雑然環境におけるMAVを用いた意味的ターゲット探索と探査
STEM: Semantic Target Search and Exploration using MAVs in Cluttered Environments
この論文は、MAV（小型無人航空機）が雑然とした3D環境でターゲットを効率的に見つけるための、意味情報を活用した視点プランナーを提案しています。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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5. 議論はある？
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著者: Nikhil Sethi, Max Lodel, Laura Ferranti, Robert Babuška, Javier Alonso-Mora
分類: cs.RO
原文アブストラクト
Autonomous target search is crucial for deploying Micro Aerial Vehicles (MAVs) in emergency response and rescue missions. Existing approaches either focus on 2D semantic navigation in structured environments -- which is less effective in complex 3D settings, or on robotic exploration in cluttered spaces -- which often lacks the semantic reasoning needed for efficient target search. This paper overcomes these limitations by proposing a novel framework that utilizes a semantically-guided viewpoint planner to minimize target search and exploration time in unstructured 3D environments using an MAV. Specifically, we develop a combinatorial planner that generates efficient semantic exploration plans by prioritizing viewpoints that likely lead to the target. To guide the planner towards the target, an active perception pipeline is developed that propagates semantic priorities of observed objects into neighboring frontier voxels for computing semantic information gains of frontier viewpoints. In addition, we demonstrate how LLM-based similarity scores can be leveraged as semantic priority input to our pipeline. Evaluations in two distinct simulation environments show that the proposed method consistently outperforms baselines by quickly finding the target while maintaining reasonable exploration times. Real-world experiments with an MAV further demonstrate the method's ability to handle practical constraints like limited battery life, small sensor range, and semantic uncertainty.
