摘要： 在无人机执行任务的过程中，识别潜在的碰撞风险并采取必要的机动措施是确保安全飞行的关键。针对环境障碍物及多无人机防撞问题，提出一种基于自适应人工势场的协商防撞算法。首先，综合考虑时间和距离两方面因素进行冲突检测，并引入自适应冲突判定系数以减少不必要的避撞机动。其次，提出自适应调整斥力增益系数的方法，防止因机动性能限制和初始斥力增益系数设置不当而造成碰撞的行为。同时，设计了基于关键度的多无人机协商策略，减少了机间冗余的避撞动作。此外，基于无人机运动学模型，根据最新时刻获得的信息，对邻居无人机的状态进行预测，以降低数据链时延和丢包引起的误差。与传统人工势场法相比，所提算法在斥力增益系数较小时仍能有效避撞，且将总路径长度缩短约1.76%。当数据链时延不超过200ms以及丢包率低于50%时，所提算法均能表现出良好的避撞性能。

Abstract: During the execution of tasks, identifying the potential collision risk and taking necessary maneuvering measures are critical to ensure the safe flight of Unmanned Aerial Vehicles (UAVs). To address the challenges of obstacle avoidance in external environments and inter-UAV collision avoidance among multiple drones, this paper proposes a cooperative collision avoidance algorithm based on adaptive artificial potential field method. Firstly, the proposed algorithm considers time and distance factors for conflict detection, and introduces an adaptive conflict detection coefficient to reduce unnecessary collision avoidance maneuvers. Then, an adaptive method for adjusting the repulsive force gain coefficient is proposed to prevent collisions caused by maneuverability limitations and improper initial settings. Besides, to reduce redundant actions during collision avoidance between UAVs, a priority-based conflict resolution strategy is developed. In addition, based on the UAV kinematic model, compensation for errors caused by communication delay and packet loss is made by predicting neighbor UAV’s position using the most recent information. Considering maneuverability constraints, the proposed algorithm outperforms the traditional APF by effectively avoiding collisions with a small repulsive gain coefficient and reducing the total path length by about 1.76%. When the data link latency is under 200ms and packet loss is below 50%, the proposed method shows good performance in avoiding collisions.