Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-12-06T17:18:16.282Z Has data issue: false hasContentIssue false

A novel movement-based operation method for dual-arm rescue construction machinery

Published online by Cambridge University Press:  11 August 2014

Cheng Fang*
Affiliation:
Robotics Institute, Department of Mechanical Engineering and Automation, Beihang University, Beijing City, 100191, P. R. China
Xilun Ding
Affiliation:
Robotics Institute, Department of Mechanical Engineering and Automation, Beihang University, Beijing City, 100191, P. R. China
*
*Corresponding author. E-mail: fcdean@163.com

Summary

The issue of the operation method for dual-arm rescue construction machinery is investigated in this paper. To increase its operational efficiency and to save more time at rescue sites, some operating strategies of the human arm are employed to design a novel operation method for construction machinery. On the basis of that, a novel and anthropomorphic task-motion planning and performing framework for rescue construction machinery is established. Firstly, the main tasks construction machinery encounter are summarized, and then, these tasks are decomposed to several manipulation and movement sequences. Finally, several frequently used movements, which consist of some basic movement elements, are designed to be intuitive movement primitives coordinating related movement elements simultaneously to improve the operational efficiency, which forms a novel operation method for rescue construction machinery. Additionally, in order to avoid the potential collision between the dual arms, a self-collision avoidance surveillance method is proposed to guarantee the safety of the novel operation method. An application case is presented to introduce the proposed method specifically, and a typical simulation of a dual-arm grip-and-cut task is carried out to verify the feasibility and effectiveness of the framework.

Type
Articles
Copyright
Copyright © Cambridge University Press 2014 

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

3.Hitachi Construction Machinery, ASTACO NEO, http://www.hitachi-kenki.co.jp/products/hot/astaco_neoindex.html, 2011.Google Scholar
5.Smith, C.et al., “Dual arm manipulation–a survey,” Robot. Auton. Syst. 60 (10), 13401353 (2012).CrossRefGoogle Scholar
6.Yamada, Y., Nagamatsu, S. and Sato, Y., “Development of Multi-Arm Robots for Automobile Assembly,” IEEE International Conference on Robotics and Automation, Nagoya, Japan, (1995) pp. 2224–2229.Google Scholar
7.Zheng, Y. F. and Chen, M. Z., “Trajectory planning for two manipulators to deform flexible beams,” Robot. Auton. Syst. 12 (1–2), 5567 (1994).CrossRefGoogle Scholar
8.Maitin-Shepard, J., Cusumano-Towner, M., Lei, J. and Abbeel, P., “Cloth Grasp Point Detection based on Multiple-View Geometric Cues with Application to Robotic Towel Folding,” IEEE International Conference on Robotics and Automation, Anchorage, Alaska, (2010) pp. 2308–2315.Google Scholar
9.Mukai, T., Hirano, S., Nakashima, H., Kato, Y., Sakaida, Y., Guo, S. and Hosoe, S., “Development of a Nursing-Care Assistant Robot Riba that can Lift a Human in its Arms,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, (2010) pp. 5996–6001.Google Scholar
10.Wimböck, T. and Ott, C., “Dual-Arm Manipulation,” Towards Service Robots for Everyday Environments, Springer Tracts in Advanced Robotics, vol. 76, (Springer, Berlin Heidelberg, 2012) pp. 353366.CrossRefGoogle Scholar
11.Edsinger, A. and Kemp, C. C., “Two Arms are Better Than One: A Behavior-Based Control System for Assistive Bimanual Manipulation,” Recent Progress in Robotics: Viable Robotic Service to Human. Lecture Notes in Control and Information Sciences, vol. 370, (Springer, Berlin Heidelberg, 2008) pp. 345355.Google Scholar
12.Schaal, S., Kotosaka, S. and Sternad, D., “Nonlinear Dynamical Systems as Movement Primitives,” IEEE International Conference on Humanoid Robotics, Cambridge, USA (2000) pp. 1–11.Google Scholar
13.Tadokoro, S., Takamori, T., Osuka, K. and Tsurutani, S., “Investigation Report of the Rescue Problem at Hanshin-Awaji Earthquake in Kobe,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Takamatsu, Japan (2000) pp. 1880–1885.Google Scholar
14.Asfour, T., Azad, P., Gyarfas, F. and Dillmann, R., “Imitation learning of dual-arm manipulation tasks in humanoid robots,” Int. J. Humanoid Robot. 5 (2), 289308 (2008).CrossRefGoogle Scholar
15.Patel, R. V., Shadpey, F., Ranjbaran, F. and Angeles, J., “A collision-avoidance scheme for redundant manipulators theory and experiments,” J. Robot. Syst. 22 (12), 737757 (2005).CrossRefGoogle Scholar
16.Chittawadigi, R. G. and Saha, S. K., “An Analytical Method to Detect Collision between Cylinders Using Dual Number Algebra,” International Conference onIntelligent Robots and Systems, Tokyo, Japan, (2013) pp. 5353–5358.Google Scholar
17.Okada, K., Inaba, M. and Inoue, H., “Real-Time and Precise Self Collision Detection System for Humanoid Robots,” International Conference on Robotics and Automation, Barcelona, Spain, (2005) pp. 1072–1077.Google Scholar
18.Hudson, T. C., Lin, M. C., Cohen, J., Gottschalk, S. and Manocha, D., “V-COLLIDE: Accelerated Collision Detection for VRML,” Proceedings of 2nd Annual Symposium on the Virtual Reality Modelling Language, Monterey, USA, (1997) pp. 117–124.Google Scholar
19.Okada, K. and Inaba, M., “A Hybrid Approach to Practical Self Collision Detection System of Humanoid Robot,” International Conference on Intelligent Robots and Systems, Beijing, China, (2006) pp. 3952–3957.Google Scholar
20.Flash, T. and Hochner, B., “Motor primitives in vertebrates and invertebrates,” Curr. Opin. Neurobiol. 15 (6), 660666 (2005).CrossRefGoogle ScholarPubMed
21.Mussa-Ivaldi, F. A. and Solla, S. A., “Neural primitives for motion control,” IEEE J. Ocean. Eng. 2 (3), 640650 (2004).CrossRefGoogle Scholar
22.Mussa-Ivaldi, F. A. and Bizzi, E., “Motor learning through the combination of primitives,” Philos. Trans. R. Soc. Lon. B, Biol. Sci. 355, 17551769 (2000).CrossRefGoogle ScholarPubMed
23.Mussa-Ivaldi, F. A. and Solla, S. A., “Neural primitives for motion control,” IEEE J. Ocean. Eng. 2 (3), 640650 (2004).CrossRefGoogle Scholar
24.Hestenes, D., “Invariant body kinematics: II. Reaching and neurogeometry,” Neural Netw. 7 (1), 7981 (1994).CrossRefGoogle Scholar
25.Kober, J. and Peters, J., “Imitation and reinforcement learning practical algorithms for motor primitives in robotics,” IEEE Robot. Autom. Mag. 17 (2), 5562 (2010).CrossRefGoogle Scholar
26.Pellis, S. M., “Conservative motor systems, behavioral modulation and neural plasticity,” Behav. Brain Res. 214 (1), 2529 (2010).CrossRefGoogle ScholarPubMed
27.Giszter, S. F. and Hart, C. B., “Biomimetic control for redundant and high degree of freedom limb systems: neurobiological modularity,” Smart Struct. Syst. 7 (3), 169184 (2011).CrossRefGoogle Scholar
28.Pastra, K. and Aloimonos, Y., “The minimalist grammar of action,” Phil. Trans. R. Soc. B-Biol. Sci. 367 (1585), 103117 (2012).CrossRefGoogle ScholarPubMed
29.Ding, X. and Fang, C., “A novel method of motion planning for an anthropomorphic arm based on movement primitives,” IEEE/ASME Trans. Mechatronics 18 (2), 624636 (2013).CrossRefGoogle Scholar
30.Denavit, J. and Hartenberg, R. S., “A kinematic notation for lower-pair mechanisms based on matrices,” J. Appl. Mech. 22 (1), 215221 (1955).CrossRefGoogle Scholar
31.Ericson, C., Real-Time Collision Detection (Morgan Kaufmann, San Francisco, 2005).Google Scholar
32.Ishii, A., “Operation System of a Double-Front Work Machine for Simultaneous Operation,” 23rd International Symposium Automation and Robotics in Construction, Tokyo, Japan, (2006) pp. 539–542.Google Scholar
33.Kamezaki, M., Iwata, H. and Sugano, S., “Operator support system based on primitive static states in intelligent operated work machines,” Adv. Robot. 23 (10), 12811297 (2009).CrossRefGoogle Scholar