| 摘 要： |
In the last years, Unmanned Aerial Vehicles (UAVs) have been widely used for several types of missions, including aerial reconnaissance, search and rescue, and military operations. In first rescue missions, the ability to detect missing people after avalanche events in a short time is fundamental to increase the probability of saving them. The use of unmanned aerial vehicles in such scenarios can improve results with respect to multiple points of view: on the one hand, it can decrease the danger for rescuers, while on the other hand, it can speed up the search process. An effective solution can be the involvement of a formation of multiple drones to cover a greater research space and accelerate the process. However, an important challenge in deploying a formation of robots in emergency scenarios is the autonomy in terms of system scalability, since drones are usually teleoperated in a one-to-one ratio with operators, requiring a large crew of rescuers. In order to improve the situational awareness and distribute the communication burden, this paper deals with a decentralized Kalman filtering algorithm using sensor data from multiple drones to estimate a target position, besides UAVs state to support guidance and control algorithms. Such decentralized Kalman filtering algorithm combines the characteristics of Consensus on Information and Consensus on Measurement techniques. The proposed technique is preliminarily validated by means of numerical simulations on an example scenario.
近年来，无人飞行器（UAV）已广泛应用于多种任务，包括空中侦察、搜救以及军事行动。在首次救援任务中，能够在雪崩事件后短时间内发现失踪人员，是提高获救概率的关键。在此类场景中使用无人机，可以从多个方面提升搜救效果：一方面可降低救援人员的危险，另一方面能加快搜索进程。一个有效的解决方案是部署多架无人机组成的编队，以覆盖更广的搜索区域并加快处理速度。然而，在紧急情况下部署机器人编队面临的重要挑战在于系统可扩展性方面的自主性，因为无人机通常与操作员一对一地进行遥控，需要大量救援人员参与。为了提高态势感知能力并减轻通信负担，本文提出了一种基于多无人机传感器数据的分布式卡尔曼滤波算法，用于估计目标位置，并结合无人机状态以支持导航和控制算法。该分布式卡尔曼滤波算法融合了信息一致性与测量一致性技术的特点。所提出的算法通过一个示例场景的数值仿真进行了初步验证。 |
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