Hostname: page-component-76dd75c94c-vpfzz Total loading time: 0 Render date: 2024-04-30T08:55:12.077Z Has data issue: false hasContentIssue false
  • English
  • Français

Safety-oriented path planning for articulated robots

Published online by Cambridge University Press:  21 February 2013

Bakir Lacevic  and
Paolo Rocco
Bakir Lacevic*
Affiliation:
University of Sarajevo, Faculty of Electrical Engineering, Sarajevo, Bosnia and Herzegovina
Paolo Rocco
Affiliation:
Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Milano, Italy
*
*Corresponding author. E-mail: bakir.lacevic@etf.unsa.ba
Get access Rights & Permissions [Opens in a new window]

Summary

This work presents an approach to motion planning for robotic manipulators that aims at improving path quality in terms of safety. Safety is explicitly assessed using the quantity called danger field. The measure of safety can easily be embedded into a heuristic function that guides the exploration of the free configuration space. As a result, the resulting path is likely to have substantially higher safety margin when compared to one obtained by regular planning algorithms. To this end, four planning algorithms have been proposed. The first planner is based on volume trees comprised of bubbles of free configuration space, while the remaining ones represent modifications of classical sampling-based algorithms. Several numerical case studies are carried out to validate and compare the performance of the presented algorithms with respect to classical planners. The results indicate significantly lower danger metric for paths obtained by safety-oriented planners even with some decrease in running time.

Keywords

Motion PlanningPath PlanningRedundant ManipulatorsSafety
Type
Articles
Information
Robotica , Volume 31 , Issue 6 , September 2013 , pp. 861 - 874
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.ANSI, ANSI/RIA R15.06-1999: Industrial Robots and Robot Systems—Safety Requirements (American National Standards Institute, New York, 1999).Google Scholar
2.ISO, Robots for Industrial Environments—Safety Requirements—Part 1: Robot. ISO10218 (International Organization for Standardization, Geneva, 2006).Google Scholar
3.Benjamini, Y., “Opening the box of a boxplot,” Am. Stat. 42 (4), 257–262 (1988).Google Scholar
4.Berenson, D., Siméon, T. and Srinivasa, S. S., “Addressing Cost-Space Chasms in Manipulation Planning,” Proceedings of the IEEE International Conference on Robotics and Automation, Shanghai, P.R. China (May 9–13, 2011) pp. 4561–4568.Google Scholar
5.Berenson, D., Srinivasa, S. and Kuffner, J., “Task space regions: A framework for pose-constrained manipulation planning,” Int. J. Robot. Res. 30 (1), 1435–1460 (2011).CrossRefGoogle Scholar
6.Berenson, D., Srinivasa, S. S., Ferguson, D., Collet, A. and Kuffner, J. J., “Manipulation Planning with Workspace Goal Regions,” Proceedings of the IEEE International Conference on Robotics and Automation, Piscataway, NJ, USA (May 17–12, 2009) pp. 1397–1403.Google Scholar
7.Bertram, D., Kuffner, J., Dillmann, R. and Asfour, T., “An Integrated Approach to Inverse Kinematics and Path Planning for Redundant Manipulators,” Proceedings of the IEEE International Conference on Robotics and Automation, Orlando, FL, USA (May 15–19, 2006) pp. 1874–1879.Google Scholar
8.Bohlin, R. and Kavraki, L. E., “Path Planning Using Lazy PRM,” Proceedings of the IEEE International Conference on Robotics and Automation, San Francisco, CA, USA (Apr. 24–18, 2000) pp. 521–528.Google Scholar
9.Brock, O. and Khatib, O., “Elastic strips: A framework for motion generation in human environments,” Int. J. Robot. Res. 21 (12), 1031–1052 (2002).CrossRefGoogle Scholar
10.Burdick, J. W., “On the Inverse Kinematics of Redundant Manipulators: Characterization of the Self-Motion Manifolds,” Proceedings of the IEEE International Conference on Robotics and Automation, Scottsdale, AZ, USA (May 14–19, 1989) pp. 264–270.Google Scholar
11.Canny, J. F., The Complexity of Robot Motion Planning (MIT Press, Cambridge, MA, 1988).Google Scholar
12.Choset, H., Burgard, W., Hutchinson, S., Kantor, G., Kavraki, L. E., Lynch, K. and Thrun, S., Principles of Robot Motion: Theory, Algorithms, and Implementation (MIT Press, Cambridge, MA, 2005).Google Scholar
13.Geraerts, R. and Overmars, M. H., “A Comparative Study of Probabilistic Roadmap Planners,” Proceedings of the Workshop on the Algorithmic Foundations of Robotics, Nice, France (Dec. 15–17, 2002) pp. 43–57.Google Scholar
14.Geraerts, R. and Overmars, M. H., “Creating high-quality paths for motion planning,” Int. J. Robot. Res. 26 (8), 845–863 (2007).CrossRefGoogle Scholar
15.Haddadin, S., Albu-Schäffer, A. and Hirzinger, G., “Requirements for safe robots: Measurements, analysis and new insights,” Int. J. Robot. Res. 28 (11–12), 1507–1527 (2009).CrossRefGoogle Scholar
16.Heinzmann, J. and Zelinsky, A., “Quantitative safety guarantees for physical human–robot interaction,” Int. J. Robot. Res. 22 (7–8), 479–504 (2003).CrossRefGoogle Scholar
17.Hsu, D., Kavraki, L. E., Latombe, J.-C., Motwani, R. and Sorkin, S., “On Finding Narrow Passages with Probabilistic Roadmap Planners,” Proceedings of the Third International Workshop on the Algorithmic Foundations of Robotics, Houston, TX, USA (Mar. 5–7, 1998) pages 141–153.Google Scholar
18.Ikuta, K., Ishii, H. and Nokata, M., “Safety evaluation method of design and control for human-care robots,” Int. J. Robot. Res. 22 (5), 281–298 (2003).CrossRefGoogle Scholar
19.Ikuta, K., Nokata, M. and Ishii, H., “General danger evaluation method for control strategy of human-care robot,” J. Robot. Soc. Japan 19 (1), 81–90 (2001).CrossRefGoogle Scholar
20.Jaillet, L., Cortes, J. and Simeon, T., “Sampling-based path planning on configuration-space costmaps,” IEEE Trans. Robot. 26 (4), 635–646 (2010).CrossRefGoogle Scholar
21.Kaindl, H. and Kainz, G., “Bidirectional heuristic search reconsidered,” J. Artif. Int. Res. 7 (1), 283–317 (1997).Google Scholar
22.Karaman, S. and Frazzoli, E., “Sampling-based algorithms for optimal motion planning,” Int. J. Robot. Res. 30 (7), 846–894 (2011).CrossRefGoogle Scholar
23.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
24.Kuffner, J. J. Jr. 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, CA, USA (Apr. 24–28, 2000) pp. 995–1001.Google Scholar
25.Kulic, D., Safety for Human–Robot Interaction Ph.D Thesis (Vancouver, Canada: The University of British Columbia, 2005).Google Scholar
26.Kulic, D. and Croft, E. A., “Affective state estimation for human–robot interaction,” IEEE Trans. Robot. 23 (5), 991–1000 (2007).CrossRefGoogle Scholar
27.Kulić, D. and Croft, E. A., “Safe planning for human–robot interaction,” J. Robot. Syst. 22 (7), 383–396 (2005).CrossRefGoogle Scholar
28.Kulic, D. and Croft, E. A., “Real-time safety for human–robot interaction,” Robot. Auton. Syst. 54 (1), 1–12 (2006).CrossRefGoogle Scholar
29.Lacevic, B., Safe Motion Planning and Control for Robotic Manipulators Ph.D Thesis (Milan: Politecnico di Milano, 2011).CrossRefGoogle Scholar
30.Lacevic, B. and Rocco, P., “Kinetostatic Danger Field: Novel Safety Assessment for Human–Robot Interaction,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei (Oct. 18–22, 2010) pp. 2169–2174.Google Scholar
31.Lacevic, B. and Rocco, P., “Sampling-Based Safe Path Planning for Robotic Manipulators,” IEEE International Conference on Emerging Technologies and Factory Automation, Bilbao, Spain (Sep. 13–16, 2010) pp. 1–7.Google Scholar
32.Lacevic, B. and Rocco, P., “Towards a Complete Safe Path Planning for Robotic Manipulators,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei (Oct. 18–22, 2010) pp. 5366–5371.Google Scholar
33.Lacevic, B., Rocco, P. and Strandberg, M., “Safe Motion Planning for Articulated Robots Using RRTs,” XXIII International Symposium on Information, Communication and Automation Technologies - ICAT, Sarajevo, Bosnia and Herzegovina (Oct. 27–29, 2011) pp. 1–7.Google Scholar
34.Latombe, J.-C., Robot Motion Planning (Kluwer Academic, Norwell, MA, 1991).CrossRefGoogle Scholar
35.LaValle, S. M. and Kuffner, J. J., “Rapidly-exploring Random Trees: Progress and Prospects,” Proceedings of the Workshop on the Algorithmic Foundations of Robotics, Hanover, NH, USA (Mar. 16–18, 2000).Google Scholar
36.LaValle, S. M., Planning Algorithms (Cambridge University Press, New York, 2006).CrossRefGoogle Scholar
37.Mainprice, J., Sisbot, E. A., Simeon, T. and Alami, R., “Planning Safe and Legible Hand-Over Motions for Human–Robot Interaction,” Proceedings of the IARP Workshop on Technical Challenges for Dependable Robots in Human Environments, Toulouse, France (Jun. 16–17, 2010) pp. 153–158.Google Scholar
38.Melchior, P., Orsoni, B., Lavialle, O., Poty, A. and Oustaloup, A., “Consideration of obstacle danger level in path planning using A* and fast-marching optimisation: Comparative study,” Signal Process. 83 (11), 2387–2396 (2003).CrossRefGoogle Scholar
39.Nokata, M., Ikuta, K. and Ishii, H., “Safety-optimizing method of human-care robot design and control,” IEEE International Conference on Robotics and Automation, Washington, DC, USA (May 11–15, 2002), pp. 1991–1996.Google Scholar
40.Quinlan, S., Real-Time Modification of Collision-Free Paths Ph.D Thesis (Stanford, CA: Stanford University, 1995).Google Scholar
41.Roy, D., “Algorithmic path planning of static robots in three dimensions using configuration space metrics,” Robotica 29 (2), 295–315 (2011).CrossRefGoogle Scholar
42.Sánchez-Ante, G. and Latombe, J.-C., “A Single-query Bi-directional Probabilistic Roadmap Planner with Lazy Collision Checking,” Proceedings of the 10th International Symposium on Robotics Research, Lorne, Australia (Nov. 9–12, 2001) pp. 403–417.Google Scholar
43.Sent, D. and Overmars, M. H., “Motion Planning in Environments with Dangerzones,” Proceedings of the IEEE International Conference on Robotics and Automation, Seoul, Korea (May 21–26, 2001) pp. 1488–1493.Google Scholar
44.Siciliano, B., Sciavicco, L., Villani, L. and Oriolo, G., Robotics Modelling, Planning and Control (Springer, London, 2009).Google Scholar
45.Stilman, M., “Global manipulation planning in robot joint space with task constraints,” IEEE Trans. Robot. 26 (3), 576–584 (2010).CrossRefGoogle Scholar
46.Strandberg, M., “Augmenting RRT-Planners with Local Trees,” Proceedings of the IEEE International Conference on Robotics and Automation, New Orleans, LA, USA (Apr. 26–May 1, 2004) pp. 3258–3262.Google Scholar
47.Urmson, C. and Simmons, R., “Approaches for Heuristically Biasing RRT Growth,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, NV, USA (Oct. 27–31, 2003).Google Scholar
48.Vande Weghe, M., Ferguson, D. and Srinivasa, S. S., “Randomized Path Planning for Redundant Manipulators without Inverse Kinematics,” Proceedings of the IEEE-RAS International Conference on Humanoid Robots, Pittsburgh, PA, USA (Nov. 29–Dec. 1, 2007), pp. 477–482.Google Scholar
49.Vázquez, A. S. and Adán, A., “Nonprobabilistic anytime algorithm for high-quality trajectories in high-dimensional spaces,” Robotica 30 (2), 289–303 (2012).CrossRefGoogle Scholar
50.Wolovich, W. A. and Elliott, H., “A Computational Technique for Inverse Kinematics,” Proceedings of the IEEE International Conference on Decision and Control, Las Vegas, NV, USA (Dec. 1984) pp. 1359–1363.Google Scholar
51.Yang, Y. and Brock, O., “Elastic roadmaps–motion generation for autonomous mobile manipulation,” Auton. Robots 28 (1), 113–130 (2010).CrossRefGoogle Scholar
52.Zinn, M., Khatib, O., Roth, B. and Salisbury, J. K., “Playing it safe [human-friendly robots],” IEEE Robot. Autom. Mag. 11 (2), 12–21 (2004).CrossRefGoogle Scholar