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SPSD: Semantics and Deep Reinforcement Learning Based Motion Planning for Supermarket Robot

Jialun CAI, Weibo HUANG, Yingxuan YOU, Zhan CHEN, Bin REN, Hong LIU

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Summary :

Robot motion planning is an important part of the unmanned supermarket. The challenges of motion planning in supermarkets lie in the diversity of the supermarket environment, the complexity of obstacle movement, the vastness of the search space. This paper proposes an adaptive Search and Path planning method based on the Semantic information and Deep reinforcement learning (SPSD), which effectively improves the autonomous decision-making ability of supermarket robots. Firstly, based on the backbone of deep reinforcement learning (DRL), supermarket robots process real-time information from multi-modality sensors to realize high-speed and collision-free motion planning. Meanwhile, in order to solve the problem caused by the uncertainty of the reward in the deep reinforcement learning, common spatial semantic relationships between landmarks and target objects are exploited to define reward function. Finally, dynamics randomization is introduced to improve the generalization performance of the algorithm in the training. The experimental results show that the SPSD algorithm is excellent in the three indicators of generalization performance, training time and path planning length. Compared with other methods, the training time of SPSD is reduced by 27.42% at most, the path planning length is reduced by 21.08% at most, and the trained network of SPSD can be applied to unfamiliar scenes safely and efficiently. The results are motivating enough to consider the application of the proposed method in practical scenes. We have uploaded the video of the results of the experiment to https://www.youtube.com/watch?v=h1wLpm42NZk.

Publication
IEICE TRANSACTIONS on Information Vol.E106-D No.5 pp.765-772
Publication Date
2023/05/01
Publicized
2022/09/15
Online ISSN
1745-1361
DOI
10.1587/transinf.2022DLP0057
Type of Manuscript
Special Section PAPER (Special Section on Deep Learning Technologies: Architecture, Optimization, Techniques, and Applications)
Category
Positioning and Navigation

Authors

Jialun CAI
  Peking University
Weibo HUANG
  Peking University
Yingxuan YOU
  Peking University
Zhan CHEN
  Peking University
Bin REN
  University of Trento
Hong LIU
  Peking University

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