Bio-inspired Goal-directed Navigation Model Based on Multi-scale Spatial Representation

LI Weilong; WU Dewei; LU Hu; DU Jia; ZHOU Yang

doi:10.11999/JEIT160892

Volume 39 Issue 6

Jun. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(6): 1363-1370

LI Weilong, WU Dewei, LU Hu, DU Jia, ZHOU Yang. Bio-inspired Goal-directed Navigation Model Based on Multi-scale Spatial Representation[J]. Journal of Electronics & Information Technology, 2017, 39(6): 1363-1370. doi: 10.11999/JEIT160892

Citation:

LI Weilong, WU Dewei, LU Hu, DU Jia, ZHOU Yang. Bio-inspired Goal-directed Navigation Model Based on Multi-scale Spatial Representation[J]. Journal of Electronics & Information Technology, 2017, 39(6): 1363-1370. doi: 10.11999/JEIT160892

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Bio-inspired Goal-directed Navigation Model Based on Multi-scale Spatial Representation

doi: 10.11999/JEIT160892

1.
(Information and Navigation College, Air Force Engineering University, Xi'an 710077, China)
2.

Funds:

The National Natural Science Foundation of China (61273048, 61473308, 61603409)

Received Date: 2016-09-02
Rev Recd Date: 2017-01-22
Publish Date: 2017-06-19

Abstract

Abstract

In order to achieve spatial cognition and autonomous navigation, enlightened by the mechanism for biological navigation, a bio-inspired goal-directed navigation model based on a multi-scale spatial representation is proposed. First, a place cell map with different scales is constructed for encoding the space environment. Second, the firing rate of place cells in each layer is calculated by the Gaussian function as the input of Q-learning process. Third, the annealing strategy is used to choose a reasonable action. After training and learning, the robot can succeed to plan an optimal route from the starting point to the goal point. Simulation results show that, the proposed method is feasible for goal-directed navigation. Compared with the spatial cognitive model of single scale place cells, the proposed method not only meets the multi-scale spatial representation nature of place cells in hippocampus, but also has a faster learning speed. Additionally, it has good performance on completing the goal- oriented navigation in the presence of obstacles, and can adapt to the change of obstacles in the environment.
- Brain-based navigation,
- Spatial cognition,
- Place cells,
- Multi-scale representation,
- Q-learning

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References(27)

References

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