Abstract
In this article, we present the application of Graph Theory in the development of an algorithm of path planning for mobile robots. The proposed system evaluates a RRT algorithm based on the individual cost of nodes and the optimized reconnection of the final path based on Dijkstra and Floyd criteria. Our proposal includes the comparisons between different RRT* algorithms and the simulation of the environments in different platforms. The results identify that these criteria must be considered in all the variations of RRT to achieve a definitive algorithm in mobile robotics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Valle, S.M.L.: Rapidly exploring random trees: a new tool for path planning. Iowa State University, p. 4 (1998)
Islam, F.: RRT*-smart: rapid convergence implementation of RRT* towards optimal solution. In: Proceedings of IEEE International Conference on Mechatronics and Automation (ICMA), pp. 1651–1656 (2012)
Alterovitz, R., Patil, S., Derbakova, A.: Rapidly-exploring roadmaps: weighing exploration vs. refinement in optimal motion planning. In: Robotics and Automation (ICRA), pp. 3706–3712 (2011)
Rios-Martinez, J., Spalanzani, A., Laugier, C.: Probabilistic autonomous navigation using Risk-RRT approach and models of human interaction. In: CONACYT (2012)
Salihoglu, S., Widom, J.: Optimizing graph algorithms on pregel-like systems. In: Proceedings of the VLDB Endowment, vol. 7 (2014)
Bast, H., et al.: Route planning in transportation networks. In: Kliemann, L., Sanders, P. (eds.) Algorithm Engineering. LNCS, vol. 9220, pp. 19–80. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49487-6_2
Lopez, D., Lozano, A.: Transport network models for routing algorithms. In: Proceedings of 2016 IEEE 13th International Conference on Networking, Sensing, and Control (2016)
Pfetsch, M.E., Borndörfer, R.: Routing in line planning for public transport. Konrad-Zuse-Zentrum f¨ur Informationstechnik Berlin (2004)
Pareekutty, N., James, F., Ravindran, B.: RRT-HX: RRT with heuristic extend operations for motion planning in robotic systems. In: Proceedings of the ASME 2016 International Design Engineering Technical Conferences (2016)
Penev, I., Karova, M.: Path planning algorithm for mobile robot. In: Conference: 15th International Conference on Applied Computer Science (2015)
Ducho, F., Babinec, A., Kajan, M.: Path planning with modified a star algorithm for a mobile robot. In: Modelling of Mechanical and Mechatronic Systems, pp. 59–69 (2014)
Purcaru, C., Precup, R.E., Iercan, D., Fedorovici, L.O., David, R.C.: Hybrid PSO-GSA robot path planning algorithm in static environments with danger zones. In: System Theory, Control and Computing (ICSTCC) (2013)
Liu, H., Stoll, N., Junginger, S., Thurow, K.: A Floyd-Dijkstra hybrid application for mobile robot path planning in life science automation. In: 8th IEEE International Conference on Automation Science and Engineering (2012)
Weiren, S., Kai, W.: Floyd algorithm for the shortest path planning of mobile robot. Chin. J. Sci. Instrum. 10, 2088–2092 (2009)
Tomoiagă, T., Predoi, C., Coşereanu, L.: Indoor mapping using low cost LIDAR based systems. In: Applied Mechanics and Materials (2016)
Kim, J.: Motion planning of aerial robot using rapidly-exploring random trees with dynamic constraints. In: International Conference on Robotics & Automation (2003)
Yang, K., et al.: Spline-based RRT path planner for non-holonomic robots. J. Intell. Robot. Syst. 73, 763–782 (2013)
Kuffner, J.J., LaValle, S.M.: RRT-connect: an efficient approach to single-query path planning. In: International Conference on Robotics & Automation (2000)
Naderi, K., Rajamaki, J., Hamalainen, P.: RT-RRT*: a real-time path planning algorithm based on RRT*. In: MIG (2015)
Ge, J., Sun, F., Liu, C.: RRT-GD: an efficient rapidly-exploring random tree approach with goal directionality for redundant manipulator path planning. In: International Conference on Robotics and Biomimetics (2016)
Kang, R., Liu, H., Wang, Z.: Fast convergence RRT for asymptotically-optimal motion planning. In: International Conference on Robotics and Biomimetics (2016)
Zhao, Y.J., et al.: 3D motion planning for robot-assisted active flexible needle based on rapidly-exploring random trees. J. Autom. Control Eng. 3(5) (2015)
Huh, J., Lee, D.D.: Learning high-dimensional mixture models for fast collision detection in rapidly-exploring random trees. In: IEEE International Conference on Robotics and Automation (ICRA) (2016)
Lite, D.: Learning high-dimensional mixture models for collision deteccion. In: International Conference on Robotics and Biometrics (2016)
Kim, H., Kim, D., Shin, J.U., Kim, H., Myung, H.: Angular rate-constrained path planning algorithm for unmanned surface vehicles. Ocean Eng. 84, 37–44 (2014)
Gautam, S.A., Verma, N.: Path planning for unmanned aerial vehicle based on genetic algorithm & artificial neural network in 3D. In: Data Mining and Intelligent Computing (ICDMIC) (2014)
Bouraine, S., Fraichard, T., Azouaoui, O.: Real-time safe path planning for robot navigation in unknown dynamic environments. In: Computing Systems and Applications (2016)
Xu, J., Duindam, V., Alterovitz, R., Goldberg, K.: Motion planning for steerable needles in 3D environments with obstacles using rapidly-exploring random trees and backchaining. In: Conference on Automation Science and Engineering, vol. 4 (2008)
Nguyen, T.H., Kim, D.H., Lee, C.H., Kim, H.K., Kim, S.B.: Mobile robot localization and path planning in a picking robot system using kinect camera in partially known environment. In: International Conference on Advanced Engineering Theory and Applications (2016)
Hebecker, T., Buchholz, R., Ortmeier, F.: Model-based local path planning for UAVs. J. Intell. Robot. Syst. 78(1), 127–142 (2015)
Paranjape, A.A., Meier, K.C., Shi, X., Chung, S.J., Hutchinson, S.: Motion primitives and 3D path planning for fast flight through a forest. Int. J. Robot. Res. 34(3), 357–377 (2015)
Farinella, J., Lay, C., Bhandari, S.: UAV collision avoidance using a predictive rapidly-exploring random Tree (RRT). In: American Institute of Aeronautics and Astronautics (2016)
Duchoň, F., et al.: Path planning with modified a star algorithm for a mobile robot. Procedia Eng. 96, 59–69 (2014)
Aguilar, W.G., Morales, S.G.: 3D environment mapping using the kinect V2 and path planning based on RRT algorithms. Electronics 5, 70 (2016)
Karaman, S., Frazzoli, E.: Incremental sampling-based algorithms for optimal motion planning. Robot. Sci. Syst. 104, 2 (2010)
Wei, D.: An optimized Floyd algorithm for the shortest path problem. J. Netw. 5, 1496 (2010)
Djojo, M.A., Karyono, K.: Computational load analysis of Dijkstra, A*, and Floyd-Warshall algorithms in mesh network. In: ROBIONETICS (2013)
Adiyatov, O., Varol, H.A.: Rapidly-exploring random tree based memory efficient motion planning. In: Mechatronics and Automation (ICMA), pp. 354–359 (2013)
Acknowledgement
This work is part of the project MultiNavCar, 2016-PIC-025, from the Universidad de las Fuerzas Armadas ESPE, directed by Dr. Wilbert G. Aguilar.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Aguilar, W.G. et al. (2018). Graph Based RRT Optimization for Autonomous Mobile Robots. In: Peng, Y., Yu, K., Lu, J., Jiang, X. (eds) Intelligence Science and Big Data Engineering. IScIDE 2018. Lecture Notes in Computer Science(), vol 11266. Springer, Cham. https://doi.org/10.1007/978-3-030-02698-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-02698-1_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02697-4
Online ISBN: 978-3-030-02698-1
eBook Packages: Computer ScienceComputer Science (R0)