Deep Reinforcement Learning for Next-Best-View Planning in Agricultural Applications

Zeng, Xiangyu; Zaenker, Tobias; Bennewitz, Maren

doi:10.1109/icra46639.2022.9811800

Cited by 9 publications

(1 citation statement)

References 25 publications

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“…a uniformly sampled approach. Zeng et al [15] used reinforcement learning to map sweet pepper crops with a robotic arm and an in-hand camera in a greenhouse environment. The authors applied an octomap and ray casting to determine the next observation for the agent.…”

Section: Related Workmentioning

confidence: 99%

Viewpoint Push Planning for Mapping of Unknown Confined Spaces

Dengler¹,

Pan²,

Kalagaturu³

et al. 2023

Preprint

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Viewpoint planning is an important task in any application where objects or scenes need to be viewed from different angles to achieve sufficient coverage. The mapping of confined spaces such as shelves is an especially challenging task since objects occlude each other and the scene can only be observed from the front, thus with limited possible viewpoints. In this paper, we propose a deep reinforcement learning framework that generates promising views aiming at reducing the map entropy. Additionally, the pipeline extends standard viewpoint planning by predicting adequate minimally invasive push actions to uncover occluded objects and increase the visible space. Using a 2.5D occupancy height map as state representation that can be efficiently updated, our system decides whether to plan a new viewpoint or perform a push. To learn feasible pushes, we use a neural network to sample push candidates on the map and have human experts manually label them to indicate whether the sampled push is a good action to perform. As simulated and real-world experimental results with a robotic arm show, our system is able to significantly increase the mapped space compared to different baselines, while the executed push actions highly benefit the viewpoint planner with only minor changes to the object configuration.

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Section: Related Workmentioning

confidence: 99%