Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-25T05:36:23.308Z Has data issue: false hasContentIssue false
  • English
  • Français

An approach for real-time motion planning of an inchworm robot in complex steel bridge environments

Published online by Cambridge University Press:  11 February 2016

David Pagano  and
Dikai Liu Open the ORCID record for Dikai Liu [Opens in a new window]
David Pagano*
Affiliation:
Centre for Autonomous Systems, University of Technology Sydney Broadway, NSW 2007, Australia. E-mail: [email protected]
Dikai Liu
Affiliation:
Centre for Autonomous Systems, University of Technology Sydney Broadway, NSW 2007, Australia. E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Path planning can be difficult and time consuming for inchworm robots especially when operating in complex 3D environments such as steel bridges. Confined areas may prevent a robot from extensively searching the environment by limiting its mobility. An approach for real-time path planning is presented. This approach first uses the concept of line-of-sight (LoS) to find waypoints from the start pose to the end node. It then plans smooth, collision-free motion for a robot to move between waypoints using a 3D-F2 algorithm. Extensive simulations and experiments are conducted in 2D and 3D scenarios to verify the approach.

Keywords

Inchworm robotMotion planningComplex environmentsReal-timeLine-of-sight
Type
Articles
Information
Robotica , Volume 35 , Issue 6 , June 2017 , pp. 1280 - 1309
Copyright
Copyright © Cambridge University Press 2016 

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. Amanatides, J. and Woo, A., “A Fast Voxel Traversal Algorithm for Ray Tracing,” Proceedings of the Eurographics, Amsterdam, Netherlands (1987) pp. 3–10.Google Scholar
2. Borenstein, J. and Koren, Y., “Real-time obstacle avoidance for fast mobile robots,” IEEE Trans. Syst. Man Cybern. 19 (5), 1179–1187 (1989).Google Scholar
3. Buniyamin, N., Sariff, N., Ngah, W. Wan and Mohamad, Z., “Robot global path planning overview and a variation of ant colony system algorithm,” Int. J. Math. Comput. Simul. 5 (1), 9–16 (2011).Google Scholar
4. Choset, H., “Coverage for robotics: A survey of recent results,” Ann. Math. Artif. Intell. 31 (1–4), 113–126 (2001).Google Scholar
5. Chotiprayanakul, P., Liu, D. and Dissanayake, G., “Human-robot-environment interaction interface for robotic Grit-blasting of complex steel bridges,” Autom. Constr. 27 (0), 11–23 (2012).Google Scholar
6. Clifton, M., Paul, G., Kwok, N., Liu, D. K. and Wang, D.-L., “Evaluating Performance of Multiple RRTs,” Proceedings of the IEEE/ASME International Conference Mechatronic and Embedded Systems and Applications, Beijing, China (2008) pp. 564–569.Google Scholar
7. Dorigo, M., Maniezzo, V. and Colorni, A., “Ant system: Optimization by a colony of cooperating agents,” IEEE Trans. Syst. Man Cybern. Part B 26 (1), 29–41 (1996).Google Scholar
8. Dung, N. A. and Shimada, A., “A Path-planning Algorithm for Humanoid Climbing Robot using Kinect Sensor,” Proceedings of the SICE Annual Conference (SICE), Japan (2014) pp. 1549–1554.Google Scholar
9. Ferguson, D., Kalra, N. and Stentz, A., “Replanning with RRTs,” Proceedings of the IEEE International Conference on Robotics and Automation, ICRA, Orlando, Florida (2006) pp. 1243–1248.Google Scholar
10. Flacco, F., Kröger, T., Luca, A. D. and Khatib, O., “A Depth Space Approach to Human-Robot Collision Avoidance,” Proceedings of the IEEE International Conference on Robotics and Automation, Saint Paul, MN, USA (2012) pp. 338–345.Google Scholar
11. Fu, Y., Ding, M. and Zhou, C., “Phase angle-encoded and quantum-behaved particle swarm optimization applied to three-dimensional route planning for UAV,” IEEE Trans. Syst. Man Cybern. Part A 42 (2), 511–526 (2012).Google Scholar
12. Gammell, J. D. Srinivasa, S. S. and Barfoot, T. D., “Informed RRT*: Optimal Incremental Path Planning Focused through an Admissible Ellipsoidal Heuristic,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), volume abs/1404.2334, Chicago, Illinois (2014) pp. 2997–3004.Google Scholar
13. Ge, S. S., Zhang, Q., Abraham, A. T. and Rebsamen, B., “Simultaneous path planning and topological mapping (SP2ATM) for environment exploration and goal oriented navigation,” Robot. Auton. Syst. 59 (3–4), 228–242 (2011).Google Scholar
14. Haddadin, S., Belder, R. and Albu-Schaeffer, A., “Dynamic Motion Planning for Robots in Partially Unknown Environments,” Proceedings of the 18th IFAC World Congress, World Congress, International Federation of Automatic Control. Milano, Italy, vol. 18 (2011) pp. 6842–6850.Google Scholar
15. Henderson, J. M. “Human gaze control during real-world scene perception,” Trends Cogn. Sci. 7 (11), 498–504 (2003).Google Scholar
16. Jain, A., Killpack, M. D., Edsinger, A. and Kemp, C. C., “Reaching in clutter with whole-arm tactile sensing,” Int. J. Robot. Res. 32 (4), 458–482 (2013).CrossRefGoogle Scholar
17. Jaradat, M. A. K., Garibeh, M. H. and Feilat, E. A., “Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field,” Soft Comput. 16 (1), 153–164 (2012).Google Scholar
18. Kalra, N., Ferguson, D. and Stentz, A., “Incremental reconstruction of generalized Voronoi diagrams on grids,” Robot. Auton. Syst. 57 (2), 123–128 (2009). Selected papers from 9th International Conference on Intelligent Autonomous Systems (IAS-9).CrossRefGoogle Scholar
19. Karaman, S. and Frazzoli, E., “Sampling-Based Algorithms for Optimal Motion Planning,” CoRR, abs/1105.1186:1-76 (2011).Google Scholar
20. Kennedy, J. and Eberhart, R., “Particle Swarm Optimization,” Proceedings IEEE International Conference on Neural Networks, vol. 4, Perth, Western Australia (1995) pp. 1942–1948.Google Scholar
21. Khatib, O., “Real-Time Obstacle Avoidance for Manipulators and Mobile Robots,” Proceedings IEEE International Conference on Robotics and Automation, vol. 2, St. Louis, Missouri (1985) pp. 500–505.Google Scholar
22. Koren, Y. and Borenstein, J., “Potential Field Methods and Their Inherent Limitations for Mobile Robot Navigation,” Proceedings of the IEEE International Conference on Robotics and Automation, vol. 2, Sacramento, California (1991) pp. 1398–1404.Google Scholar
23. Lepora, N. F., Verschure, P. and Prescott, T. J., “The state of the art in biomimetics,” Bioinspiration Biomimetics 8 (1), 1–11 (2013).Google Scholar
24. Li, Q., Wang, L., Chen, B. and Zhou, Z., “An Improved Artificial Potential Field Method for Solving Local Minimum Problem,” Proceedings of the 2nd International Conference on Intelligent Control and Information Processing (ICICIP), vol. 1, Harbin, China (2011) pp. 420–424.Google Scholar
25. Mujahed, M., Jaddu, H., Fischer, D. and Mertsching, B., “Tangential Closest Gap based (TCG) Reactive Obstacle Avoidance Navigation for Cluttered Environments,” Proceedings of the IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), Linköping, Sweden (2013) pp. 1–6.Google Scholar
26. Murphy, M. P., Kute, C., Mengüç, Y. and Sitti, M., “Waalbot II: Adhesion recovery and improved performance of a climbing robot using fibrillar adhesives,” Int. J. Robot. Res. 30 (1), 118–133 (2011).Google Scholar
27. New South Wales Government (2011). WorkCover.Google Scholar
28. Nia, D. N., Tang, H. S., Karasfi, B., Motlagh, O. R. E. and Kit, A. C., “Virtual force field algorithm for a behaviour-based autonomous robot in unknown environments,” Proc. Inst. Mech. Eng. Part I: J. Syst. Control Eng. 225 (1), 51–62 (2011).Google Scholar
29. Nourani-Vatani, N., Bosse, M., Roberts, J. and Dunbabin, M., “Practical Path Planning and Obstacle Avoidance for Autonomous Mowing,” Proceedings of the Australasian Conference of Robotics and Automation, Auckland, New Zealand (2006) pp. 1–9.Google Scholar
30. Pagano, D., Liu, D. and Waldron, K., “A Method for Optimal Design of an Inchworm Climbing Robot,” Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO), Guangzhou, China (2012) pp. 1293–1298.Google Scholar
31. Paul, G., Liu, D. and Kirchner, N., “An Algorithm for Surface Growing from Laser Scan Generated Point Clouds,” Robotic Welding, Intelligence and Automation, Springer, New York (2007) pp. 481–491.Google Scholar
32. Paul, G., Webb, S., Liu, D. and Dissanayake, G., “Autonomous robot manipulator-based exploration and mapping system for bridge maintenance,” Robot. Auton. Syst. 59 (7–8), 543–554 (2011).Google Scholar
33. Payton, D. W., Daily, M. J., Hoff, B., Howard, M. D. and Lee, C. L., “Pheromone Robotics,” Proceedings of the Intelligent Systems and Smart Manufacturing, International Society for Optics and Photonics, Boston, Massachusetts (2001) pp. 67–75.Google Scholar
34. Qureshi, A., Iqbal, K., Qamar, S., Islam, F., Ayaz, Y. and Muhammad, N., “Potential Guided Directional-RRT* for Accelerated Motion Planning in Cluttered Environments,” Proceedings of the IEEE International Conference on Mechatronics and Automation (ICMA), Takamatsu, Kagawa, Japan (2013) pp. 519–524.Google Scholar
35. Rashid, A. T., Ali, A. A., Frasca, M. and Fortuna, L., “Path planning with obstacle avoidance based on visibility binary tree algorithm,” Robot. Auton. Syst. 61 (12), 1440–1449 (2013).Google Scholar
36. Romey, P., Nhan, A., Williams, K. and Dunn, M., “Sydney Harbour Bridge Conservation Management Plan 2007,” Technical report, Roads and Traffic Authority.Google Scholar
37. Schmidt, D. and Berns, K., “Climbing robots for maintenance and inspections of vertical structures: A survey of design aspects and technologies,” Robot. Auton. Syst. 61 (12), 1288–1305 (2013).CrossRefGoogle Scholar
38. Shiller, Z. and Dubowsky, S., “On computing the Global time-optimal motions of robotic manipulators in the presence of obstacles,” IEEE Trans. Robot. Autom. 7 (6), 785–797 (1991).Google Scholar
39. Shiomi, M., Zanlungo, F., Hayashi, K. and Kanda, T., “Towards a socially acceptable collision avoidance for a mobile robot navigating among pedestrians using a pedestrian model,” Int. J. Soc. Robot. 6 (3), 443–455 (2014).CrossRefGoogle Scholar
40. Sintov, A., Avramovich, T. and Shapiro, A., “Design and motion planning of an autonomous climbing robot with claws,” Robot. Auton. Syst. 59 (11), 1008–1019 (2011).Google Scholar
41. Tanzmeister, G., Friedl, M., Wollherr, D. and Buss, M., “Path Planning on Grid Maps with Unknown Goal Poses,” Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems-(ITSC), IEEE, The Hague, Netherlands (2013) pp. 430–435.Google Scholar
42. Wang, D., Kwok, N. M., Liu, D. K., Lau, H. and Dissanayake, G., “PSO-Tuned F2 Method for Multi-Robot Navigation,” Proceedings of the IEEERSJ International Conference on Intelligent Robots and Systems IROS, San Diego, California (2007) pp. 3765–3770.Google Scholar
43. Ward, P., Paul, G., Quin, P., Pagano, D., Yang, C.-H., Liu, D., Waldron, K., Dissanayake, G., Brooks, P., Mann, P., Kaluarachchi, W., Manamperi, P. and Matkovic, L., “Climbing Robot For Steel Bridge Inspection: Design Challenges,” Proceedings of the 9th Austroads Bridge Conference, Sydney, New South Wales (2014) pp. 1–12.Google Scholar
44. Wen, S., Zheng, W., Zhu, J., Li, X. and Chen, S., “Elman fuzzy adaptive control for obstacle avoidance of mobile robots using hybrid force/position incorporation,” IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 42 (4), 603–608 (2012).Google Scholar