arXiv 2504.110642025-04-15cs.MAcs.ROcs.SYeess.SY

一种结合改进模拟退火与自适应人工势场的多无人机编队避障方法

A Multi-UAV Formation Obstacle Avoidance Method Combined Improved Simulated Annealing and Adaptive Artificial Potential Field

School of Aeronautics and Astronautics, University of Electronic Science and Technology of China（电子科技大学航空航天学院）；SDU-ANU Joint Science College, Shandong University（山东大学山东大学-澳大利亚国立大学联合科学学院）；Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China（电子科技大学长三角研究院（湖州））；Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China（电子科技大学四川省飞行器集群智能感知与协同控制重点实验室）；National Key Laboratory of Adaptive Optics, Chengdu（成都自适应光学全国重点实验室）

Bo Ma, Yi Ji, Liyong Fang

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AI总结：

针对传统人工势场（APF）方法力分布不合理、易陷入局部极小值的问题，本文提出DSA-AAPF混合算法，结合改进模拟退火与增强APF模型，集成Leader-Follower策略，通过自适应增益、快速收敛控制器和方向偏转机制提升多无人机编队避障性能，仿真验证其可行性与优越性。

摘要：

传统人工势场（APF）方法在力分布方面存在局限性：无人机远离目标时吸引力过大可能导致与障碍物碰撞，而接近目标时吸引力不足常导致无法到达目标。此外，APF极易陷入局部极小值，降低了复杂环境下的运动可靠性。为解决这些挑战，本文提出一种新型混合避障算法——偏转模拟退火-自适应人工势场（DSA-AAPF），该算法结合了改进的模拟退火机制与增强的APF模型。所提出的方法将Leader-Follower分布式编队策略与APF框架相集成，其中合力公式被重新定义以平滑无人机轨迹。引入自适应引力增益函数，基于环境上下文动态调整无人机速度；同时采用快速收敛控制器，确保准确且高效地收敛至目标。此外，在模拟退火过程中嵌入方向偏转机制，通过连续旋转运动使无人机能够逃离半封闭障碍物导致的局部极小值。涵盖编队重构、复杂避障和陷阱逃离的仿真结果，证明了所提出的DSA-AAPF算法的可行性、鲁棒性和优越性。

英文摘要：

The traditional Artificial Potential Field (APF) method exhibits limitations in its force distribution: excessive attraction when UAVs are far from the target may cause collisions with obstacles, while insufficient attraction near the goal often results in failure to reach the target. Furthermore, APF is highly susceptible to local minima, compromising motion reliability in complex environments. To address these challenges, this paper presents a novel hybrid obstacle avoidance algorithm-Deflected Simulated Annealing-Adaptive Artificial Potential Field (DSA-AAPF)-which combines an improved simulated annealing mechanism with an enhanced APF model. The proposed approach integrates a Leader-Follower distributed formation strategy with the APF framework, where the resultant force formulation is redefined to smooth UAV trajectories. An adaptive gravitational gain function is introduced to dynamically adjust UAV velocity based on environmental context, and a fast-converging controller ensures accurate and efficient convergence to the target. Moreover, a directional deflection mechanism is embedded within the simulated annealing process, enabling UAVs to escape local minima caused by semi-enclosed obstacles through continuous rotational motion. The simulation results, covering formation reconfiguration, complex obstacle avoidance, and entrapment escape, demonstrate the feasibility, robustness, and superiority of the proposed DSA-AAPF algorithm.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。