A Spatial Location Representation Method Incorporating Boundary Information
In response to problems concerning the low autonomous localization accuracy of mobile robots in unknown environments and large cumulative errors due to long time running, a spatial location representation method incorporating boundary information (SLRB) is proposed, inspired by the mammalian spatial...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/13/7929 |
| _version_ | 1797592098782838784 |
|---|---|
| author | Hui Jiang Yukun Zhang |
| author_facet | Hui Jiang Yukun Zhang |
| author_sort | Hui Jiang |
| collection | DOAJ |
| description | In response to problems concerning the low autonomous localization accuracy of mobile robots in unknown environments and large cumulative errors due to long time running, a spatial location representation method incorporating boundary information (SLRB) is proposed, inspired by the mammalian spatial cognitive mechanism. In modeling the firing characteristics of boundary cells to environmental boundary information, we construct vector relationships between the mobile robot and environmental boundaries with direction-aware information and distance-aware information. The self-motion information (direction and velocity) is used as the input to the lateral anti-Hebbian network (LAHN) to generate grid cells. In addition, the boundary cell response values are used to update the grid cell distribution law and to suppress the error response of the place cells, thus reducing the localization error of the mobile robot. Meanwhile, when the mobile robot reaches the boundary cell excitation zone, the activated boundary cells are used to correct the accumulated errors that occur due to long running times, which thus improves the localization accuracy of the system. The main contributions of this paper are as follows: 1. We propose a novel method for constructing boundary cell models. 2. An approach is presented that maps the response values of boundary cells to the input layer of LAHN (Location-Adaptive Hierarchical Network), where grid cells are generated through LAHN learning rules, and the distribution pattern of grid cells is adjusted using the response values of boundary cells. 3. We correct the cumulative error caused by long-term operation of place cells through the activation of boundary cells, ensuring that only one place cell responds to the current location at each individual moment, thereby improving the positioning accuracy of the system. |
| first_indexed | 2024-03-11T01:46:36Z |
| format | Article |
| id | doaj.art-e6979856dccf440795b6750127ad778e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:46:36Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e6979856dccf440795b6750127ad778e2023-11-18T16:13:28ZengMDPI AGApplied Sciences2076-34172023-07-011313792910.3390/app13137929A Spatial Location Representation Method Incorporating Boundary InformationHui Jiang0Yukun Zhang1School of Intelligent Manufacturing, Huainan Union University, Huainan 232038, ChinaSchool of Electrical Engineering, Anhui Polytechnic University, Wuhu 241060, ChinaIn response to problems concerning the low autonomous localization accuracy of mobile robots in unknown environments and large cumulative errors due to long time running, a spatial location representation method incorporating boundary information (SLRB) is proposed, inspired by the mammalian spatial cognitive mechanism. In modeling the firing characteristics of boundary cells to environmental boundary information, we construct vector relationships between the mobile robot and environmental boundaries with direction-aware information and distance-aware information. The self-motion information (direction and velocity) is used as the input to the lateral anti-Hebbian network (LAHN) to generate grid cells. In addition, the boundary cell response values are used to update the grid cell distribution law and to suppress the error response of the place cells, thus reducing the localization error of the mobile robot. Meanwhile, when the mobile robot reaches the boundary cell excitation zone, the activated boundary cells are used to correct the accumulated errors that occur due to long running times, which thus improves the localization accuracy of the system. The main contributions of this paper are as follows: 1. We propose a novel method for constructing boundary cell models. 2. An approach is presented that maps the response values of boundary cells to the input layer of LAHN (Location-Adaptive Hierarchical Network), where grid cells are generated through LAHN learning rules, and the distribution pattern of grid cells is adjusted using the response values of boundary cells. 3. We correct the cumulative error caused by long-term operation of place cells through the activation of boundary cells, ensuring that only one place cell responds to the current location at each individual moment, thereby improving the positioning accuracy of the system.https://www.mdpi.com/2076-3417/13/13/7929boundary cellsgrid cellsplace cellsenvironmental characterizationbrain- inspired computing |
| spellingShingle | Hui Jiang Yukun Zhang A Spatial Location Representation Method Incorporating Boundary Information Applied Sciences boundary cells grid cells place cells environmental characterization brain- inspired computing |
| title | A Spatial Location Representation Method Incorporating Boundary Information |
| title_full | A Spatial Location Representation Method Incorporating Boundary Information |
| title_fullStr | A Spatial Location Representation Method Incorporating Boundary Information |
| title_full_unstemmed | A Spatial Location Representation Method Incorporating Boundary Information |
| title_short | A Spatial Location Representation Method Incorporating Boundary Information |
| title_sort | spatial location representation method incorporating boundary information |
| topic | boundary cells grid cells place cells environmental characterization brain- inspired computing |
| url | https://www.mdpi.com/2076-3417/13/13/7929 |
| work_keys_str_mv | AT huijiang aspatiallocationrepresentationmethodincorporatingboundaryinformation AT yukunzhang aspatiallocationrepresentationmethodincorporatingboundaryinformation AT huijiang spatiallocationrepresentationmethodincorporatingboundaryinformation AT yukunzhang spatiallocationrepresentationmethodincorporatingboundaryinformation |