情報利得に基づくUAV群による農地モニタリングとマッピング
Monitoring and mapping of crop fields with UAV swarms based on information gain
複数UAVの群ロボティクスで、期待情報利得に基づき観測すべき領域を適応的に選び、雑草などの不均一な分布を効率的にマッピングする手法を提案した。
著者: Carlos Carbone, Dario Albani, Federico Magistri, Dimitri Ognibene, Cyrill Stachniss, Gert Kootstra, Daniele Nardi, Vito Trianni
分類: cs.RO
原文アブストラクト
Monitoring crop fields to map features like weeds can be efficiently performed with unmanned aerial vehicles (UAVs) that can cover large areas in a short time due to their privileged perspective and motion speed. However, the need for high-resolution images for precise classification of features (e.g., detecting even the smallest weeds in the field) contrasts with the limited payload and ight time of current UAVs. Thus, it requires several flights to cover a large field uniformly. However, the assumption that the whole field must be observed with the same precision is unnecessary when features are heterogeneously distributed, like weeds appearing in patches over the field. In this case, an adaptive approach that focuses only on relevant areas can perform better, especially when multiple UAVs are employed simultaneously. Leveraging on a swarm-robotics approach, we propose a monitoring and mapping strategy that adaptively chooses the target areas based on the expected information gain, which measures the potential for uncertainty reduction due to further observations. The proposed strategy scales well with group size and leads to smaller mapping errors than optimal pre-planned monitoring approaches.
