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Collision-free motion planning of a virtual arm based on the FABRIK algorithm

Published online by Cambridge University Press:  18 April 2016

Songqiao Tao  and
Yumeng Yang
Songqiao Tao*
Affiliation:
Department of Mechanic and Electronic Engineering, Wuhan Technical College of Communications, 6 Baishazhou Road, Wuhan City, Hubei Province, 430065, P. R. China
Yumeng Yang
Affiliation:
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan City, Hubei Province, 430074, P. R. China Email: yym20071202@163.com
*
*Corresponding author. E-mail: taosongqiao@163.com
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Summary

Collision-free motion planning of a virtual arm is an intractable task in high-interference environments. In this paper, an approach for collision-free motion planning of a virtual arm based on the forward and backward reaching inverse kinematics (FABRIK) algorithm is proposed. First, a random rotation strategy and local optimum-seeking technology are introduced to improve the FABRIK algorithm in order to avoid obstacles. The improvement FABRIK algorithm is used to design the final grasping posture of a virtual arm according the target position. Then, a bidirectional rapidly exploring random tree (Bi-RRT) algorithm is adopted to explore the process postures from a given initial posture to the final grasping posture. Different from the existing method, the proposed Bi-RRT algorithm in this paper plans the motions of a virtual arm in a seven-dimensional angle space, and the final grasping posture is automatically designed using the obstacle-avoidance FABRIK algorithm instead of the manual design. Finally, the post-processing technique is introduced to remove redundant nodes from the planned motions. This procedure has resolved the problem that the Bi-RRT algorithm is a random algorithm. The experimental results show the proposed method for collision-free motion planning of a virtual arm is feasible.

Keywords

Collision-freeMotion planningInverse kinematicsObstacle avoidanceVirtual arm
Type
Articles
Information
Robotica , Volume 35 , Issue 6 , June 2017 , pp. 1431 - 1450
Copyright
Copyright © Cambridge University Press 2016 

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References

1. Aristidou, A. and Lasenby, J., “FABRIK: A fast, iterative solver for the inverse kinematics problem,” Graph. Models 73 (5), 243–260 (2011).CrossRefGoogle Scholar
2. Aristidou, A., Chrysanthou, Y. and Lasenby, J. “Extending FABRIK with model constraints,” Comput. Animat. Virtual Worlds DOI: 10.1002/cav.1630. (2015).Google Scholar
3. LaValle, S. M., “Rapidly-exploring random trees: A new tool for path planning,” Technical Report (Computer Science Department, Iowa State University, 1998) (TR 98-11).Google Scholar
4. Kuffner, J. J. and LaValle, S. M., “RRT-connect: An efficient approach to single-query path planning,” IEEE Int. Conf. Robot. Autom. 2, 995–1001 (2000).Google Scholar
5. Schreiber, G. and Hirzinger, G., “Singularity Consistent Inverse Kinematics by Enhancing the Jacobian Transpose,” In: Advances in Robot Kinematics: Analysis and Control (Jadran, L. and Husty, M. L., eds.) (Springer, Netherlands, 1998), pp. 475–482.Google Scholar
6. Bhatti, Z., Shah, A., Shahidi, F. and Karbisi, M., “Forward and inverse kinematics seamless matching using Jacobian,” Sindh Univ. Res. J. (SURJ) 45 (2), 387–392 (2014).Google Scholar
7. Nakamura, Y. and Hanafusa, H., “Inverse kinematic solutions with singularity robustness for robot manipulator control,” J. Dyn. Syst. Meas. Control, 108 (3), 163–171 (1986).Google Scholar
8. Cui, L. and Dai, J. S., “Reciprocity-based singular value decomposition for inverse kinematic analysis of the metamorphic multifingered hand,” J. Mech. Robot. 4 (3), 034502 (2012).Google Scholar
9. Wampler, C. W., “Manipulator inverse kinematic solutions based on vector formulations and damped least-squares methods,” IEEE Trans. Syst. Man Cybern. 16 (1), 93–101 (1986).CrossRefGoogle Scholar
10. Phuoc, L. M., Martinet, P., Lee, S. and Kim, H., “Damped least square based genetic algorithm with Ggaussian distribution of damping factor for singularity-robust inverse kinematics,” J. Mech. Sci. Technol. 22 (7), 1330–1338 (2008).Google Scholar
11. Welman, C., “Inverse Kinematics And Geometric Constraints For Articulated Figure Manipulation,” Master Dissertation, (Simon Fraser University, Department of Computer Science, 1993).Google Scholar
12. Meredith, M. and Maddock, S., “Adapting motion capture data using weighted real-time inverse kinematics,” Comput. Entertainment (CIE) 3 (1), 1–15 (2005).Google Scholar
13. Tejomurtula, S. and Kak, S., “Inverse kinematics in robotics using neural networks,” Inf. Sci. 116 (2), 147–164 (1999).Google Scholar
14. Karlık, B. and Aydın, S., “An improved approach to the solution of inverse kinematics problems for robot manipulators,” Eng. Appl. Artif. Intell. 13 (2), 159–164 (2000).Google Scholar
15. Mayorga, R. V. and Sanongboon, P., “Inverse kinematics and geometrically bounded singularities prevention of redundant manipulators: An artificial neural network approach,” Robot. Auton. Syst. 53 (3), 164–176 (2005).Google Scholar
16. Köker, R., “A genetic algorithm approach to a neural-network-based inverse kinematics solution of robotic manipulators based on error minimization,” Inf. Sci. 222 (10), 528–543 (2013).CrossRefGoogle Scholar
17. Köker, R., Çakar, T. and Sari, Y., “A neural-network committee machine approach to the inverse kinematics problem solution of robotic manipulators,” Eng. Comput. 30 (4), 641–649 (2014).Google Scholar
18. Daya, B., Khawandi, S. and Akoum, M., “Applying neural network architecture for inverse kinematics problem in robotics,” J. Softw. Eng. Appl. 3 (03), 230 (2010).Google Scholar
19. Wang, L. C. and Chen, C. C., “A combined optimization method for solving the inverse kinematics problems of mechanical manipulators,” IEEE Trans. Robot. Autom. 7 (4), 489–499 (1991).CrossRefGoogle Scholar
20. Canutescu, A. A. and Dunbrack, R. L., “Cyclic coordinate descent: A robotics algorithm for protein loop closure,” Protein Sci. 12 (5), 963–972 (2003).Google Scholar
21. Olsen, A. L. and Petersen, H. G., “Inverse kinematics by numerical and analytical cyclic coordinate descent,” Robotica 29 (04), 619–626 (2011).Google Scholar
22. Kenwright, B., “Inverse kinematics–cyclic coordinate descent (CCD),” J. Graph. Tools 16 (4), 177–217 (2012).Google Scholar
23. Müller-Cajar, R. and Mukundan, R., “Triangulation: A New Algorithm for Inverse Kinematics,” Proceedings of the Image and Vision Computing, University of Waikato Hamilton, New Zealand, (2007) pp. 181–186.Google Scholar
24. Mukundan, R., “A robust inverse kinematics algorithm for animating a joint chain,” Int. J. Comput. Appl. Technol. 34 (4), 303–308 (2009).Google Scholar
25. Khatib, O., “Real-time obstacle avoidance for manipulators and mobile robots,” The Int. J. Robot. Res. 5 (1), 90–98 (1986).CrossRefGoogle Scholar
26. Koren, Y. and Borenstein, J., “Potential Field Methods and Their Inherent Limitations for Mobile Robot Navigation,” IEEE International Conference on Robotics and Automation, Sacramento, California, (1991) pp. 1398–1404.Google Scholar
27. Ren, J., McIsaac, K. and Patel, R. V., “Modified Newton's method applied to potential field-based navigation for mobile robots,” IEEE Trans. Robot. 22 (2), 384–391 (2006).Google Scholar
28. Xiao, B., Yu, L., Li, S. and Chen, R., “Research of escaping local minima strategy for artificial potential field,” J. Syst. Simul. 19 (19), 4495–4503 (2007).Google Scholar
29. Jia, Q. and Wang, X., “Path planning for mobile robots based on a modified potential model,” IEEE, International Conference on Mechatronics and Automation, Changchun, China, (2009) pp. 4946–4951.Google Scholar
30. Lee, J., Nam, Y., Hong, S. and Cho, W. “New potential functions with random force algorithms using potential field method,” J. Intell. Robot. Syst. 66 (3), 303–319 (2012).Google Scholar
31. Velagic, J., Lacevic, B. and Osmic, N., “Efficient Path Planning Algorithm for Mobile Robot Navigation with a Local Minima Problem Solving,” IEEE International Conference on Industrial Technology, Mumbai, India, (2006) pp. 2325–2330.Google Scholar
32. Raja, R., Dutta, A. and Venkatesh, K. S., “New potential field method for rough terrain path planning using genetic algorithm for a 6-wheel rover,” Robot. Auton. Syst. 72 (10), 295–306 (2015).CrossRefGoogle Scholar
33. Kavraki, L. E., Švestka, 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).Google Scholar
34. Kavraki, L. E., Kolountzakis, M. N. and Latombe, J. C., “Analysis of probabilistic roadmaps for path planning,” IEEE Trans. Robot. Autom. 14 (1), 166–171 (1998).Google Scholar
35. Kuffner, J. J. and LaValle, S. M., “RRT-connect: An Efficient Approach to Single-Query Path Planning,” IEEE International Conference on Robotics and Automation, San Francisco, California, 2, 995–1001 (2000).Google Scholar
36. LaValle, S. M. and Kuffner, J. J., “Randomized kinodynamic planning,” The Int. J. Robot. Res. 20 (5), 378–400 (2001).Google Scholar
37. Hsu, D., Latombe, J. C. and Kurniawati, H., “On the probabilistic foundations of probabilistic roadmap planning,” The Int. J. Robot. Res. 25 (7), 627–643 (2006).CrossRefGoogle Scholar
38. Rantanen, M.T. and Juhola, M., “A configuration deactivation algorithm for boosting probabilistic roadmap planning of robots,” Int. J. Autom. Comp. 9 (2), 155–164 (2012).Google Scholar
39. Kothari, M. and Postlethwaite, I., “A probabilistically robust path planning algorithm for UAVs using rapidly-exploring random trees,” J. Intell. Robot. Syst. 71 (2), 231–253 (2013).CrossRefGoogle Scholar
40. LaValle, S. M. and Kuffner, J. J., “Randomized kinodynamic planning,” The Int. J. Robot. Res. 20 (5), 378–400 (2011).Google Scholar
41. Qureshi, A. H. and Ayaz, Y., “Intelligent bidirectional rapidly-exploring random trees for optimal motion planning in complex cluttered environments,” Robot. Auton. Syst. 68 (6), 1–11 (2015).Google Scholar