Entropy Quantum Collaborative Planning Method for Emergency Path of Unmanned Aerial Vehicles Driven by Survival Probability

WANG Enliang; ZHANG Zhen; SUN Zhixin

doi:10.11999/JEIT250694

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WANG Enliang, ZHANG Zhen, SUN Zhixin. Entropy Quantum Collaborative Planning Method for Emergency Path of Unmanned Aerial Vehicles Driven by Survival Probability[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250694

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WANG Enliang, ZHANG Zhen, SUN Zhixin. Entropy Quantum Collaborative Planning Method for Emergency Path of Unmanned Aerial Vehicles Driven by Survival Probability[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250694

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Entropy Quantum Collaborative Planning Method for Emergency Path of Unmanned Aerial Vehicles Driven by Survival Probability

doi: 10.11999/JEIT250694 cstr: 32379.14.JEIT250694

1.
Jiangsu Postal Big Data Technology and Application Engineering Research Center, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2.
Nanjing University of Posts and Telecommunications, National Postal Industry Technology R & D Center (Internet of Things Technology), Nanjing 210003, China

Funds: The National Natural Science Foundation of China (61972208, 62272239), Jiangsu Agriculture Science and Technology Innovation Fund(JASTIF) (CX(22)1007), Guizhou Provincial Key Technology R&D Program ([2023]272)

Received Date: 2025-07-23
Rev Recd Date: 2025-10-16

Available Online: 2025-10-27

Abstract

Abstract

Objective Natural disaster emergency rescue places stringent requirements on the timeliness and safety of Unmanned Aerial Vehicle (UAV) path planning. Conventional optimization objectives, such as minimizing total distance, often fail to reflect the critical time-sensitive priority of maximizing the survival probability of trapped victims. Moreover, existing algorithms struggle with the complex constraints of disaster environments, including no-fly zones, caution zones, and dynamic obstacles. To address these challenges, this paper proposes an Entropy-Enhanced Quantum Ripple Synergy Algorithm (E²QRSA). The primary goals are to establish a survival probability maximization model that incorporates time decay characteristics and to design a robust optimization algorithm capable of efficiently handling complex spatiotemporal constraints in dynamic disaster scenarios. Methods E²QRSA enhances the Quantum Ripple Optimization framework through four key innovations: (1) information entropy–based quantum state initialization, which guides population generation toward high-entropy regions; (2) multi-ripple collaborative interference, which promotes beneficial feature propagation through constructive superposition; (3) entropy-driven parameter control, which dynamically adjusts ripple propagation according to search entropy rates; and (4) quantum entanglement, which enables information sharing among elite individuals. The model employs a survival probability objective function that accounts for time-sensitive decay, base conditions, and mission success probability, subject to constraints including no-fly zones, warning zones, and dynamic obstacles. Results and Discussions Simulation experiments are conducted in medium- and large-scale typhoon disaster scenarios. The proposed E²QRSA achieves the highest survival probabilities of 0.847 and 0.762, respectively (Table 1), exceeding comparison algorithms such as SEWOA and PSO by 4.2–16.0%. Although the paths generated by E²QRSA are not the shortest, they are the most effective in maximizing survival chances. The ablation study (Table 3) confirms the contribution of each component, with the removal of multi-ripple interference causing the largest performance decrease (9.97%). The dynamic coupling between search entropy and ripple parameters (Fig. 2) is validated, demonstrating the effectiveness of the adaptive control mechanism. The entanglement effect (Fig. 4) is shown to maintain population diversity. In terms of constraint satisfaction, E²QRSA-planned paths consume only 85.2% of the total available energy (Table 5), ensuring a safe return, and all static and dynamic obstacles are successfully avoided, as visually verified in the 3D path plots (Figs. 6 and 7). Conclusions E²QRSA effectively addresses the challenge of UAV path planning for disaster relief by integrating adaptive entropy control with quantum-inspired mechanisms. The survival probability objective captures the essential requirements of disaster scenarios more accurately than conventional distance minimization. Experimental validation demonstrates that E²QRSA achieves superior solution quality and faster convergence, providing a robust technical basis for strengthening emergency response capabilities.
- Disaster relief,
- UAV path planning,
- Quantum ripple optimization,
- Information entropy,
- Survival probability

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References(22)

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