A technique that allows a formation-enforcing control (FEC) derived from graph rigidity theory to interface with a realistic relative localization system onboard lightweight Unmanned Aerial Vehicles (UAVs) is proposed in this paper. The proposed methodology enables reliable real-world deployment of UAVs in tight formation using real relative localization systems burdened by non-negligible sensory noise, which is typically not fully taken into account in FEC algorithms. The proposed solution is based on decomposition of the gradient descent-based FEC command into interpretable elements, and then modifying these individually based on the estimated distribution of sensory noise, such that the resulting action limits the probability of overshooting the desired formation. The behavior of the system has been analyzed and the practicality of the proposed solution has been compared to pure gradient-descent in real-world experiments where it presented significantly better performance in terms of oscillations, deviation from the desired state and convergence time.

翻译：本文提出一种技术，使基于图刚性理论的编队保持控制（FEC）能够与轻量级无人飞行器（UAV）上搭载的实际相对定位系统相接口。所提方法使用受非可忽略传感器噪声影响的实际相对定位系统，实现了无人机在紧密编队中的可靠实际部署，而此类噪声在FEC算法中通常未被充分纳入考量。该解决方案基于将梯度下降型FEC指令分解为可解释元素，然后根据传感器噪声的估计分布单独修改这些元素，使得最终动作能够限制偏离期望编队的概率。通过分析系统行为，并在实际实验中与纯梯度下降方法进行比较，所提方案在振荡幅度、对期望状态的偏离和收敛时间方面表现出显著更优的性能。