Abstract
We present a novel approach for solving path-planning problems using a state-of-the-art data-driven deep learning technique. Although machine learning has been previously utilized for path planning, it has proven to be challenging due to the discrete nature of search algorithms. In this study, we propose a deep learning-based algorithm for path planning, which incorporates a Convolutional Recurrent Neural Network (CRNN) to create an end-to-end trainable neural network planner. This planner is then combined with the A* algorithm through an adaptive autonomy concept to autonomously select the best path planning strategy for increasing time efficiency and completeness. To train the CRNN, a labeled data set is generated autonomously from various maps by changing the starting and endpoints. The trained CRNN can find the shortest path from the starting point to the goal point by evaluating map images in one go. Additionally, the CRNN can predict way-points on image inputs. Our simulation results demonstrate that our proposed strategy is capable of finding the shortest path much faster than the A* algorithm in sparse environments, achieving a speed-up of up to 831 in some cases, which is exceptional.
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
Similar content being viewed by others
References
Guruji, A.K., Agarwal, H.,Parsediya, D.K.: Time-efficient A* algorithm for robot path planning. Procedia Technol. 23, 144–149 (2016). 3rd International Conference on Innovations in Automation and Mechatronics Engineering 2016, ICIAME 2016 05–06 February 2016. ISSN 2212-0173
Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968). Publisher: IEEE
Algfoor, Z.A., Sunar, M.S., Kolivand, H.: A comprehensive study on pathfinding techniques for robotics and video games. Int. J. Comput. Games Technol. (2015)
Kala, R., Shukla, A., Tiwari, R., Rungta, S., Janghel, R.R.: Mobile robot navigation control in moving obstacle environment using genetic algorithm, artificial neural networks and A* algorithm. In: 2009 WRI World Congress on Computer Science and Information Engineering, vol. 4, pp. 705–713, IEEE (2009)
Tamar, A., Wu, Y., Thomas, G., Levine, S.,Abbeel, P.: Value iteration networks. arXiv preprint arXiv:1602.02867 (2016)
Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. J. Mol. Biol. 147, 195–197 (1981)
Choudhury, S., et al.: Data-driven planning via imitation learning. Int. J. Robot. Res. 37(13–14), 1632–1672 (2018)
Qureshi, A.H.,Simeonov, A., Bency, M.J., Yip, M.C.: Motion planning networks. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 2118–2124. IEEE(2019)
Takahashi, T., Sun, H., Tian, D., Wang, Y.: Learning heuristic functions for mobile robot path planning using deep neural networks. In: Proceedings of the International Conference on Automated Planning and Scheduling, vol. 29, pp. 764–772 (2019)
Ichter, B., Schmerling, E., Lee, T.W.E., Faust, A.: Learned critical probabilistic roadmaps for robotic motion planning. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 9535–9541. IEEE (2020)
Kim, B., Pineau, J.: Socially adaptive path planning in human environments using inverse reinforcement learning. Int. J. Soc. Robot. 8(1), 51–66 (2016)
Kretzschmar, H., Spies, M., Sprunk, C., Burgard, W.: Socially compliant mobile robot navigation via inverse reinforcement learning. Int. J. Robot. Res. 35(11), 1289–1307 (2016)
Lee, L.,Parisotto, E., Chaplot, D.S., Xing, E., Salakhutdinov, R.: Gated path planning networks. In: International Conference on Machine Learning, pp. 2947–2955. PMLR (2018)
Pol, R.S., Murugan, M.: A review on indoor human aware autonomous mobile robot navigation through a dynamic environment survey of different path planning algorithmand methods. In: 2015 International Conference on Industrial Instrumentation and Control (ICIC), pp. 1339–1344. IEEE (2015)
Yonetani, R., Taniai, T., Barekatain, M., Nishimura, M., Kanezaki, A.: Path planning using neural A* search. In: International Conference on Machine Learning, pp. 12029–12039 (2021)
Scerri, P., Reed, N.: Designing agents for systems with adjustable autonomy. Citeseer (2001)
Tang, S.H., Yeong, C.F., Su, E.L.M.: Comparison between normal waveform and modified wavefront path planning algorithm for mobile robot. Appl. Mech. Mater. 607, 778–781. Trans. Tech. Publ. (2014)
Noreen, I., Khan, A., Habib, Z.: Optimal path planning for mobile robots using memory efficient A. In: 2016 International Conference on Frontiers of Information Technology (FIT), pp. 142–146. IEEE (2016)
Botea, A., Müller, M., Schaeffer, J.: Near optimal hierarchical path finding. J. Game Dev. 1(1), 1–30. Citeseer (2004)
Benders, S., Schopferer, S.: A line-graph path planner for performanceconstrained fixed-wing UAVs in wind fields. In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 79–86. IEEE (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Aatif, M., Adeel, U., Basiri, A., Mariani, V., Iannelli, L., Glielmo, L. (2023). Deep Learning Based Path-Planning Using CRNN and A* for Mobile Robots. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23). Lecture Notes in Networks and Systems, vol 721. Springer, Cham. https://doi.org/10.1007/978-3-031-35308-6_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-35308-6_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-35307-9
Online ISBN: 978-3-031-35308-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)