Abstract
Nowadays, robotic applications exist in various fields, including medical, industrial, and educational. The critical aspect of most of these applications is robot movement, where an efficient path-planning algorithm is required in order to guarantee a safe and cost-effective movement. The main goal of the path planning technique is to find the shortest possible path to the destination while avoiding the obstacles on the route. This study proposes a framework employing swarm intelligence optimization techniques based on an improved genetic algorithm and particle swarm optimization to obtain the optimum trajectory. The simulations are conducted using MATLAB R2022b. It is observed that the proposed particle swarm optimization achieves better accuracy of up to 99.5% and faster convergence time when compared with the genetic algorithm that attains 74.6% accuracy. The proposed optimized path planning algorithm is considerably advantageous, especially in realistic applications such as rescue robots and item delivery.
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
Acemoglu, D., Restrepo, P.: Robots and jobs: evidence from us labor markets. J. Polit. Econ. 128(6), 2188–2244 (2020)
Ali, S.M., Yonan, J.F., Alniemi, O., Ahmed, A.A.: Mobile robot path planning optimization based on integration of firefly algorithm and cubic polynomial equation. J. ICT Res. Appl. 16(1), 1–22 (2022). https://doi.org/10.5614/itbj.ict.res.appl.2022.16.1.1
Ansari, S., Alnajjar, K.A.: Multi-hop genetic-algorithm-optimized routing technique in diffusion-based molecular communication. IEEE Access 11, 22689–22704 (2023). https://doi.org/10.1109/ACCESS.2023.3244556
Ansari, S., Alnajjar, K.A., Saad, M., Abdallah, S., El-Moursy, A.A.: Automatic digital modulation recognition based on genetic-algorithm optimized machine learning models. IEEE Access 10, 50265–50277 (2022). https://doi.org/10.1109/ACCESS.2022.3171909
Brooks, A., et al.: Path planning for robotic delivery systems. In: SoutheastCon 2022, pp. 421–426. IEEE (2022)
Cheng, X., Li, J., Zheng, C., Zhang, J., Zhao, M.: An improved PSO-GWO algorithm with chaos and adaptive inertial weight for robot path planning. Front. Neurorobot. 15, 770361 (2021)
Chopard, B., Tomassini, M.: Particle swarm optimization. In: Chopard, B., Tomassini, M. (eds.) An Introduction to Metaheuristics for Optimization, pp. 97–102. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-319-93073-2_6
Choueiry, S., Owayjan, M., Diab, H., Achkar, R.: Mobile robot path planning using genetic algorithm in a static environment. In: 2019 Fourth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 1–6. IEEE (2019)
Gupta, S.K.: An overview of genetic algorithms: a structural analysis. Int. J. Innov. Sci. Res. Tech. 15, 58 (2021)
Li, G., et al.: Improved artificial fish swarm algorithm approach to robot path planning problems. In: 2020 5th International Conference on Automation, Control and Robotics Engineering (CACRE), pp. 71–75. IEEE (2020)
Liang, X., Kou, D., Wen, L.: An improved chicken swarm optimization algorithm and its application in robot path planning. IEEE Access 8, 49543–49550 (2020)
Lu, C.Y., Kao, C.C., Lu, Y.H., Juang, J.G.: Application of path planning and image processing for rescue robots. Sensors and Materials 34(1), 65–80 (2022)
Ma, H.: Application of an improved ant colony algorithm in robot path planning and mechanical arm management. Int. J. Mechatron. Appl. Mech. 10, 196–203 (2021)
Mahajan, B.D., Marbate, P.: Literature review on path planning in dynamic environment (2013)
Pagallo, U.: Vital, Sophia, and co.—the quest for the legal personhood of robots. Information 9(9), 230 (2018)
Rahmaniar, W., Rakhmania, A.E.: Mobile robot path planning in a trajectory with multiple obstacles using genetic algorithms. J. Rob. Control (JRC) 3(1), 1–7 (2022)
Shao, X., Wang, G., Zheng, R., Wang, B., Yang, T., Liu, S.: Path planning for mine rescue robots based on improved ant colony algorithm. In: 2022 8th International Conference on Control, Automation and Robotics (ICCAR), pp. 161–166. IEEE (2022)
Tuba, E., Dolicanin, E., Tuba, M.: Water cycle algorithm for robot path planning. In: 2018 10th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), pp. 1–6. IEEE (2018)
Tuba, E., Strumberger, I., Zivkovic, D., Bacanin, N., Tuba, M.: Mobile robot path planning by improved brain storm optimization algorithm. In: 2018 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2018)
Wang, Z., Wu, C., Xu, J., Ling, H.: Research on path planning of cleaning robot based on an improved ant colony algorithm. In: MATEC Web of Conferences, vol. 336, p. 07005. EDP Sciences (2021)
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 Singapore Pte Ltd.
About this paper
Cite this paper
Kaissar, A. et al. (2023). Robot Path Planning Using Swarm Intelligence Algorithms. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14086. Springer, Singapore. https://doi.org/10.1007/978-981-99-4755-3_12
Download citation
DOI: https://doi.org/10.1007/978-981-99-4755-3_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-4754-6
Online ISBN: 978-981-99-4755-3
eBook Packages: Computer ScienceComputer Science (R0)