Open Access Open Access  Restricted Access Subscription or Fee Access

Q-Free Walk Ant Hybrid Architecture for Mobile Robot Path Planning in Dynamic Environment

(*) Corresponding author

Authors' affiliations



This work provides a novel hybrid system for the trajectory planning of an autonomous mobile robot named Q-Free Walk Ant Hybrid Architecture (Q-FWAHA). The proposed approach combines two layers: a deliberative layer (global planning) and a reactive one (local planning), it is adapted to partially unknown environments (dynamic). It includes both reactive planning methods based on the Perception & Action principle (Sense & Act) and deliberative methods based on the Perception-Planning & Action principle (Sense-Plan & Act). The deliberative layer consists of a global planning algorithm and a model (a map) based on current knowledge of the environment. It is based on a method inspired by nature called Ant Colony Optimization (ACO) for optimal trajectory planning in known environments. The next layer is the reactive layer, it allows the robot to avoid collisions with dynamic obstacles by directly using sensory data. The reactive planning method used is a reinforcement learning approach for planning in dynamic environments; The Q-learning method. The deliberative layer is compared alone with an existing system for path planning, results show that the final path produced by our system is more efficient in terms of safety and energy saving. Simulations and experimental results to validate the proposed hybrid system are presented.
Copyright © 2022 Praise Worthy Prize - All rights reserved.


Ant Colony Optimization; Dynamic Environment; Mobile Robot; Q-Learning; Reinforcement Learning

Full Text:



K. Cai, C. Wang, J. Cheng, C. W. De Silva, and M. Q.-H. Meng, Mobile Robot Path Planning in Dynamic Environments: A Survey, arXiv Prepr. arXiv2006.14195, 2020.

H. Zhang, W. Lin, and A. Chen, Path planning for the mobile robot: A review, Symmetry (Basel)., vol. 10, no. 10, p. 450, 2018.

Oultiligh, A., Ayad, H., El Kari, A., Mjahed, M., El Gmili, N., A Hybrid PSO-GWO Algorithm for Robot Path Planning in Uncertain Environments, (2021) International Review of Automatic Control (IREACO), 14 (6), pp. 360-372.

A. R. Hubert, L. Giovanni, A. Ramirez-serrano, and H. Liu, Mobile robot localization in quasi-dynamic environments, 2008.

Y. Li, Z. Huang, and Y. Xie, Path planning of mobile robot based on improved genetic algorithm, in 2020 3rd International Conference on Electron Device and Mechanical Engineering (ICEDME), 2020, pp. 691-695.

K. H. Sedighi, K. Ashenayi, T. W. Manikas, R. L. Wainwright and Heng-Ming Tai, Autonomous local path planning for a mobile robot using a genetic algorithm, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753), 2004, pp. 1338-1345 Vol.2.

B. K. Patle, G. Babu L, A. Pandey, D. R. K. Parhi, and A. Jagadeesh, A review: On path planning strategies for navigation of mobile robot, Def. Technol., vol. 15, no. 4, pp. 582-606, 2019.

J.-H. Jung and D.-H. Kim, Local path planning of a mobile robot using a novel grid-based potential method, Int. J. Fuzzy Log. Intell. Syst., vol. 20, no. 1, pp. 26-34, 2020.

D. Siegwart, Roland and Nourbakhsh, Illah Reza and Scaramuzza, Introduction to autonomous mobile robots, vol. 49, no. 03. MIT press, 2011.

A. Hoover and B. D. Olsen, Sensor network perception for mobile robotics, in Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065), 2000, vol. 1, pp. 342-347.

A. A. Panchpor, S. Shue, and J. M. Conrad, A survey of methods for mobile robot localization and mapping in dynamic indoor environments, in 2018 Conference on Signal Processing And Communication Engineering Systems (SPACES), 2018, pp. 138-144.

A. M. Varghese and V. R. Jisha, Motion planning and control of an autonomous mobile robot, in 2018 International CET Conference on Control, Communication, and Computing (IC4), 2018, pp. 17-21.

M. G. Mohanan and A. Salgoankar, A survey of robotic motion planning in dynamic environments, Rob. Auton. Syst., vol. 100, pp. 171-185, 2018.

D. Nakhaeinia, S. H. Tang, S. B. Mohd Noor, and O. Motlagh, A review of control architectures for autonomous navigation of mobile robots, Int. J. Phys. Sci., vol. 6, no. 2, pp. 169-174, 2011.

M. Dorigo, V. Maniezzo, A. Colorni, Ant system: Optimization by a colony of cooperating agents, IEEE Trans. Syst. Man, Cybern. Part B Cybern., vol. 26, no. 1, pp. 29-41, 1996.

M. Dorigo and G. Di Caro, Ant colony optimization: A new meta-heuristic, Proc. 1999 Congr. Evol. Comput. CEC 1999, vol. 2, pp. 1470-1477, 1999.

M. Dorigo, C. Blum, Ant colony optimization theory: A survey, Theor. Comput. Sci., vol. 344, no. 2-3, pp. 243-278, 2005.

M. Dorigo, M. Birattari, and T. Stutzle, Ant colony optimization, IEEE Comput. Intell. Mag., vol. 1, no. 4, pp. 28-39, 2006.

S. Mirjalili, J. S. Dong, and A. Lewis, Ant colony optimizer: theory, literature review, and application in AUV path planning, Nature-Inspired Optim., pp. 7-21, 2020.

Ming-Ru Zeng, Lu Xi & Ai-Min Xiao, (2016) The free step length ant colony algorithm in mobile robot path planning, Advanced Robotics, 30:23, 1509-1514.

C. Buerkle, F. Oboril, J. Jarquin, and K.-U. Scholl, Efficient dynamic occupancy grid mapping using non-uniform cell representation, in 2020 IEEE Intelligent Vehicles Symposium (IV), 2020, pp. 1629-1634.

M. Dorigo and T. Stützle, Ant colony optimization: overview and recent advances, Handb. metaheuristics, pp. 311-351, 2019.

C. J. C. H. Watkins and P. Dayan, Q-learning, Mach. Learn., vol. 8, no. 3-4, pp. 279-292, 1992.

C. C. White, A survey of solution techniques for the partially observed Markov decision process, Ann. Oper. Res., vol. 32, no. 1, pp. 215-230, 1991.

B. R. Kiran et al., Deep reinforcement learning for autonomous driving: A survey, IEEE Trans. Intell. Transp. Syst., 2021.

E. Nelson, M. Corah, and N. Michael, Environment model adaptation for mobile robot exploration, Auton. Robots, vol. 42, no. 2, pp. 257-272, 2018.

W. Y. Chien, Stereo-Camera Occupancy Grid Mapping, 2020.

H. Arndt, M. Bundschus, and A. Naegele, Towards a next-generation matrix library for java, in Proceedings - International Computer Software and Applications Conference, 2009, vol. 1, pp. 460-467.

C. Lamini, S. Benhlima, and A. Elbekri, Genetic algorithm based approach for autonomous mobile robot path planning, Procedia Comput. Sci., vol. 127, pp. 180-189, 2018.

Gunantara, N., Nurweda Putra, I., Antara, I., The Characteristics of Multi-Criteria Weight on Ad-Hoc Network with Ant Colony Optimization, (2020) International Journal on Communications Antenna and Propagation (IRECAP), 10 (4), pp. 249-256.

Abidi, M., Fizazi, H., Boudali, N., Clustering of Remote Sensing Data Based on Spherical Evolution Algorithm, (2021) International Review of Aerospace Engineering (IREASE), 14 (2), pp. 72-79.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2023 Praise Worthy Prize