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A Novel Collision-Free Path Planning Modeling and Simulation Methodology for Robotical Arms Using Resistive Grids

Published online by Cambridge University Press:  22 August 2019

Carlos Hernández-Mejía*
Affiliation:
Escuela de Ingenierías, Universidad de Xalapa, Xalapa, Mexico
Héctor Vázquez-Leal
Affiliation:
Maestría en Ingeniería Electrónica y Computación, Universidad Veracruzana, Xalapa, Mexico E-mails: hvazquez@uv.mx, deletsm@gmail.com
Delia Torres-Muñoz
Affiliation:
Maestría en Ingeniería Electrónica y Computación, Universidad Veracruzana, Xalapa, Mexico E-mails: hvazquez@uv.mx, deletsm@gmail.com
*
*Corresponding author. E-mail: cmahernandez@gmail.com

Summary

Path planning represents planning collision-free strategies to move from starting point to ending point. These strategies can be carried out for known and unknown environments. Recently, a novel and reduced CPU-time modeling and simulation methodology for path planning in known environment based on resistive grids (RGs) has been introduced. In this work, a novel modified version of Resistive Grid Path Planning Methodology (RGPPM) methodology is presented with the purpose of exploring collision-free path planning for robotic arms. This extension of the methodology allows to numerically relate positions in the RG with angular values of the robotic systems. In addition, it is possible to include obstacles in the configuration space, and therefore collision detection can be established for RGs. Finally, the variation of links for robotic arms and obstacles for configuration space is explored by simulating different scenarios.

Type
Articles
Copyright
© Cambridge University Press 2019

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References

Siciliano, B and Khatib, O, Handbook of Robotics (Springer, Berlin Heidelberg, 2008).CrossRefGoogle Scholar
Choset, H, Lynch, K. M., Hutchinson, S, Kantor, G. A., Burgard, W, Kavraki, L. E. and Thrun, S, Principles of Robot Motion: Theory, Algorithms, and Implementations (MIT Press, Cambridge, MA, 2005).Google Scholar
Wang, D, “A linear-time algorithm for computing collision-free path on reconfigurable mesh,” Parall. Comput. 34(9), 487496 (2008).CrossRefGoogle Scholar
Bahar, M. R., Ghiasi, A. R. and Bahar, H. B., “Grid roadmap based ANN corridor search for collision free, path planning,” Scientia Iranica 19(6), 18501855 (2012).CrossRefGoogle Scholar
Tarassenko, L and Blake, A, “Analogue Computation of Collision-Free Paths,” Proceedings of the IEEE International Conference on Robotics and Automation, Sacramento, CA, USA (1991) pp. 540545.Google Scholar
Stan, M and Burleson, W, “Analog VLSI for Robot Path Planning,” Proceedings of the Twenty-Sixth Asilomar Conference on Signals, Systems Computers, Pacific Grove, CA, USA (1992) pp. 915919.Google Scholar
Stan, M, Burleson, W, Connolly, C and Grupen, R, “Analog VLSI for robot path planning,” Anal. Integr. Circ. Sig. Process. 6(1), 6173 (1994).CrossRefGoogle Scholar
Althofer, K, Fraser, D. A. and Bugmann, G, “Rapid path planning for robotic manipulators using an emulated resistive grid,” Electr. Lett. 31(22), 19601961 (1995).CrossRefGoogle Scholar
Koziol, S and Hasler, P, “Reconfigurable Analog VLSI circuits for robot path planning,” Proceedings of the NASA/ESA Conference on Adaptive Hardware and Systems, San Diego, CA, USA (2011) pp. 3643.Google Scholar
Koziol, S, Hasler, P and Stilman, M, “Robot Path Planning Using Field Programmable Analog Arrays,” Proceedings of the IEEE International Conference on Robotics and Automation, Saint Paul, MN, USA (2012) pp. 17471752.Google Scholar
Marshall, G. F. and Tarassenko, L, “Robot path planning using VLSI resistive grids,” Proceedings of the Third International Conference on Artificial Neural Networks, Brighton, UK (1993) pp. 163167.Google Scholar
Marshall, G. F. and Tarassenko, L, “Robot Path Planning Using Resistive Grids,” Proceedings of the Second International Conference on Artificial Neural Networks, Bournemouth, UK (1991) pp. 149152.Google Scholar
Hassouna, M. S., Abdel-Hakim, A. E. and Farag, A. A., “PDE-Based Robust Robotic Navigation,” Proceedings of the 2nd Canadian Conference on Computer and Robot Vision, Victoria, BC, Canada (2005) pp. 176183.Google Scholar
Namgung, I, “A global collision-free path planning using parametric parabola through Geometry Mapping of obstacles in robot work space,” KSME J. 10(4), 443 (1996).CrossRefGoogle Scholar
Vazquez-Leal, H, Marin-Hernandez, A, Khan, Y, Yildirim, A, Filobello-Nino, U, Castaneda-Sheissa, R and Jimenez-Fernandez, V. M., “Exploring collision-free path planning by using homotopy continuation methods,” Appl. Math. Comput. 219(14), 75147532 (2013).Google Scholar
Karpinska, J and Tchon, K, “Continuation Method Approach to Trajectory Planning in Robotic Systems,” Proceedings of the 16th International Conference on Methods Models in Automation Robotics, Miedzyzdroje, Poland (2011) pp. 5156.Google Scholar
Ortega, L. M., Rueda, A. J. and Feito, F. R., “A solution to the path planning problem using angle preprocessing,” Robot. Auton. Syst. 58(1), 2736 (2010).CrossRefGoogle Scholar
Rashid, A. T., Ali, A. A., Frasca, M and Fortuna, L, “Path planning with obstacle avoidance based on visibility binary tree algorithm,” Robot. Auto. Syst. 61(12), 14401449 (2013).CrossRefGoogle Scholar
Nieto, J, Slawinski, E, Mut, V and Wagner, B, “Online Path Planning Based on Rapidly-Exploring Random Trees,” Proceedings of the IEEE International Conference on Industrial Technology, Vina del Mar, Chile (2010) pp. 14511456.Google Scholar
Bry, A and Roy, N, “Rapidly-Exploring Random Belief Trees for Motion Planning Under Uncertainty,” Proceedings of the IEEE International Conference on Robotics and Automation, Shanghai, China (2011) pp. 723730.Google Scholar
Kothari, M, Postlethwaite, I and Gu, D. W., “Multi-UAV Path Planning in Obstacle Rich Environments Using Rapidly-Exploring Random Trees,” Proceedings of the 48th IEEE Conference on Decision and Control, Shanghai, China (2009) pp. 30693074.Google Scholar
Aghababa, M. P., “3D path planning for underwater vehicles using five evolutionary optimization algorithms avoiding static and energetic obstacles,” Appl. Ocean Res. 38, 4862 (2012).CrossRefGoogle Scholar
Hong, Q, Ke, X and Takacs, A, “An improved genetic algorithm with co-evolutionary strategy for global path planning of multiple mobile robots,” Neurocomputing 120, 509517 (2013).Google Scholar
Lee, S and Park, J, “Neural computation for collision-free path planning,” J. Intell. Manufact. 2(5), 315326 (1991).CrossRefGoogle Scholar
Zhang, Y, Gong, D.-W. and Zhang, J.-H., “Robot path planning in uncertain environment using multi-objective particle swarm optimization,” Neurocomputing 103, 172185 (2013).CrossRefGoogle Scholar
Ho, C.-W., Ruehli, A and Brennan, P, “The modified nodal approach to network analysis,” IEEE Trans. Circ. Syst. 22(6), 504509 (1975).Google Scholar
Schwarz, A. F., Computer-Aided Design of Microelectronic Circuits and Systems: General Introduction and Analog-Circuit Aspects (Academic Press, Orlando, FL, USA, 1987).Google Scholar
Ogrodzki, J, Circuit Simulation Methods and Algorithms (Taylor & Francis, Abingdon, Oxfordshire, 1994).Google Scholar
Davis, T. A., “Algorithm 832: UMFPACK V4.3—an unsymmetric-pattern multifrontal method,” ACM Trans. Math. Softw. 30(2), 196199 (2004).CrossRefGoogle Scholar
Kanaya, M, Cheng, G. X., Watanabe, K and Tanaka, M, “Shortest Path Searching for Robot Walking Using an Analog Resistive Network,” Proceedings of the IEEE International Symposium on Circuits and Systems, London, UK (1994) pp. 311314.Google Scholar
Kavraki, L. E., Svestka, P, Latombe, J and Overmars, M. H., “Probabilistic roadmaps for path planning in high-dimensional configuration spaces,” IEEE Trans. Robot. Auto. 12(4), 566580 (1996).CrossRefGoogle Scholar
Chien, R. T., Zhang, L and Zhang, B, “Planning collision-free paths for robotic arm among obstacles,” IEEE Trans. Pattern Anal. Mach. Intell. 6(1), 9196 (1984).CrossRefGoogle ScholarPubMed
Kim, M.-C. and Song, J.-B., “Informed RRT* with improved converging rate by adopting wrapping procedure,” Intell. Serv. Robot. 11(1), 5360 (2018).CrossRefGoogle Scholar