OS33-2 Target Search Based on Scene Priors

Title:	OS33-2 Target Search Based on Scene Priors
Publication:	ICAROB2022
Volume:	27
Pages:	124-131
ISSN:	2188-7829
DOI:	10.5954/ICAROB.2022.OS33-2
Author(s):	Shengyang Lu, Lanjun Liang, Huailin Zhao, Fangbo Zhou, Feng yao
Publication Date:	January 20, 2022
Keywords:	Target search, Reinforcement Learning, Scene priors, visual navigation
Abstract:	Aiming at the problems of reinforcement learning algorithm in target search tasks, such as low accuracy and low fault tolerance, this article mainly introduces a method of reinforcement learning target search based on scene prior in simulation environment. This method mainly uses graph convolutional neural network to extract the current object relationship as the input of prior knowledge. Secondly, it uses the actor-critic algorithm to take the agent's vision, position and prior knowledge as input to decide the agent's next navigation. Finally, use path planning to navigate to the target point to find the target. Through experiments conducted in Habitat and compared with the previous algorithm, the experiment shows that this method is better than the previous algorithm in target search accuracy and navigation efficiency.
PDF File:	https://alife-robotics.co.jp/members2022/icarob/data/html/data/OS/OS33/OS33-2.pdf
Copyright:	© The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/

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