Multi-Objective Evolutionary Artificial Potential Field for Indoor Path Planning

Authors

  • Ahmed A. Abdelaal Computer and Systems Engineering Department, Faculty of Engineering, Zagazig University, Zagazig, 44511, Egypt
  • Nesreen I. Ziedan Computer and Systems Engineering Department, Faculty of Engineering, Zagazig University, Zagazig, 44511, Egypt
  • Tamer S. Gaafar Computer and Systems Engineering Department, Faculty of Engineering, Zagazig University, Zagazig, 44511, Egypt

Keywords:

Genetic Algorithms, Optimization, Artificial Potential Field, Path Planning, Mobile Robots

Abstract

Path planning is crucial for robotics, enabling robots to find collision-free routes from their current positions to target positions. The Artificial Potential Field (APF) approach utilizes attractive and repulsive fields to guide robots towards targets while avoiding obstacles. However, the conventional APF's repulsive potential equation can yield suboptimal results due to local minima. To address this, a novel method called Multi-Objective Evolutionary Artificial Potential Field (MOE-APF) is introduced. MOE-APF modifies the repulsive potential equation and employs the membrane computing and Genetic Algorithm (GA) to optimize a new set of APF parameters. The fitness function considers multiple objectives: path length, smoothness, success rate, and safety. A comparison with a recent method called Membrane Evolutionary Artificial Potential Field (memEAPF) shows that MOE-APF significantly enhances path quality, optimization time, and success rate across various environments. MOE-APF's versatility allows it to tackle path planning challenges involving non-holonomic robots, multiple robots, industrial manipulators, and dynamic obstacles.

Downloads

Download data is not yet available.

References

Rubio, F., F. Valero, and C. Llopis-Albert, A review of mobile robots: Concepts, methods, theoretical framework, and applications. International Journal of Advanced Robotic Systems, 2019. 16(2).

Niloy, M.A.K., et al., Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review. IEEE Access, 2021. 9: p. 35338-35370.

Tsagaris, A., P. Kyratsis, and G. Mansour, The Integration of Genetic and Ant Colony Algorithm in a Hybrid Approach. International Journal of Intelligent Systems and Applications in Engineering, 2023. 11(2): p. 336 – 342.

Ajani, S.N., et al., Dynamic RRT* Algorithm for Probabilistic Path Prediction in Dynamic Environment. International Journal of Intelligent Systems and Applications in Engineering, 2023. 11(7s): p. 263 - 271.

Tan, C.S., R. Mohd-Mokhtar, and M.R. Arshad, A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms. IEEE Access, 2021. 9: p. 119310-119342.

Dong, L., et al., A review of mobile robot motion planning methods: from classical motion planning workflows to reinforcement learning-based architectures. Journal of Systems Engineering and Electronics, 2023. 34(2): p. 439-459.

Wang, J., et al., Neural RRT*: Learning-Based Optimal Path Planning. IEEE Transactions on Automation Science and Engineering, 2020. 17(4): p. 1748-1758.

Sasagawa, A., S. Sakaino, and T. Tsuji, Motion Generation Using Bilateral Control-Based Imitation Learning With Autoregressive Learning. IEEE Access, 2021. 9: p. 20508-20520.

Ataollahi, M. and M. Farrokhi, Online path planning of cooperative mobile robots in unknown environments using improved Q‐Learning and adaptive artificial potential field. The Journal of Engineering, 2023. 2023(2).

Arambula Cosío, F. and M.A. Padilla Castañeda, Autonomous robot navigation using adaptive potential fields. Mathematical and Computer Modelling, 2004. 40(9-10): p. 1141-1156.

Wahab, M.N.A., S. Nefti-Meziani, and A. Atyabi, A comparative review on mobile robot path planning: Classical or meta-heuristic methods? Annual Reviews in Control, 2020. 50: p. 233-252.

Cao, M., B. Li, and M. Shi, The Dynamic Path Planning of Indoor Robot Fusing B-Spline and Improved Anytime Repairing A* Algorithm. IEEE Access, 2023. 11: p. 92416-92423.

Zhang, W., Y. Xu, and J. Xie, Path Planning of USV Based on Improved Hybrid Genetic Algorithm, in 2019 European Navigation Conference (ENC). 2019. p. 1-7.

Montiel, O., R. Sepúlveda, and U. Orozco-Rosas, Optimal Path Planning Generation for Mobile Robots using Parallel Evolutionary Artificial Potential Field. Journal of Intelligent & Robotic Systems, 2014. 79(2): p. 237-257.

Koren, Y. and J. Borenstein, Potential field methods and their inherent limitations for mobile robot navigation, in Proceedings of the 1991 IEEE International Conference on Robotics and Automation. 1991. p. 7.

Mohamed, A., N. Ziedan, and T. Gaafar, Artificial Potential Field Approaches for Indoor Mobile Robot Path Planning: A Review. The Egyptian International Journal of Engineering Sciences and Technology, 2023. 0(0): p. 0-0.

Khatib, O., Real-time obstacle avoidance for manipulators and mobile robots, in Proceedings. 1985 IEEE International Conference on Robotics and Automation. 1985. p. 500-505.

Ge, S.S. and Y.J. Cui, New potential functions for mobile robot path planning. IEEE Transactions on Robotics and Automation, 2000. 16(5): p. 615-620.

Raja, R., A. Dutta, and K.S. Venkatesh, New potential field method for rough terrain path planning using genetic algorithm for a 6-wheel rover. Robotics and Autonomous Systems, 2015. 72: p. 295-306.

Zhou, Z., et al., Tangent navigated robot path planning strategy using particle swarm optimized artificial potential field. Optik, 2018. 158: p. 639-651.

Zhang, T., J. Xu, and B. Wu, Hybrid Path Planning Model for Multiple Robots Considering Obstacle Avoidance. IEEE Access, 2022. 10: p. 71914-71935.

Chen, G. and J. Liu, Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential Field Method. Comput Intell Neurosci, 2019. 2019: p. 1932812.

Orozco-Rosas, U., O. Montiel, and R. Sepúlveda, Mobile robot path planning using membrane evolutionary artificial potential field. Applied Soft Computing, 2019. 77: p. 236-251.

Sfeir, J., M. Saad, and H. Saliah-Hassane, An improved Artificial Potential Field approach to real-time mobile robot path planning in an unknown environment, in 2011 IEEE International Symposium on Robotic and Sensors Environments (ROSE). 2011. p. 208-213.

Li, H., Robotic Path Planning Strategy Based on Improved Artificial Potential Field, in 2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE). 2020. p. 67-71.

Goricanec, J., et al., Collision-Free Trajectory Following With Augmented Artificial Potential Field Using UAVs. IEEE Access, 2023. 11: p. 83492-83506.

Zhang, T., Y. Zhu, and J. Song, Real‐time motion planning for mobile robots by means of artificial potential field method in unknown environment. Industrial Robot: An International Journal, 2010. 37(4): p. 384-400.

Jayaweera, H.M. and S. Hanoun, A Dynamic Artificial Potential Field (D-APF) UAV Path Planning Technique for Following Ground Moving Targets. IEEE Access, 2020. 8: p. 192760-192776.

Yao, Q., et al., Path Planning Method With Improved Artificial Potential Field—A Reinforcement Learning Perspective. IEEE Access, 2020. 8: p. 135513-135523.

Chen, Y., et al., Path Planning and Obstacle Avoiding of the USV Based on Improved ACO-APF Hybrid Algorithm With Adaptive Early-Warning. IEEE Access, 2021. 9: p. 40728-40742.

Luo, J., Z.-X. Wang, and K.-L. Pan, Reliable Path Planning Algorithm Based on Improved Artificial Potential Field Method. IEEE Access, 2022. 10: p. 108276-108284.

Szczepanski, R., T. Tarczewski, and K. Erwinski, Energy Efficient Local Path Planning Algorithm Based on Predictive Artificial Potential Field. IEEE Access, 2022. 10: p. 39729-39742.

Mitchell, M., An Introduction to Genetic Algorithms. 1998.

Nazarahari, M., E. Khanmirza, and S. Doostie, Multi-objective multi-robot path planning in continuous environment using an enhanced genetic algorithm. Expert Systems with Applications, 2019. 115: p. 106-120.

Downloads

Published

02.02.2024

How to Cite

Abdelaal, A. A. ., Ziedan, N. I. ., & Gaafar, T. S. . (2024). Multi-Objective Evolutionary Artificial Potential Field for Indoor Path Planning. International Journal of Intelligent Systems and Applications in Engineering, 12(14s), 715–731. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4753

Issue

Vol. 12 No. 14s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.