基于ACO信息素与PSO调参的无人机去中心化编队研究
Decentralised Drone Swarm Formation under Silent Failures with ACO Pheromone Claims and PSO Tuning
这篇论文挺有意思：不用中心服务器，靠信息素蒸发来检测掉队无人机，一半机群静默挂掉也能恢复编队。适合做无人机集群、分布式系统容错的开发者，结论也提醒大家：效果主要靠调参而非算法本身的随机机制。
arXiv 论文研究无中心规划器的无人机群编队控制，针对静默故障、短距易失联通信和执行噪声等困难条件。控制器用蚁群优化（ACO）为无人机分配拍摄位置，分配声明以强化并蒸发的信息素形式在邻居间传播，蒸发机制同时充当故障检测。粒子群优化（PSO）负责飞行控制，Voronoi 滤波防止碰撞。
论文证明了多项性质：被摧毁无人机的声明会在有限时间内消失，交换信息素表的无人机间冲突可一致解决，幸存者对画面的覆盖不超过最优的2倍，安全单元可行时不会相撞。在留出种子上，最难场景下95-99%的运行成功成图；正常难度下，半数无人机静默损失后96-100%的运行可恢复编队。控制器无需修改即可迁移到8种未见形状和300架无人机规模，21,088次运行中仅5次重叠。
对比实验显示，用同样预算调参后，一个共享蒸发声明但用 argmax 而非交换的 CBBA 风格基线可靠性相当且修复更快；本文方法在未见形状上的泛化更好。作者指出收益来自信息素动力学和调参，而非ACO的随机构造或PSO的社会项，中心化Oracle仍更快。
Decentralised Drone Swarm Formation under Silent Failures with ACO Pheromone Claims and PSO Tuning
We study decentralised drone formation, without a central planner, under the conditions that make it hard: nobody knows how many drones are alive, failures are silent, radio is short-ranged and lossy, and actuation is noisy. In our controller, ant colony optimisation (ACO) allocates drones to picture positions through claims that behave as reinforced, evaporating pheromone and are gossiped between neighbours, so evaporation doubles as failure detection. A particle swarm optimisation (PSO) law flies the drones, a noise-buffered Voronoi filter keeps them apart, and PSO also tunes the controller offline. We prove that claims of destroyed drones vanish within bounded time, that conflicts between drones that exchange tables are resolved consistently, that survivors cover the picture to within a factor of two, and that drones cannot collide while their safety cells are feasible. On held-out seeds and with two tuned configurations, the controller formed the picture in 95-99% of runs in the hardest regime, restored it after a silent loss of half the swarm in 96-100% of runs at normal difficulty, transferred unchanged to eight unseen shapes and to 300 drones, and overlapped in 5 of 21,088 runs. A CBBA-style baseline that shares the evaporating claims but uses argmax without swaps was at least as reliable as ours once tuned with the same budget, and repaired faster, whereas ours generalised better to unseen shapes. The benefit thus comes from the pheromone dynamics and from tuning, not from ACO's stochastic construction or PSO's social terms. A centralised oracle is faster.
This report is categorized under AITOP's "Papers" desk. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include ACO, PSO, 无人机集群, 去中心化.
Source attribution: curated from arXiv cs.AI (@Md. Abu Horaira).