Cooperative navigation for fleets of robots conventionally adopts algorithms based on Reynolds's flocking rules, which usually use a weighted sum of vectors for calculating the velocity from behavioral velocity vectors with corresponding fixed weights. Although optimal values of the weighting coefficients giving good performance can be found through many experiments for each particular scenario, the overall performance could not be guaranteed due to unexpected conditions not covered in experiments. This paper proposes a novel control scheme for a swarm of Unmanned Aerial Vehicles (UAVs) that also employs the original Reynolds rules but adopts an adaptive weight allocation mechanism based on the current context than being fixed at the beginning. The simulation results show that our proposed scheme has better performance than the conventional Reynolds-based ones in terms of the flock compactness and the reduction in the number of crashed swarm members due to collisions. The analytical results of behavioral rules’ impact also validate the proposed weighting mechanism's effectiveness leading to improved performance.

中文翻译：

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基于雷诺规则的植绒控制方案的自适应加权机制

机器人车队的协作导航通常采用基于雷诺（Reynolds）植绒规则的算法，该算法通常使用向量的加权总和从具有相应固定权重的行为速度向量中计算速度。尽管可以通过针对每个特定场景的许多实验找到提供良好性能的加权系数的最佳值，但由于实验中未涵盖的意外条件，无法保证总体性能。本文提出了一种新颖的无人飞行器（UAV）群控制方案，该方案也采用了原始的雷诺规则，但采用了基于当前上下文的自适应权重分配机制，而不是一开始就被固定下来。仿真结果表明，我们提出的方案在群体紧凑性和减少因碰撞而导致的成群虫害数量方面比传统的基于雷诺兹的方案具有更好的性能。行为规则影响的分析结果也验证了所提出的加权机制的有效性，从而提高了性能。