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About

Tobias Zaenker is a robotics researcher specializing in autonomous agricultural systems, active perception, and next-best-view (NBV) planning. His work sits at the intersection of computer vision, deep learning, and robotic autonomy, with a particular focus on solving the persistent challenge of occlusion in complex plant environments such as glasshouse pepper crops. Zaenker's most impactful contributions center on intelligent viewpoint planning for fruit detection, mapping, and harvesting. His NBV-SC framework (2023, 30 citations) introduced shape completion as a computationally efficient alternative to costly ray casting, enabling robots to better reconstruct occluded fruits. Complementing this, his deep reinforcement learning approach to NBV planning (2022, 25 citations) demonstrated how autonomous agents can learn exploration strategies for 3D agricultural environments without hand-crafted heuristics. His fruit mapping work (2022, 24 citations) further advanced volumetric estimation of partially visible fruits, critical for yield prediction and robotic harvesting. Beyond perception, Zaenker contributed to the PATHoBot platform, a practical glasshouse phenotyping robot, and semantic mapping frameworks for service robotics. His more recent work explores multi-robot UAV-UGV collaboration for leaf-level crop inspection, reflecting a growing scope across agricultural automation. With over 90 cumulative citations, Zaenker is an emerging voice in precision agriculture robotics.