A Systematic Literature Review of Path-Planning Strategies for Robot Navigation in Unknown Environment
Keywords:
Mobile Robot, Path Planning, Perception, Motion Control, Heuristic Methods, Classical MethodsAbstract
The Many industries, including ports, space, surveillance, military, medicine and agriculture have benefited greatly from mobile robot technology. An autonomous mobile robot navigates in situations that are both static and dynamic. As a result, robotics experts have proposed a range of strategies. Perception, localization, path planning, and motion control are all required for mobile robot navigation. However, Path planning is a critical component of a quick and secure navigation. Over the previous few decades, many path-planning algorithms have been developed. Despite the fact that the majority of mobile robot applications take place in static environments, there is a scarcity of algorithms capable of guiding robots in dynamic contexts. This review compares qualitatively mobile robot path-planning systems capable of navigating robots in static and dynamic situations. Artificial potential fields, fuzzy logic, genetic algorithms, neural networks, particle swarm optimization, artificial bee colonies, bacterial foraging optimization, and ant-colony are all discussed in the paper. Each method's application domain, navigation technique and validation context are discussed and commonly utilized cutting-edge methods are analyzed. This research will help researchers choose appropriate path-planning approaches for various applications including robotic cranes at the sea ports as well as discover gaps for optimization.
References
J. Szrek, J. Jakubiak, and R. Zimroz, “A Mobile Robot-Based System for Automatic Inspection of Belt Conveyors in Mining Industry,” Energies, vol. 15, no. 1, pp. 1–17, 2022, doi: 10.3390/en15010327.
M. Abernot et al., “Oscillatory Neural Networks for Obstacle Avoidance on Mobile Surveillance Robot E4,” Proc. Int. Jt. Conf. Neural Networks, vol. 2022-July, 2022, doi: 10.1109/IJCNN55064.2022.9891923.
J. Zhang, J. Zhang, Q. Zhang, and X. Wei, “Obstacle Avoidance Path Planning of Space Robot Based on Improved Particle Swarm Optimization,” Symmetry (Basel)., vol. 14, no. 5, pp. 1–12, 2022, doi: 10.3390/sym14050938.
Y. Li, S. Member, J. He, S. Member, and C. Chen, “Robots With Obstacle-Avoidance,” pp. 1–20, 2022.
C. L. Lundberg, H. E. Sevil, D. Behan, and D. O. Popa, “Robotic Nursing Assistant Applications and Human Subject Tests through Patient Sitter and Patient Walker Tasks†,” Robotics, vol. 11, no. 3, pp. 1–22, 2022, doi: 10.3390/robotics11030063.
J. Yang, J. Ni, Y. Li, J. Wen, and D. Chen, “The Intelligent Path Planning System of Agricultural Robot via Reinforcement Learning,” Sensors, vol. 22, no. 12, pp. 1–19, 2022, doi: 10.3390/s22124316.
H. S. Hewawasam, M. Y. Ibrahim, and G. K. Appuhamillage, “Past, Present and Future of Path-Planning Algorithms for Mobile Robot Navigation in Dynamic Environments,” IEEE Open J. Ind. Electron. Soc., vol. 3, no. March, pp. 353–365, 2022, doi: 10.1109/OJIES.2022.3179617.
S. Wang, “Mobile Robot Path Planning based on Fuzzy Logic Algorithm in Dynamic Environment,” Proc. - 2022 Int. Conf. Artif. Intell. Everything, AIE 2022, pp. 106–110, 2022, doi: 10.1109/AIE57029.2022.00027.
A. Singh, V. Kalaichelvi, and R. Karthikeyan, “A survey on vision guided robotic systems with intelligent control strategies for autonomous tasks,” Cogent Eng., vol. 9, no. 1, 2022, doi: 10.1080/23311916.2022.2050020.
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, doi: 10.1016/j.dt.2019.04.011.
R. Kot, “Review of Collision Avoidance and Path Planning Algorithms Used in Autonomous Underwater Vehicles,” Electron., vol. 11, no. 15, 2022, doi: 10.3390/electronics11152301.
A. N. A. Rafai, N. Adzhar, and N. I. Jaini, “A Review on Path Planning and Obstacle Avoidance Algorithms for Autonomous Mobile Robots,” J. Robot., vol. 2022, 2022, doi: 10.1155/2022/2538220.
P. K. Mohanty, A. K. Singh, A. Kumar, M. K. Mahto, and S. Kundu, “Path Planning Techniques for Mobile Robots: A Review,” Lect. Notes Networks Syst., vol. 417 LNNS, pp. 657–667, 2022, doi: 10.1007/978-3-030-96302-6_62.
A. Loganathan and N. S. Ahmad, “A systematic review on recent advances in autonomous mobile robot navigation,” Eng. Sci. Technol. an Int. J., vol. 40, p. 101343, 2023, doi: 10.1016/j.jestch.2023.101343.
Barbara Kitchenham, “Procedures for Performing Systematic Reviews,” Keele Univ. Tech. Rep., vol. 33, no. 2004, pp. 1–26, 2014.
B. Kitchenham, O. Pearl Brereton, D. Budgen, M. Turner, J. Bailey, and S. Linkman, “Systematic literature reviews in software engineering - A systematic literature review,” Inf. Softw. Technol., vol. 51, no. 1, pp. 7–15, 2009, doi: 10.1016/j.infsof.2008.09.009.
P. V. Torres-Carrión, S. Aciar, C. S. González-González, and G. Rodríguez-Morales, “applied to Engineering and Education,” 2018 IEEE Glob. Eng. Educ. Conf., pp. 1364–1373, 2018.
B. Ramakrishna and K. V. Subbaiah, “INTELLIGENT SYSTEMS AND APPLICATIONS IN PSO Based Path Planning Strategy in Manufacturing Plants with Unknown Environmental Criteria,” vol. 11, no. 2, pp. 267–271, 2023.
J. Cai, F. Yan, Y. Shi, M. Zhang, and L. Guo, “Autonomous robot navigation based on a hierarchical cognitive model,” Robotica, vol. 41, no. 2, pp. 690–712, 2023, doi: 10.1017/s0263574722001539.
N. Herojit, A. Laishram, and K. Thongam, “ScienceDirect Optimal Path Planning for Mobile Robot Navigation Using FA-TPM Optimal Path Planning for Mobile Robot Navigation Using FA-TPM in Cluttered Dynamic Environments in Cluttered Dynamic Environments,” Procedia Comput. Sci., vol. 218, pp. 612–620, 2023, doi: 10.1016/j.procs.2023.01.043.
M. Quinones-Ramirez, J. Rios-Martinez, and V. Uc-Cetina, “Robot path planning using deep reinforcement learning,” no. Dl, pp. 1–42, 2023, [Online]. Available: http://arxiv.org/abs/2302.09120
I. Reguii, I. Hassani, and C. Rekik, “Neuro-fuzzy Control of a Mobile Robot,” no. December, pp. 45–50, 2023, doi: 10.1109/sta56120.2022.10018999.
F. Kamil and M. Y. Moghrabiah, “Multilayer Decision-Based Fuzzy Logic Model to Navigate Mobile Robot in Unknown Dynamic Environments,” Fuzzy Inf. Eng., vol. 14, no. 1, pp. 51–73, 2022, doi: 10.1080/16168658.2021.2019432.
Z. Yang, J. Li, L. Yang, Q. Wang, P. Li, and G. Xia, Path planning and collision avoidance methods for distributed multirobot systems in complex dynamic environments, vol. 20, no. 1. 2023. doi: 10.3934/mbe.2023008.
L. Han, X. Tan, Q. Wu, and X. Deng, “An Improved Algorithm for Complete Coverage Path Planning Based on Biologically Inspired Neural Network,” IEEE Trans. Cogn. Dev. Syst., pp. 1–1, 2023, doi: 10.1109/tcds.2023.3237612.
A. K. Kashyap, D. R. Parhi, and V. Kumar, “Navigation for multi-humanoid using MFO-aided reinforcement learning approach,” Robotica, vol. 41, no. 1, pp. 346–369, 2023, doi: 10.1017/S0263574722001357.
J. Qu, Z. Zhang, H. Li, M. Li, X. Xi, and R. Zhang, “Design and Experiments of a Two-Stage Fuzzy Controller for the Off-Center Steer-by-Wire System of an Agricultural Mobile Robot,” Machines, vol. 11, no. 2, 2023, doi: 10.3390/machines11020314.
V. T. Pham, A. Stefek, V. Krivanek, and T. S. Nguyen, “Design of a Saving-Energy Fuzzy Logic Controller for a Differential Drive Robot Based on an Optimization,” Appl. Sci., vol. 13, no. 2, 2023, doi: 10.3390/app13020997.
Z. Zhang, S. Wang, J. Chen, and Y. Han, “A Bionic Dynamic Path Planning Algorithm of the Micro UAV Based on the Fusion of Deep Neural Network Optimization/Filtering and Hawk-Eye Vision,” IEEE Trans. Syst. Man, Cybern. Syst., pp. 1–13, 2023, doi: 10.1109/tsmc.2023.3233965.
W. Tang, Y. Zhou, T. Zhang, Y. Liu, J. Liu, and Z. Ding, “Cooperative collision avoidance in multirobot systems using fuzzy rules and velocity obstacles,” Robotica, vol. 41, no. 2, pp. 668–689, 2023, doi: 10.1017/s0263574722001515.
G. G. R. d. Castro et al., “Adaptive Path Planning for Fusing Rapidly Exploring Random Trees and Deep Reinforcement Learning in an Agriculture Dynamic Environment UAVs,” Agric., vol. 13, no. 2, 2023, doi: 10.3390/agriculture13020354.
M. Bikova, V. O. Latkoska, B. Hristov, and D. Stavrov, “Path Planning Using Fuzzy Logic Control of a 2-DOF Robotic Arm,” IEEE Int. Conf. Control Autom. ICCA, vol. 2022-June, pp. 998–1003, 2022, doi: 10.1109/ICCA54724.2022.9831903.
A. K. Kashyap and D. R. Parhi, “Obstacle avoidance and path planning of humanoid robot using fuzzy logic controller aided owl search algorithm in complicated workspaces,” Ind. Rob., vol. 49, no. 2, pp. 280–288, 2022, doi: 10.1108/IR-05-2021-0091.
Y. Li, B. Tian, Y. Yang, and C. Li, “Path planning of robot based on artificial potential field method,” IEEE 6th Inf. Technol. Mechatronics Eng. Conf. ITOEC 2022, no. Imcec, pp. 91–94, 2022, doi: 10.1109/ITOEC53115.2022.9734712.
C. Ntakolia, K. S. Platanitis, G. P. Kladis, C. Skliros, and A. D. Zagorianos, “A Genetic Algorithm enhanced with Fuzzy-Logic for multi-objective Unmanned Aircraft Vehicle path planning missions,” 2022 Int. Conf. Unmanned Aircr. Syst. ICUAS 2022, pp. 114–123, 2022, doi: 10.1109/ICUAS54217.2022.9836068.
Y. Rasekhipour, A. Khajepour, S. K. Chen, and B. Litkouhi, “A Potential Field-Based Model Predictive Path-Planning Controller for Autonomous Road Vehicles,” IEEE Trans. Intell. Transp. Syst., vol. 18, no. 5, pp. 1255–1267, 2017, doi: 10.1109/TITS.2016.2604240.
J. Wei, Y.-J. Liu, H. Chen, and L. Liu, “Fuzzy Adaptive Control for Vehicular Platoons With Constraints and Unknown Dead-Zone Input,” IEEE Trans. Intell. Transp. Syst., pp. 1–10, 2023, doi: 10.1109/tits.2023.3234910.
I. Shafieenejad, E. D. Rouzi, J. Sardari, M. S. Araghi, A. Esmaeili, and S. Zahedi, “Fuzzy logic, neural-fuzzy network and honey bees algorithm to develop the swarm motion of aerial robots,” Evol. Syst., vol. 13, no. 2, pp. 319–330, 2022, doi: 10.1007/s12530-021-09391-4.
O. A. Abubakr, M. A. Jaradat, and M. F. Abdel-Hafez, “Intelligent Optimization of Adaptive Dynamic Window Approach for Mobile Robot Motion Control Using Fuzzy Logic,” IEEE Access, vol. 10, no. November, pp. 119368–119378, 2022, doi: 10.1109/ACCESS.2022.3220703.
Z. E. Kanoon, A. S. Al-Araji, and M. N. Abdullah, “Enhancement of Cell Decomposition Path-Planning Algorithm for Autonomous Mobile Robot Based on an Intelligent Hybrid Optimization Method,” Int. J. Intell. Eng. Syst., vol. 15, no. 3, pp. 161–175, 2022, doi: 10.22266/ijies2022.0630.14.
O. A. A. Salama, M. E. H. Eltaib, H. A. Mohamed, and O. Salah, “RCD: Radial Cell Decomposition Algorithm for Mobile Robot Path Planning,” IEEE Access, vol. 9, pp. 149982–149992, 2021, doi: 10.1109/ACCESS.2021.3125105.
T. Grenko et al., “On the Use of a Genetic Algorithm for Determining Ho–Cook Coefficients in Continuous Path Planning of Industrial Robotic Manipulators,” Machines, vol. 11, no. 2, p. 167, 2023, doi: 10.3390/machines11020167.
J. Palacín, E. Rubies, R. Bitrià, and E. Clotet, “Non-Parametric Calibration of the Inverse Kinematic Matrix of a Three-Wheeled Omnidirectional Mobile Robot Based on Genetic Algorithms,” Appl. Sci., vol. 13, no. 2, 2023, doi: 10.3390/app13021053.
R. Szczepanski, K. Erwinski, M. Tejer, A. Bereit, and T. Tarczewski, “Optimal scheduling for palletizing task using robotic arm and artificial bee colony algorithm,” Eng. Appl. Artif. Intell., vol. 113, no. May, p. 104976, 2022, doi: 10.1016/j.engappai.2022.104976.
L. Yang, B. Xing, C. Li, and W. Wang, “Research on Artificial Bee Colony Method Based Complete Coverage Path Planning Algorithm for Search and Rescue Robot,” 2022 5th Int. Symp. Auton. Syst. ISAS 2022, 2022, doi: 10.1109/ISAS55863.2022.9757266.
K. Nonoyama, Z. Liu, T. Fujiwara, M. M. Alam, and T. Nishi, “Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization,” Energies, vol. 15, no. 6, 2022, doi: 10.3390/en15062074.
“MOBILE ROBOT NAVIGATION IN KNOWN MANUFACTURING PLANT,” vol. 38, no. 1, pp. 60–65, 2023, doi: 10.5281/zenodo.7586073.
Y. Long, S. Liu, D. Qiu, C. Li, X. Guo, and B. Shi, “Local Path Planning with Multiple Constraints for USV Based on Improved Bacterial Foraging Optimization Algorithm,” 2023.
Z. Wang, J. Peng, and S. Ding, “A Bio-inspired trajectory planning method for robotic manipulators based on improved bacteria foraging optimization algorithm and tau theory,” Math. Biosci. Eng., vol. 19, no. 1, pp. 643–662, 2022, doi: 10.3934/mbe.2022029.
T. Thi et al., “Wave Environment Decomposition with Adaptive Tri-Objective Particle Swarm Optimization for Mobile Robot Path Planning,” 2022 IEEE Symp. Ser. Comput. Intell. (SSCI 2022), pp. 990–997, 2022.
L. Yang, L. Fu, P. Li, J. Mao, and N. Guo, “An Effective Dynamic Path Planning Approach for Mobile Robots Based on Ant Colony Fusion Dynamic Windows,” Machines, vol. 10, no. 1, 2022, doi: 10.3390/machines10010050.
X. Meng and X. Zhu, “SS symmetry Autonomous Obstacle Avoidance Path Planning for Grasping,” 2022.
B. K. Patle, N. Pagar, D. R. K. Parhi, and S. Sanap, “Hybrid FA-GA Controller for Path Planning of Mobile Robot,” 2022 Int. Conf. Intell. Controll. Comput. Smart Power, ICICCSP 2022, pp. 6–11, 2022, doi: 10.1109/ICICCSP53532.2022.9862422.
Y. Wang, X. Li, J. Zhang, S. Li, Z. Xu, and X. Zhou, “Review of wheeled mobile robot collision avoidance under unknown environment,” Sci. Prog., vol. 104, no. 3, pp. 1–26, 2021, doi: 10.1177/00368504211037771.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Shitsukane Shisiali, Eng. Amuti Mathews, Otieno Calvins, Obuhuma James
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who submit papers with this journal agree to the following terms.
