No CrossRef data available.
Article contents
Algorithmic path planning of static robots in three dimensions using configuration space metrics
Published online by Cambridge University Press: 16 April 2010
Summary
Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.
Collision-free path planning for static robots is a demanding manifold of contemporary robotics research, vastly due to the growing industrial applications. In this paper, a novel ‘visibility map’-based heuristic algorithm is used to generate near-optimal safe path for a three-dimensional congested robot workspace. The final path is obtainable in terms of joint configurations, by considering the Configuration Space of the task space. The developed algorithm has been verified initially by considering representative 2D workspaces, cluttered with different obstacles with regular geometries and then after with the spatial endeavour. A case study reveals the effectiveness of the developed modules of the configuration space mapping, pertaining to a five degrees-of-freedom low payload articulated robot.
Information
- Type
- Article
- Information
- Copyright
- Copyright © Cambridge University Press 2010
References
1.Acar, E. U., Chosel, H., Zhang, Y. and Schervish, M., “Path planning for robotic demining: Robust sensor-based coverage of unstructured environments and probabilistic methods,” Int. J. Rob. Res. 22 (7–8), 441–466 (2003).CrossRefGoogle Scholar
2.Bajaj, C. and Kim, M. S., “Generation of configuration space obstacles: Moving algebraic surfaces,” Int. J. Rob. Res. 9 (1), 92–112 (1990).CrossRefGoogle Scholar
3.Branicky, M. S. and Newman, W. S., “Rapid Computation of Configuration Space Obstacles,” Proceedings of the IEEE International Conference on Robotics and Automation, Cincinnati, OH, USA (1990) pp. 304–310.CrossRefGoogle Scholar
4.Brooks, R. A., “Solving the find-path problem by good representation of free space,” IEEE Trans. Syst. Man Cybern. SMC-13 (3), 190–197 (1983).CrossRefGoogle Scholar
5.Brost, R. C., “Computing Metric and Topological Properties of Configuration Space Obstacles,” Proceedings: IEEE International Conference on Robotics and Automation, Scottsdale, AZ, USA (1989) pp. 170–176.Google Scholar
6.Campbell, D. and Higgins, J., “Minimal visibility graphs,” Inf. Process. Lett. 37 (1), 49–53 (1991).CrossRefGoogle Scholar
7.Curto, B. and Moreno, V., “Mathematical Formalism for the Fast Evaluation of the Configuration Space,” Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation, Monterey, CA (Jul. 1997) pp. 194–199.Google Scholar
8.De Pedro, M. T. and Rosa, R. G., “Robot path planning in the configuration space with automatic obstacle transformation,” Cybernatics Syst. 23 (4), 367–378 (1992).CrossRefGoogle Scholar
9.Erdmann, M., “On a representation of friction in configuration space,” Int. J. Rob. Res. 13 (3), 240–271 (1994).CrossRefGoogle Scholar
10.Fu, L.-C. and Liu, D.-Y., “An efficient algorithm for finding a collision-free path among polyhedral obstacles,” J. Rob. Syst. 7 (1), 129–137 (1990).CrossRefGoogle Scholar
11.Gilbert, E. G. and Johnson, D. W., “Distance functions and their application to robot path planning in the presence of obstacles,” IEEE Trans. Rob. Autom. RA-1 (1), 21–30 (1985).Google Scholar
12.Hasegawa, T. and Terasaki, H., “Collision avoidance: Divide – and – conquer approach by space characterization and intermediate goals,” IEEE Trans. Syst. Man Cybern. SMC-18 (3), 337–347 (1988).CrossRefGoogle Scholar
13.Hwang, Y. K. and Ahuja, N., “Gross motion planning – a survey,” ACM Comput. Surv. 24 (3), 219–291 (1992).CrossRefGoogle Scholar
14.Jun, S. and Shin, K. G., “A Probabilistic Approach to Collision-Free Robot Path Planning,” Proceedings of the IEEE International Conference on Robotics and Automation, Philadelphia, PH, USA (1988) pp. 220–225.Google Scholar
15.Keerthi, S. S. and Selvaraj, J., “A fast method of collision avoidance for an articulated two link planar robot using distance functions,” J. Ins. Electron. Telecom. Eng. 35 (4), 207–217 (1989).Google Scholar
16.Khouri, J. and Stelson, K. A., “Efficient algorithm for shortest path in 3-D with polyhedral obstacles,” Trans. ASME – J. Dyn. Syst. Meas. Control 8 (3), 433–436 (1989).CrossRefGoogle Scholar
17.Kohler, M. and Spreng, M., “Fast computation of the c-space of convex 2D algebraic objects,” Int. J. Rob. Res. 14 (6), 590–608 (1995).CrossRefGoogle Scholar
18.Lozano-Perez', T., “Spatial planning: A configuration space approach,” IEEE Trans. Comput. C-32 (2), 108–120 (1983).CrossRefGoogle Scholar
19.Lozano-Perez', T., “A simple motion planning algorithm for general robot manipulators,” IEEE Trans. Rob. Autom. RA-3 (3), 207–223 (1987).Google Scholar
20.Lumelsky, V. and Sun, K., “A Study of the Obstacle Avoidance Problem Based on the Deformation Retract Technique,” Proceedings of the 29th. IEEE Conference on Decision and Control, Honolulu, HI (Dec. 1990) pp. 1099–1104.Google Scholar
21.Lumelsky, V. and Sun, K., “A unified methodology for motion planning with uncertainty for 2-D and 3-D two-link robot arm manipulators,” Int. J. Rob. Res. 9 (5), 89–104 (1990).Google Scholar
22.Ralli, E. and Hirzinger, G., “Global and resolution complete path planner for up to 6 dof robot manipulators,” Proceedings of the IEEE International Conference on Robotics and Automation, Minneapolis, MN (Apr. 1996) pp. 3295–3302.CrossRefGoogle Scholar
23.Red, R. E. and Truong-Cao, H. V., “Configuration maps for robot path planning in two dimensions,” Trans. ASME – J. Dyn. Syst. Meas. Control 107, 292–298 (1985).Google Scholar
24.Red, W. E., Truong-Cao, H. V., Kim, K. H., “Robot path planning in three dimensions using the direct subspace,” Trans. ASME – J. Dyn. Syst. Meas. Control 109, 238–244 (1987).Google Scholar
25.Roy, D., “Study on the configuration space based algorithmic path planning of industrial robots in an unstructured congested 3-Dimensional space: An approach using visibility map,” J. Intell. Rob. Syst. 43 (2–4), 111–145 (2005).Google Scholar
26.Sacks, E. and Bajaj, C., “Sliced configuration spaces for curved planar bodies,” Int. J. Rob. Res. 17 (6), 639–651 (1998).CrossRefGoogle Scholar
27.Sacks, E., “Practical sliced configuration spaces for curved planar pairs,” Int. J. Rob. Res. 18 (1), 59–63 (1999).Google Scholar
28.Sachs, S., La Valle, S. M. and Rajko, S., “Visibility-based pursuit – evasion in an unknown planar environment,” Int. J. Rob. Res. 23 (1), 3–26 (2004).Google Scholar
29.Schwartz, J. T. and Sharir, M., “A survey of motion planning and related geometric algorithms,” Artif. Intell. 37, 157–169 (1988).Google Scholar
30.Slotine, J., Jacques, E. and Yang, H. S., “Improving the efficiency of time-optimal path following algorithm,” IEEE Trans. Rob. Autom. RA-5 (1), 118–124 (1989).CrossRefGoogle Scholar
31.Verwer, B. J. H., “A Multi-Resolution Workspace, Multi-Resolution Configuration Space Approach to Solve the Path Planning Problem,” Proceedings of the IEEE International Conference on Robotics and Automation, Cincinnati, OH, USA (1990) pp. 2107–2112.CrossRefGoogle Scholar
32.Welzl, E., “Constructing the visibility graph for n-line segments in o (n 2) time,” Inf. Process. Lett. 20, 167–171 (1985).Google Scholar
33.Wise, K. D. and Bowyer, A., “A survey of global configuration-space mapping techniques for a single robot in a static environment,” Int. J. Rob. Res. 19 (8), 762–779 (2000).Google Scholar
34.Yu, Y. and Gupta, K., “C-space entropy: A measure for view planning and exploration for general robot – sensor systems in unknown environment,” Int. J. Rob. Res. 23 (12), 1197–1223 (2004).Google Scholar
35.Zelinsky, A., “Using path transforms to guide the search for findpath in 2D,” Int. J. Rob. Res. 13 (4), 315–325 (1994).Google Scholar
You have
Access
- 5
- Cited by