A Phase Transition Obstacle Avoidance Method for UAV Swarms Driven by Multistable Potential Fields

HE Ming; CHEN QiYang; HAN Wei; PAN Fan; MA YiSong

doi:10.11999/JEIT260357

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HE Ming, CHEN QiYang, HAN Wei, PAN Fan, MA YiSong. A Phase Transition Obstacle Avoidance Method for UAV Swarms Driven by Multistable Potential Fields[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260357

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HE Ming, CHEN QiYang, HAN Wei, PAN Fan, MA YiSong. A Phase Transition Obstacle Avoidance Method for UAV Swarms Driven by Multistable Potential Fields[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260357

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A Phase Transition Obstacle Avoidance Method for UAV Swarms Driven by Multistable Potential Fields

doi: 10.11999/JEIT260357 cstr: 32379.14.JEIT260357

HE Ming^{1, 2, 3},
CHEN QiYang^{1
,
,},
HAN Wei¹,
PAN Fan⁴,
MA YiSong²

1.
College of Command and Control Engineering, Army Engineering University, Nanjing 210007, Jiangsu Province
2.
Unit 32180 Chinese People’s Liberation Army, Beijing 100072, China
3.
National Key Laboratory of Near-Surface Detection, Beijing 100072, China
4.
Unit 96901, Chinese People’s Liberation Army, Beijing 100094, China

Funds: The National Natural Science Foundation of China (62273356), The National-level Talent Program (2022-JCJQ-ZQ-001), The National Key Research and Development Program (2024YFF140140), Qiyuan Laboratory Fund (2025-JCJQ-LA-001-101)

Received Date: 2026-03-30
Accepted Date: 2026-06-29
Rev Recd Date: 2026-06-24

Available Online: 2026-07-07

Abstract

Abstract

Objective Unmanned Aerial Vehicle (UAV) swarms have demonstrated considerable potential for complex missions, such as search, surveillance, and disaster response, because of their distributed coordination and robustness. However, in dynamic environments with dense obstacles and rapidly changing risks, conventional swarm control methods often exhibit discontinuous behavior switching and control chattering, which reduce system stability and coordination efficiency. Existing approaches, including threshold-based switching and Artificial Potential Field (APF) methods with fixed potential weights, rely on abrupt transitions between behavioral modes, leading to oscillatory responses. To address these limitations, a phase transition obstacle avoidance method for UAV swarms driven by multistable potential fields is proposed. Swarm behavior evolution is modeled as a continuous phase transition process within a unified potential field framework, enabling smooth and adaptive transitions between formation flight and obstacle avoidance. Methods An environmental risk assessment model is first established by integrating static obstacle risk, dynamic obstacle risk, and inter-agent proximity risk. A distributed consensus protocol is then employed to establish global risk consensus. Subsequently, a morphology factor is generated through nonlinear mapping of the global risk consensus and is used as an order parameter to characterize the macroscopic swarm state. A unified time-varying potential field, comprising formation, obstacle avoidance, and navigation potentials, is constructed, and the relative weights of these potentials are continuously adjusted by the morphology factor. When the risk level is low, the system exhibits a monostable structure dominated by the formation and navigation potentials. As the risk increases, the potential field continuously evolves into a multistable structure dominated by the obstacle avoidance potential, thereby enabling distributed obstacle avoidance. A distributed consensus control law based on the negative gradient of the unified potential field is further developed. A damping term is incorporated to dissipate system energy and improve stability, while a dynamic compensation term addresses nonlinear dynamics. The control law depends only on local information, ensuring good scalability. The global uniform ultimate boundedness of the closed-loop system is established using Lyapunov theory. Results and Discussions Simulation results demonstrate that the proposed method enables the swarm to maintain a compact solid-phase swarm formation in low-risk regions and to transition smoothly to a dispersed liquid-phase swarm configuration when obstacles are encountered, followed by rapid formation recovery after obstacle avoidance. The pitch and roll angles of each UAV vary smoothly without abrupt changes, and both the UAV-to-obstacle distance and the inter-UAV separation remain above the prescribed safety threshold throughout the flight, ensuring collision-free operation. Statistical results obtained from 20 independent simulation runs show that, compared with the threshold-switching method, the proposed method reduces the rate of control input variation by approximately 26% and decreases the peak control input by approximately 18%. Compared with the bio-inspired diversion method, the average formation recovery time after obstacle avoidance is reduced by approximately 16%. Ablation experiments further demonstrate that removing the morphology-driven phase transition mechanism significantly increases trajectory oscillation and control oscillation, confirming the critical role of the multistable continuous phase transition mechanism in maintaining smooth swarm motion. In complex narrow-channel environments, the proposed method effectively avoids the local minimum problem encountered by conventional APF methods and generates smoother flight trajectories with substantially reduced oscillation. Conclusions A phase transition obstacle avoidance method for UAV swarms driven by multistable potential fields is proposed. By introducing a morphology factor and constructing a unified potential field framework, swarm behavior evolution is represented as a continuous phase transition process. The distributed control law enables smooth behavioral transitions while maintaining system stability and scalability. Simulation results demonstrate that the proposed method achieves better safety, smoother control, and higher coordination efficiency than conventional methods.
- Unmanned Aerial Vehicle (UAV) swarms,
- Phase transition control,
- Swarm intelligence,
- Obstacle avoidance,
- Multistable potential fields

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References(22)

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