Cooperative Unmanned aerial vehicles (UAVs) have been widely employed as effective tools for various information-gathering tasks in complex environments with increased efficiency and resiliency. The mission-level guidance and control of UAVs often depend on an accurate map and inaccurate maps may lead to the UAV's inappropriate accommodation to the environment. In this paper, we propose a new framework to generate and utilize semantic map information, which we defined as risk factors for cooperative UAVs. First, we generate a high-precision panorama as a global map by mosaicking a bird's-eye atlas. Afterward, we build a semantic map based on a neural network. Finally, we utilize the semantic information-enhanced map to guide the path-planning functions. Experiments show that our proposed method can improve the success rate of planning in the outdoor scene, and demonstrate its efficiency.
Semantic Information Based Path Planning for Cooperative UAV Systems
2022-12-02
6550718 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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