期刊
IEEE ROBOTICS AND AUTOMATION LETTERS
卷 4, 期 4, 页码 3113-3120出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2019.2924125
关键词
Agricultural automation; multi-robot systems; computer vision for automation
类别
资金
- Natural Science Foundation of China [61725204, 61521002, 61628211]
- Royal Society-Newton Advanced Fellowship [NA150431]
- MOE-Key Laboratory of Pervasive Computing
- CUHK Direct Research Grant [4055094]
- Science and Technology Department of Jiangsu Province, China
Manual plant phenotyping is slow, error prone, and labor intensive. In this letter, we present an automated robotic system for fast, precise, and noninvasive measurements using a new deep-learning-based next-best view planning pipeline. Specifically, we first use a deep neural network to estimate a set of candidate voxels for the next scanning. Next, we cast rays from these voxels to determine the optimal viewpoints. We empirically evaluate our method in simulations and real-world robotic experiments with up to three robotic arms to demonstrate its efficiency and effectiveness. One advantage of our new pipeline is that it can he easily extended to a multi-robot system where multiple robots move simultaneously according to the planned motions. Our system significantly outperforms the single robot in flexibility and planning time. High-throughput phenotyping can be made practically.
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