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

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

doi:10.11999/JEIT260357

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HE Ming, CHEN QiYang, HAN Wei, PAN Pan, 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 Pan, 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 Pan⁴,
MA YiSong²

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

Funds: The National Natural Science Foundation of China (62273356), National-level Talent Program (2022-JCJQ-ZQ-001), 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: 2026-06-22

Available Online: 2026-07-07

Abstract

Abstract

Objective Unmanned aerial vehicle swarms have demonstrated significant potential in complex missions such as search, surveillance, and disaster response due to their distributed coordination and robustness. However, in dynamic environments with dense obstacles and rapidly changing risks, conventional swarm control methods often suffer from discontinuous behavior switching and control chattering, which degrade stability and coordination efficiency. Existing approaches, including threshold-based switching and fixed-weight artificial potential fields, rely on abrupt transitions between behavioral modes, leading to oscillations. To address these issues, this paper proposes a phase transition obstacle avoidance method for UAV swarms driven by multi-stable potential fields, where swarm behavior evolution is modeled as a continuous phase transition process within a unified potential field framework, enabling smooth and adaptive transitions between formation and obstacle avoidance behaviors. Methods An environmental situation awareness model is first established by integrating static obstacle risk, dynamic obstacle risk, and inter-agent proximity risk. A distributed consensus protocol is employed to obtain global risk awareness. A morphology factor is then introduced as an order parameter via nonlinear mapping of the global risk, characterizing the macroscopic swarm state.A unified time-varying potential field is constructed, consisting of formation, obstacle avoidance, and navigation potentials, whose relative weights are dynamically adjusted by the morphology factor. When the risk is low, the system exhibits a mono-stable structure dominated by formation and navigation potentials; as the risk increases, it transitions to a multi-stable structure dominated by avoidance potential, enabling distributed obstacle avoidance.A distributed consensus control law based on the negative gradient of the potential field is further designed. A damping term ensures energy dissipation and stability, while a dynamic compensation term addresses nonlinear dynamics. The control law relies only on local information, ensuring scalability. The global uniform ultimate boundedness of the closed-loop system is proven using Lyapunov theory. Results and Discussions Simulation results demonstrate that the proposed method enables the swarm to maintain compact solid-phase formation in low-risk areas and smoothly transit to dispersed liquid-phase structure when encountering obstacles, followed by rapid integral regrouping after obstacle traversal. The pitch and roll angles of each UAV change gently without sharp jumps, and the distances between UAVs and obstacles/inter-UAV gaps always stay above the preset safety threshold, guaranteeing collision-free flight. Quantitative comparison statistics from 20 repeated experiments show that compared with threshold-switching control, the variation rate of control inputs is reduced by 26% and the peak control magnitude drops by 18%; compared with bionic shunting methods, the formation recovery time after obstacle avoidance is shortened by 16%. Ablation test results verify that removing the morphology-driven phase transition mechanism will obviously amplify trajectory fluctuation and control oscillation, which proves the core role of multi-stable continuous phase transition in smoothing swarm motion. In narrow channel complex environments, the proposed method effectively avoids the local minimum trap existing in traditional APF and produces smoother flight trajectories without obvious vibration. Conclusions A phase transition obstacle avoidance method for UAV swarms based on multi-stable potential fields is proposed. By introducing a morphology factor and constructing a unified potential field, swarm behavior evolution is modeled as a continuous phase transition process. The distributed control law ensures smooth behavior transitions, stability, and scalability.Simulation results verify that the proposed method outperforms conventional approaches in safety, smoothness, and coordination efficiency.
- UAV swarms,
- phase transition control,
- swarm intelligence,
- obstacle avoidance,
- multi-stable potential field

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References(22)

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