Stealthy Path Planning Algorithm for UAV Swarm Based on Improved APF-RRT* Under Dynamic Threat

ZHANG Xinrui; SHI Chenguang; WU Zhifeng; WEN Wen; ZHOU Jianjiang

doi:10.11999/JEIT250554

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ZHANG Xinrui, SHI Chenguang, WU Zhifeng, WEN Wen, ZHOU Jianjiang. Stealthy Path Planning Algorithm for UAV Swarm Based on Improved APF-RRT* Under Dynamic Threat[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250554

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ZHANG Xinrui, SHI Chenguang, WU Zhifeng, WEN Wen, ZHOU Jianjiang. Stealthy Path Planning Algorithm for UAV Swarm Based on Improved APF-RRT* Under Dynamic Threat[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250554

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Stealthy Path Planning Algorithm for UAV Swarm Based on Improved APF-RRT* Under Dynamic Threat

doi: 10.11999/JEIT250554 cstr: 32379.14.JEIT250554

ZHANG Xinrui^{1, 2},
SHI Chenguang^{1, 2
,
,},
WU Zhifeng²,
WEN Wen^{1, 2},
ZHOU Jianjiang¹

1.
Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2.
Jianghuai Advance Technology Center, Hefei, 230000, China

Funds: This work is supported in part by the National Natural Science Foundation of China under Grant 62271247, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20240181, in part by the National Aerospace Science Foundation of China under Grant 20220055052001, in part by Qing Lan Project of Jiangsu Province, and in part by Dreams Foundation of Jianghuai Advance Technology Center under Grant 2023-ZM01D001

Received Date: 2025-06-16
Accepted Date: 2025-11-03
Rev Recd Date: 2025-11-03

Available Online: 2025-11-12

Abstract

Abstract

Objective The efficient penetration and survivability of unmanned aerial vehicle (UAV) swarms in complex battlefield environments critically depend on robust trajectory planning. With the increasing deployment of advanced air defense systems—featuring radar network, anti-aircraft artillery and dynamic no-fly zones—conventional planning methods struggle to meet simultaneous requirements for stealth, feasibility, and safety. Although prior studies have contributed valuable insights into UAV swarm path planning, they present several limitations: (1) Most research focuses on detection models for single radars and does not sufficiently incorporate the coupling between UAV radar cross section (RCS) and stealth trajectory optimization; (2) UAV kinematic constraints are often treated independently of stealth characteristics; (3) Environmental threats are typically modeled as static and singular, overlooking real-time dynamic threats; (4) Stealth planning is predominantly studied for individual UAVs, with limited attention to swarm-level coordination. This work addresses these gaps by proposing a cooperative stealth trajectory planning framework that integrates real-time threat perception with swarm dynamics optimization, significantly enhancing survivability in contested airspace. Methods To overcome the aforementioned challenges, this paper proposes a stealth path planning algorithm for UAV swarm based on improved artificial potential field (APF) and rapidly-exploring random trees star (RRT*) framework under dynamic threat. First, a multi-threat environment model is constructed, incorporating radars, anti-aircraft artillery, and fixed obstacles. A comprehensive stealth cost function is developed by integrating UAV RCS characteristics, accounting for flight distance, radar detection probability, and artillery threat probability. Second, a stealth trajectory optimization model is formulated with the objective of minimizing the overall cost function, subject to strict constraints on UAV kinematics, swarm coordination, and path feasibility. To solve this model efficiently, an enhanced APF-RRT* algorithm is designed. A rolling-window strategy is introduced to facilitate continuous local replanning in response to dynamic threats, enabling real-time trajectory updates and improving responsiveness to sudden changes in the threat landscape. Furthermore, a target-biased sampling technique is applied to reduce sampling redundancy, thereby enhancing algorithmic convergence speed. By combining the global search capability of RRT* with the local adaptability of APF, the proposed approach enables UAV swarms to generate stealth-optimal paths in real time while maintaining high levels of safety and coordination in adversarial environments. Results and Discussions Simulation experiments validate the effectiveness of the proposed algorithm. During global path planning, some UAVs enter regions threatened by dynamic no-fly zones, radars, and artillery systems, while others successfully reach their destinations through unobstructed paths. In the local replanning phase, affected UAVs adaptively adjust their trajectories to minimize radar detection probability and overall stealth cost. When encountering mobile threats, UAVs perform lateral evasive maneuvers to avoid collisions and ensure mission completion. In contrast, the detection probabilities of the UAVs requiring replanning all exceed the specified threshold for networked radar detection under the comparison algorithms. This indicates that, in practical scenarios, the comparison algorithms fail to generate UAV swarm trajectories that meet platform safety requirements, rendering them ineffective. Comparative simulations demonstrate that the proposed method significantly outperforms existing approaches by reducing stealth costs and improving trajectory feasibility and swarm coordination. The algorithm achieves optimal swarm-level stealth and ensures safe and efficient penetration in dynamic environments. Conclusions This study addresses the problem of stealth trajectory planning for UAV swarms in dynamic threat environments by proposing an improved APF-RRT* algorithm. The following key findings are derived from extensive simulations conducted across different contested scenarios (Section 5): (1) The proposed algorithm reduces the voyage distance by 11.1km in scene 1 and 66.9km in scene 2 compared with the baseline RRT* method (Tab. 3, Tab. 5), primarily due to RCS-minimizing attitude adjustments by heading angle chang (Fig. 3, Fig. 6); (2) The networked radar detection probability remains below the 30% threshold for all UAVs (Fig. 4(a), Fig. 7(a)), whereas comparison algorithm exceed the safety limit of 98% of the group members at most (Fig. 4(b), Fig. 7(b), Fig. 9(a), Fig. 9(b)); (3) The rolling-window replanning mechanism enables real-time avoidance of mobile threats such as dynamic no-fly zones and anti-aircraft artillery (Fig. 5, Fig. 8), while simultaneously reducing the comprehensive trajectory cost by 9.0% in Scene 1 and 15.6% in Scene 2 compared with the baseline RRT method (Tab. 3, Tab. 5). (4) Cooperative constraints embedded in the planning algorithm maintain safe inter-UAV separation and jointly optimize swarm-level stealth performance (Fig. 2, Fig. 5, Fig. 8). These results collectively demonstrate the superiority of the proposed method in balancing stealth optimization, dynamic threat adaptation, and swarm kinematic feasibility. Future research will extend this framework to 3D complex terrain environments and integrate deep reinforcement learning to further enhance predictive threat response and battlefield adaptability.
- Stealthy path planning,
- UAV swarm,
- Dynamic threat,
- Artificial potential field,
- Rapidly-exploring random trees star

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References(37)

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