Trajectory Planning Framework for Drones Under Sensor Occlusion in Unknown Indoor Environments
Year: 2026
Type: article
Abstract: Autonomous drone navigation relies on onboard sensors to perceive obstacle information in real time. However, indoor environments contain abundant wall structures that occlude the sensor’s field of view, rendering obstacle information within occluded regions undetectable to the drone. Existing trajectory planning algorithms fail to adequately account for the safety risks introduced by sensor occlusion. To address this limitation, this article proposes a novel trajectory planning framework to enhance drone flight performance in indoor environments. Specifically, a 3D occupancy grid map is first constructed from sensor data, and an initial trajectory is generated from the current position to the goal. A sensor occlusion detection algorithm then classifies the current scene into three categories: occlusion-free, partial occlusion, and full occlusion. For occlusion-free scenarios, the initial trajectory is directly forwarded to the controller. For partial and full occlusion cases, an occlusion-aware trajectory replanning algorithm generates multiple candidate trajectories in unknown regions. These candidates are evaluated by a scoring function comprising three metrics: safety, efficiency, and smoothness. Upon detection of a collision between the currently executing initial trajectory and an obstacle, the active trajectory is immediately switched to the highest-scoring candidate trajectory, thereby ensuring both flight safety and navigation efficiency of the drone. Extensive experiments are conducted across multiple occlusion scene configurations to validate the performance of the proposed method. Experimental results demonstrate that the proposed method is capable of providing safe and efficient trajectories for drones under both partial occlusion and full occlusion conditions.
Source: Drones
Authors: Jingsen Zhang, Biao Hou, Xing Yuan
Institution: Xidian University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7166707858
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