Adversarial Reinforcement Learning Based Robustification of Highlighted Map for Mobile Robot Localization

被引：0
作者：
Yoshimura, Ryota [1 ,2 ]
Maruta, Ichiro [2 ]
Fujimoto, Kenji [2 ]
Sato, Ken [3 ]
Kobayashi, Yusuke [3 ]
机构：
[1] Tokyo Metropolitan Ind Technol Res Inst, Reg Technol Support Div, Tokyo, Japan
[2] Kyoto Univ, Dept Aeronaut & Astronaut, Kyoto, Japan
[3] Tokyo Metropolitan Ind Technol Res Inst, Digitalizat Promot Sect, Tokyo, Japan
来源：
2021 60TH ANNUAL CONFERENCE OF THE SOCIETY OF INSTRUMENT AND CONTROL ENGINEERS OF JAPAN (SICE) | 2021年
关键词：
Adversarial reinforcement learning; highlighted map; mobile robots; Monte Carlo localization; particle filters;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A highlighted map, where objects with unique shapes are highlighted, has been studied for mobile robot localization. This map improves the localization accuracy without adding any sensors or online computations for localization. In addition, it can be used in various particle-filter-based localization algorithms. For generating a highlighted map, reinforcement learning has been used. Since this method generates the highlighted map by utilizing a limited number of the actual sensor measurement data, the generated map is vulnerable to unexpected sensor measurement noise. In this paper, the robustification method of a highlighted map is proposed. Our proposed method introduces a virtual obstacle that causes measurement noise, and learns both the worst-case obstacle behavior and the optimal highlighted map simultaneously based on adversarial reinforcement learning. We perform a numerical simulation to verify the robustness of the map.
引用
收藏
页码：599 / 605
页数：7
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