三维环境中基于ESMF的多机器人协作观测方法

提出了一种基于扩展集员估计（ESMF）的多机器人协作观测方法，该方法将多机器人之间的观测数据融合过程嵌入到估计过程当中，从而减少了数据处理的过程，增强了算法的快速性。同时，这种方法在实现协作观测时只需要协作机器人传送观测信息而不是整个的估计信息，因此可以减轻多机器人系统的通信负担。除此之外，该方法在融合多机器人的观测数据过程中避免了多余的近似过程，增加了观测的准确性。最后，给出了三维环境下的仿真结果，验证了方法的可行性。

A new multiple unmanned aerial vehicles (UAVs) cooperative observation approach based on extended set-membership filter (ESMF) is proposed to track a moving target in 3D environments. The new method embeds the data fusion among multiple UAVs into estimation process, which decreases the data processing and improves the algorithm speed. Only observation information is transmitted by the cooperative UAV during cooperative observation, so the communication burden of the multi-UAV system is relatively light. Moreover, when fusing the observation result, the new method avoids approximating estimation result so as to improve the accuracy of the cooperative observation result. Finally, the simulations in 3D environments are conducted to verify the feasibility of the method.

机器人学国家重点实验室自主课题资助（RLZ200806）