A Hybrid Path Planning Framework for Inspection Robots: Integrating Improved A-Star Algorithm with Dynamic Safety Constraints
Main Article Content
Abstract
Path planning is a core technology for enabling inspection robots to operate safely and efficiently in complex industrial spaces, including smart manufacturing sites where sensor reliability, wireless communication continuity, and electromagnetic interference may affect autonomous navigation. Although the classical A-star algorithm is widely used in global path planning because of its completeness and optimality guarantees, it often suffers from computational inefficiency, excessive node expansion, and non-smooth trajectories that are unsuitable for real robot motion. To address these limitations, this work proposes an enhanced A-star algorithm for inspection robots. The heuristic function is redesigned with an adaptive weighting mechanism, balancing search speed and path optimality and reducing the number of expanded nodes. A safety-aware strategy is further introduced by adding virtual safety buffers around obstacles, allowing the robot to maintain a minimum safe distance during navigation. Finally, Bézier curve interpolation is used to improve path smoothness and kinematic feasibility by generating continuous and differentiable trajectories. Simulation experiments show that, compared with the standard A-star algorithm, the proposed method reduces searched nodes by nearly 50% and redundant turns by more than 60%. Overall inspection efficiency is improved by up to 90%, confirming the method’s practicality for robot navigation in complex engineering environments.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
S. Alshammrei, S. Boubaker, and L. Kolsi, “Improved Dijkstra algorithm for mobile robot path planning and obstacle avoidance,” Comput- ers Materials & Continua, vol. 72, no. 3, pp. 5939-5954, 2022, DOI: 10.32604/CMC.2022.028165.
S. Baik, H. J. Bong, and S. Jeong, “Weight-incorporating A* algorithm with multi-factor cost function for enhanced mobile robot path planning,” Actuators, vol. 14, no. 8, p. 369, 2025, DOI: 10.3390/ACT14080369.
X. Bu, G. Li, and B. Tong, “A robot navigation system based on improved A-star algorithm,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 38, no. 16, 2024, DOI: 10.1142/S0218001424560123.
Y. Chen, Y. Liu, and W. Xu, “Improved hybrid A*algorithm based on Lemming optimization for path planning of autonomous vehicles,” Applied Sciences, vol. 15, no. 14, p. 7734, 2025, DOI: 10.3390/APP15147734.
Z. Chi, Z. Yu, and Q. Wei, “High-efficiency navigation of nonholonomic mobile robots based on improved hybrid A* algorithm,” Applied Sciences, vol. 13, no. 10, 2023, DOI: 10.3390/APP13106141.
J. Dai, Y. Chai, and P. Xiong, “Path planning of underground robots via improved A* and dynamic window approach,” Applied Sciences, vol. 15, no. 13, p. 6953, 2025, DOI: 10.3390/APP15136953.
Y. Fang, J. He, X. Wang, and B. Ma, “A* algorithm for on-site collaborative path planning in building construction robots,” Buildings, vol. 15, no. 21, p. 3876, 2025, DOI: 10.3390/BUILDINGS15213876.
H. Ge, Z. Ying, Z. Chen, and B. Zhang, “Improved A* algorithm for path planning of spherical robot considering energy consumption,” Sensors, vol. 23, no. 16, 2023, DOI: 10.3390/S23167115.
J. Guo, J. Yu, C. Feng, and B. Li, “Global path planning on uneven terrain at construction sites for robots based on an improved A* algorithm,” Journal of Ambient Intelligence and Smart Environments, vol. 17, no. 3, pp. 302-325, 2025, DOI: 10.1177/18761364241296814.
S. Huang, X. Wu, and Y. Wang, “Adaptive observation A* (AOA): Efficient deep heuristic method for global path planning in mountainous terrain,” Information Sciences, vol. 731, Art. no. 122916, 2026, DOI: 10.1016/J.INS.2025.122916.
R. Kabir, Y. Watanobe, and R. M. Islam, “Enhanced robot motion block of A-star algorithm for robotic path planning,” Sensors, vol. 24, no. 5, 2024, DOI: 10.3390/S24051422.
A. Liu and H. Liu, “Path planning of intelligent robots based on improved A* algorithm,” Journal of Physics: Conference Series, vol. 3055, no. 1, Art. no. 012001, 2025, DOI: 10.1088/1742-6596/3055/1/012001.
X. Liu, W. Zhou, C. Zhai, and S. Li, “Path planning and optimization of space robots on satellite surfaces based on an improved A* algorithm and B-spline curves,” Aerospace, vol. 12, no. 10, pp. 943, 2025, DOI: 10.3390/AEROSPACE12100943.
H. Ryu and K. W. Chung, “Local map-based exploration using a breadth-first search algorithm for mobile robots,” International Journal of Precision Engineering and Manufacturing, vol. 16, no. 10, pp. 2073-2080, 2015, DOI: 10.1007/s12541-015-0269-9.
W. Wu, C. Kong, Z. Xiao, and J. Xu, “3D-MIHE-RRT-A*: multi-indicator heuristic evaluation hybrid path planning algorithm for UAV navigation in complex environments,” Measurement, vol. 260, Art. no. 119838, 2026, DOI: 10.1016/J.MEASUREMENT.2025.119838.
X. J. Wu, P. Peng, L. Wang, and L. Feng, “A hybrid A* path planning algorithm for underground load-haul-dump vehicles guided by reference states,” Measurement Science and Technology, vol. 36, no. 10, Art. no. 106215, 2025, DOI: 10.1088/1361-6501/AE10CE.
X. Xing, Z. Jiazhu, Z. Yun, and S. Wei, “Research on global path planning algorithm for mobile robots based on improved A*,” Expert Systems with Applications, vol. 243, Art. no. 122922, 2024, DOI: 10.1016/J.ESWA.2023.122922.
Y. Yang, Z. Meng, W. Guo, and W. Li, “Full coverage path planning for multiple farming machines based on an improved A* algorithm,” Journal of Physics: Conference Series, vol. 2991, no. 1, Art. no. 012001, 2025, DOI: 10.1088/1742-6596/2991/1/012001.
W. Zhang, W. Li, X. Zheng, and H. Li, “Improved A* and DWA fusion algorithm based path planning for intelligent substation inspection robot,” Measurement and Control, vol. 59, no. 1, pp. 113-119, 2026, DOI: 10.1177/00202940251316687.
X. Zhou, J. Yan, M. Yan, and G. Wang, “Path planning of rail-mounted logistics robots based on the improved Dijkstra algorithm,” Applied Sciences, vol. 13, no. 17, pp. 9955, 2023, DOI: 10.3390/APP13179955.
H. Zhuang, S. Wang, N. Wang, and W. Liu, “Low-energy path planning method of electrically driven heavy-duty six-legged robot based on improved A* algorithm,” Applied Sciences, vol. 15, no. 24, Art. no. 13113, 2025, DOI: 10.3390/APP152413113.
D. Linjie, Z. Renfei, W. Junfei, and D. Zongcheng, “Research on the path planning algorithm and obstacle-crossing motion planning strategy for a cable trench inspection robot,” Robotica, vol. 43, no. 2, pp. 433-448, 2024, DOI: 10.1017/S0263574724001930.