Partition and planning: A human-like motion decision for UAV in trap environment

This paper presents a human-like motion decision-making method for unmanned aerial vehicles (UAVs) navigating in trap environments. We proposed a space partitioning method based on sampling and consistency control to conduct a preliminary analysis of the indoor environment based on architectural blueprints. This method reduces the dimensionality of the path planning problem, thereby enhancing the efficiency. Then, we designed a target-switching logic for the dynamic window approach. This improvement endows the UAV with the capability of both real-time obstacle avoidance and global navigation, enhancing the efficiency of the UAV in flying to task spots indoors. Additionally, by applying human-like methods of batch distance perception and obstacle perception to this scheme, we have further enhanced the robustness and efficiency of path decisions. Finally, considering the scenario of high-rise fire rescue, we conducted simulation verification. It demonstrates that our scheme enhances the efficiency and robustness of path planning.