Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-14T18:33:48.377Z Has data issue: false hasContentIssue false
  • English
  • Français

Collision-Free Motion Planning for an Aligned Multiple-turret System Operating in Extreme Environment

Published online by Cambridge University Press:  16 June 2021

Ümit Yerlikaya Open the ORCID record for Ümit Yerlikaya [Opens in a new window]  and
R.Tuna Balkan
Ümit Yerlikaya*
Affiliation:
Department of Mechanical Engineering, Middle East Technical University, Ankara, Turkey
R.Tuna Balkan
Affiliation:
Department of Mechanical Engineering, Middle East Technical University, Ankara, Turkey
*
*Corresponding author. Email: yerlikaya04@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Instead of using the tedious process of manual positioning, an off-line path planning algorithm has been developed for military turrets to improve their accuracy and efficiency. In the scope of this research, an algorithm is proposed to search a path in three different types of configuration spaces which are rectangular-, circular-, and torus-shaped by providing three converging options named as fast, medium, and optimum depending on the application. With the help of the proposed algorithm, 4-dimensional (D) path planning problem was realized as 2-D + 2-D by using six sequences and their options. The results obtained were simulated and no collision was observed between any bodies in these three options.

Keywords

A* AlgorithmAutonomous TurretsHigh-dimensional C-SpaceManifold C-SpaceMotion Planning
Type
Article
Information
Robotica , Volume 40 , Issue 3 , March 2022 , pp. 690 - 719
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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

Bahrin, M., Othman, M., Azli, N. H. N. and Talib, M. F., “Industry 4.0: A review on industrial automation and robotic,” Jurnal Teknologi (Sciences and Engineering), 137–143 (2016).Google Scholar
Padula, F. and Perdereau, V., “An on-line path planner for industrial manipulators,” Int. J. Adv Robotic Syst. January (2013).CrossRefGoogle Scholar
Yoon, S., Do, H. and Kim, J., “Collaborative mission and route planning of multi-vehicle systems for autonomous search in marine environment,” Int. J. Control Autom. Syst. 18(3), 546–555 (2020).CrossRefGoogle Scholar
Reuter, M., Oberc, H., Wannöffel, M., Kreimeier, D., Klippert, J., Pawlicki, P. and Kuhlenkötter, B., “Learning factories’ trainings as an enabler of proactive workers participation regarding industrie 4.0,” Procedia Manuf. 354–360 (2017).CrossRefGoogle Scholar
Liu, Y., Yu, C., Sheng, J. and Zhang, T., “Self-collision avoidance trajectory planning and robust control of a dual-arm space robot,” Int. J. Control Autom. Syst. 16, 2896–2905 (2018).Google Scholar
Park, S. O., Lee, M. C. and Kim, J., “Trajectory planning with collision avoidance for redundant robots using jacobian and artificial potential field-based real-time inverse kinematics,” Int. J. Control Autom. Syst. 18, 2095–2107 (2020).CrossRefGoogle Scholar
The requirements for future military robots supporting mobility relevance and possible future role of robotic/unmanned systems for FINABEL land forces. European land forces interoperability center FINABEL, (2013).Google Scholar
Pan, Z., Wang, D., Deng, H. and Li, K., “A virtual spring method for the multi-robot path planning and formation control,” Int. J. Control Autom. Syst. 17, 1272–1282 (2019).CrossRefGoogle Scholar
Zhao, J., Chao, Y. and Yuan, Y., “A cooperative obstacle-avoidance approach for two-manipulator based on A* algorithm,” International Conference on Intelligent Robotics and Applications (ICIRA), 16–25, (2019).CrossRefGoogle Scholar
Freund, E. and Hoyer, H., “Real-time path finding in multi-robot systems including obstacle avoidance,” Int J Robotics 7(1), 42–70 (1988).10.1177/027836498800700104CrossRefGoogle Scholar
Fei, Y., Fuqiang, D. and Xifang, Z., “Collision-free motion planning of dual-arm reconfigurable robots,” Robot.Comput. Manuf. 20(4), 351–357 (2004).CrossRefGoogle Scholar
“Multiple cradle launcher,” Roketsan Missiles Inc., www.roketsan.com.tr/wpcontent/uploads/2013/05/IDEX-1.pdf (last accessed: April-2020).Google Scholar
“Remote controlled weapon systems,” Aselsan A.S. https://www.aselsan.com.tr/Remote_Controlled_Weapon_Systems_3441.pdf (last accessed: April-2020).Google Scholar
Pan, Z., Wang, D., Deng, H. and Li, K., “A Virtual Spring Method for the Multi-robot Path Planning and Formation Control,” Int. J. Control Autom. Syst. 17(5), 1272–1282 (2019).CrossRefGoogle Scholar
Lozano-Pérez, T. and Wesley, M. A., “An algorithm for planning collision-free paths among polyhedral obstacles,” Commun of the ACM, 22(10), 560–570 (1979).CrossRefGoogle Scholar
Lozano-Pérez, T., “Automatic planning of manipulator transfer movements,” IEEE Trans. Syst. Man Cybern. 11(10), 681–698 (1981).CrossRefGoogle Scholar
Lozano-Perez, T., “Spatial planning: A configuration space approach,” IEEE Trans. Comput. 32(2), 108–120 (1983).CrossRefGoogle Scholar
Pan, J. and Manocha, D., “Efficient configuration space construction and optimization for motion planning,” Engineering 1(1), 46–57 (2015).CrossRefGoogle Scholar
Choset, H. and Latombe, J., “Principles of robot motion: theory, algorithms, and implementations [Book Review],” IEEE Robot Autom Mag. 12 (2005).Google Scholar
Bottin, M., Boschetti, G. and Rosati, G., “A novel collision avoidance method for serial robots,” IFToMM Symposium on Mechanism Design for Robotics, vol. 66, (Springer, Cham, 2018).Google Scholar
Rodriguez-Garavito, C. H., Pati no-Forero, A. A. and Camacho-Munoz, G. A., “Collision detector for industrial robot manipulators,” In Proceedings of the 13th International Conference on Soft Computing Models in Industrial and Environmental Applications, San Sebastian, Spain, 6–8 June (2018).CrossRefGoogle Scholar
Jiménez, P., Thomas, F., and Torras, C., “3d collision detection: a survey,” Comput. Graph. 25(2), 269–285 (2001).CrossRefGoogle Scholar
Liski, T., 3-D collision checking for improving machine operator’s spatial awareness (Master Thesis), Aalto University-School of Electrical Engineering, Finland (2014).Google Scholar
Wu, W., Zhu, H., Zhuang, X., Ma, G. and Cai, Y., “A multi-shell cover algorithm for contact detection in the three-dimensional discontinuous deformation analysis,” Theor. Appl. Fract. Mech. 72(1), 136–149 (2014).CrossRefGoogle Scholar
Klein, J. and Zachmann, G., “Point cloud collision detection,” Comput. Graph. Forum 23(3), 567–576 (2004).CrossRefGoogle Scholar
Schauer, J. and Nüchter, A., “Collision detection between point clouds using an efficient k-d tree implementation,” Adv. Eng. Inform. 29(3), 440–458 (2015).CrossRefGoogle Scholar
Beksi, W. J. and Papanikolopoulos, N., “A topology -based descriptor for 3D point cloud modeling: Theory and experiments,” Image Vis Comput. 88, 84–95 (2019).CrossRefGoogle Scholar
Han, J., “An efficient approach to 3D path planning,” Inform. Sci. 478, 318–330 (2019).CrossRefGoogle Scholar
Yerlikaya, U. and Balkan, R. T., “Obtaining High-dimensional Configuration Space for Robotic Systems Operating in a Common Environment,” ICACSR 2021: International Conference on Autonomous Control Systems and Robotics, London, UK, May (2021).Google Scholar
Ting, Y., Lei, W. I, and Jar, H. C.. “A path planning algorithm for industrial robots,” Comput. Industr. Engineer. 42(2–4), 299–308 (2002).CrossRefGoogle Scholar
Jiang, Y., Han, J., Xia, L., Lu, L., Tian, X. and Liu, H., “A decoupled five-axis local smoothing interpolation method to achieve continuous acceleration of tool axis,” Int. J. Adv. Manuf. Technol. 111(1), 449–470 (2020).10.1007/s00170-020-05936-0CrossRefGoogle Scholar
Bottin, M. and Rosati, G., “Trajectory Optimization of a Redundant Serial Robot Using Cartesian via Points and Kinematic Decoupling,” Robotics, 8(4), 101 (2019).CrossRefGoogle Scholar
Kang, S., Wu, H., Yao, L. and Li, D., “Coordinated workspace analysis and trajectory planning of redundant dual-arm robot,” 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pp. 178–183 (2016).Google Scholar
Cui, X. and Shi, H., “A*-based pathfinding in modern computer games,” Int. J. Comput. Sci. Netw. Secur. 11, 125–130 (2011).Google Scholar
Kim, K. H., Sin, S. and Lee, W., “Exploring 3D shortest distance using A* algorithm in unity3d,” TechArt: J Arts Imaging Sci. 2(3), 8185 (2015).CrossRefGoogle Scholar
Yuan, C., Liu, G., Zhang, W. and Pan, X., “An efficient rrt cache method in dynamic environments for path planning,” Robot. Auton. Sys. 131, 103595 (2020).CrossRefGoogle Scholar
Kavraki, L., Svestka, P., Latombe, J. C. and Overmars, M., “Probabilistic roadmaps for path planning in high-dimensional configuration spaces,” IEEE Trans. Robot. Autom. 12(4), 566–580 (1996).CrossRefGoogle Scholar
Pamosoaji, A. K., M. Piao and K.-S Hong, “PSO-based minimum-time motion planning for multiple vehicles under acceleration and velocity limitations,” Int. J. Control Autom. Syst. 17, 2610–2623 (2019).CrossRefGoogle Scholar
Bouraine, S. and Azouaoui, O., “Safe Motion Planning Based on a New Encoding Technique for Tree Expansion Using Particle Swarm Optimization,” Robotica 39(5), 885–927 (2021).CrossRefGoogle Scholar
Shentu, S., Xie, F., Liu, X. and Gong, Z., “Motion Control and Trajectory Planning for Obstacle Avoidance of the Mobile Parallel Robot Driven by Three Tracked Vehicles,” Robotica 39(6), 1037–1050 (2021).CrossRefGoogle Scholar
Yu, J., Ji, J., Miao, Z. and Zhou, J., “Fully Distributed Region-Reaching Control with Collision Avoidance for Multi-robot Systems,” Robotica, 1–12 (2021).CrossRefGoogle Scholar
Shahabi, M., Ghariblu, H., Beschi, M. and Pedrocchi, N., “Path Planning Methodology for Multi-Layer Welding of Intersecting Pipes Considering Collision Avoidance,” Robotica 39(6), 945–958 (2021).CrossRefGoogle Scholar
Gómez-Bravo, F., Carbone, G. and Fortes, J. C., “Collision free trajectory planning for hybrid manipulators,” Mechatronics 22(6), 836–851 (2012).CrossRefGoogle Scholar
Ramaiah, M., Mukerjee, A., Chakraborty, A. and Sharma, S., “Visual generalized coordinates,” Math. Comput. Sci., arXiv:1509.05636 September (2015).Google Scholar
Park, F. C. and Lynch, K. M., Introduction to robotics: mechanics, planning, and control (Cambridge University Press, United Kingdom, 2017).Google Scholar
Raheem, F. A. and Hussain, A. A., “Applying A* path planning algorithm based on modified c-space analysis,” Al-Khwarizmi Eng. J. 13(4), 124–136 (2017).Google Scholar
Jaillet, L. and Porta, J. M., “Efficient asymptotically-optimal path planning on manifolds,” Robot. Auton. Syst. 61(8), 797–807 (2013).CrossRefGoogle Scholar
Henrich, D., Wurll, C. and Worn, H., “Online path planning with optimal c-space discretization,” Proceedings of the 1998 IEEE/RSJ International Conference on Robots and System (Victoria, BC, Canada, pp. 1479–84, 1998).Google Scholar