Skin for Motion Planning in an Uncertain Environment,” Proceedings of the IEEE International Conference on Robotics and Automation, Belgium> (1998) pp. 7–12.+(1998)+pp.+7–12.>Google Scholar
15.Zavlangas, P. and Tzafestas, S., “Industrial Robot Navigation and Obstacle Avoidance Employing Fuzzy Logic,” J. Intell. Robot. Syst. 27 (1/2), 85–97 (2000).CrossRefGoogle Scholar
16.Wei, W., Mbede, J. B. and Zhang, Q. “Fuzzy Sensor-Based Motion Control Among Dynamic Obstacles for Intelligent Rigid-Link Electrically Driven Arm Manipulators,” J. Intell. Robot. Syst. 30 (1), 49–71 (2001).CrossRefGoogle Scholar
17.Kavraki, L. E. et al. ., “Probabilistic Roadmaps for Path Planning in High-Dimensional Configuration Spaces,” IEEE Trans. Robot. Autom. 12 (4), 566–580 (1996).CrossRefGoogle Scholar
18.Kuffner, J. J. and LaValle, S. M., “RRT-Connect: An Efficient Approach to Single-Query Path Planning,” Proceedings of the IEEE International Conference on Robotics and Automation, San Francisco, California (2000) pp. 995–1001.Google Scholar
19.Cheung, E. and Lumelsky, V., “Proximity Sensing in Robot Manipulator Motion Planning: System and Implementation Issues,” IEEE Trans. Robot. Autom. 5 (6), 740–751 (1989).CrossRefGoogle Scholar
20.Lumelsky, V., Shur, M. S. and Wagner, S., “Sensitive Skin,” IEEE Sensors J. 1 (1), 41–51 (2001).CrossRefGoogle Scholar
21.Ivanisevic, I. and Lumelsky, V., “Configuration Space as a Means for Augmenting Human Performance in Teleoperation Tasks,” IEEE Trans. Syst. Man Cybern. Part B 30 (3), 471–484 (2000).CrossRefGoogle ScholarPubMed
22.Baginski, B., “Efficient Dynamic Collision Detection Using Expanded Geometry Models,” Proceedings of the IEEE International Conference on Intelligent Robots and Systems 3 (1997) pp. 1714–1719.Google Scholar
23.Wu, R. and Tsao, T., “Theorem and Application of Adjustable Spectrum,” IEEE Trans. Power Del. 18 (2), 372–376 (2003).Google Scholar
24.Harris, F. J., “On the Use of Windows for Harmonic Analysis With the Discrete Fourier Transform,” Proc. IEEE 66, 51–83 (1978).CrossRefGoogle Scholar
25.Ha, Y. H. and Pearce, J. A., “A New Window and Comparison to Standard Windows,” IEEE Trans. Acoust., Speech, Signal Process. 37 (2), 298–301 (1989).CrossRefGoogle Scholar
26.Gautam, J. K., Kumar, A. and Saxena, R., “On the Modified Bartlett–Hanning Window (family),” IEEE Trans. Signal Process. 44 (8), 2098–2102 (1996).CrossRefGoogle Scholar
27.Hansson, M. and Salomonsson, G., “A Multiple Window Method for Estimation of Peaked Spectra,” IEEE Trans. Signal Process. 45 (3), 778–781 (1997).CrossRefGoogle Scholar
28.Xue, H. and Yang, R., “Optimal Interpolating Windowed Discrete Fourier Transform Algorithms for Harmonic Analysis in Power Systems,” IEE Proc. Gener. Transm. Distrib. 150 (5), 583–587 (2003).CrossRefGoogle Scholar
29.Agrez, D., “Weighted Multipoint Interpolated DFT to Improve Amplitude Estimation of Multifrequency Signal,” IEEE Trans. Instrum. Meas. 51, 287–292 (2002).CrossRefGoogle Scholar
30.Zhang, F., Geng, Z. and Yuan, W., “The Algorithm of Interpolating Windowed FFT For Harmonic Analysis of Electric Power System,” IEEE Trans. Power Del. 16 (2), 160–164 (2001).CrossRefGoogle Scholar
31.Geraerts, R. and Overmars, M. H., “Sampling Techniques for Probabilistic Roadmap Planners,” Proceedings of the Conference on Intelligent Autonomous Systems (2004) pp. 600–609.Google Scholar
32.LaValle, S. M., Planning Algorithms (Cambridge University Press, UK, 2006).CrossRefGoogle Scholar
33.Klosowski, J. et al. ., “Efficient Collision Detection Using Bounding Volume Hierarchies of k-DOPs,” IEEE Trans. Vis. Comput. Graphics 4, 21–37 (1998).CrossRefGoogle Scholar
34.Lin, M. C. and Canny, J. F. “Efficient Algorithms for Incremental Distance Computation,” Proceedings of the IEEE International Conference on Robotics and Automation, Sacramento, California April 7–12, (1991) pp. 1008–1014.Google Scholar
35.Cameron, S., “Enhancing GJK: Computing Minimum and Penetration Distance Between Convex Polyhedra,” Proceedings of the IEEE International Conference on Robotics and Automation (1997) pp. 3112–3117.CrossRefGoogle Scholar
36.Ehmann, S. and Lin, M. C. “Accelerated Proximity Queries Between Convex Polyhedra Using Multi-Level Voronoi Marching,” Proceedings of the IEEE International Conference on Intelligent Robots and Systems (2000) pp. 2101–2106.Google Scholar
37.Ehmann, S. and Lin, M. C. “Accurate and Fast Proximity Queries Between Polyhedra Using Convex Surface Decomposition,” Comput. Graph. Forum 20 (3), 500–510 (2001).CrossRefGoogle Scholar
38.Schwarzer, F., Saha, M. and Latombe, J. C., “Exact Collision Checking of Robot Paths,” Algorithmic Foundations of Robotics, pp. 25–41 (2004).CrossRefGoogle Scholar