Knowledge Commons of Institute of Automation,CAS
Self-modeling Tracking Control of Crawler Fire Fighting Robot Based on Causal Network | |
Chang WK(常文凯)![]() ![]() ![]() ![]() ![]() ![]() | |
2019 | |
会议名称 | International Conference on Intelligent Robots & Systems |
会议日期 | 2019年11月4日至8日 |
会议地点 | 中国澳门 |
摘要 | In this paper, a self-modeling method based on causal network is proposed for the tracking control of Crawler Fire Fighting Robot (CFFR). The method mainly consists of two parts, one is a motion model, based on data driving, learning to establish the correspondence between control signal sequence and vehicle motion, estimating the motion state of the next moment from historical data, eliminating complex CFFR modeling. The other is the tracking network. Based on the simulation data of above-mentioned motion model, the relationship between the target trajectory and the current control command is learned, which simplifies the design and cumbersome tuning of the complex controller. The effectiveness of the proposed method is verified in both simulated and real-world environments. Qualitative and quantitative experimental results verify the accuracy of the tracking. |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 智能机器人 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/40633 |
专题 | 多模态人工智能系统全国重点实验室_智能机器人系统研究 复杂系统管理与控制国家重点实验室 中国科学院分子影像重点实验室 |
通讯作者 | Chang WK(常文凯) |
作者单位 | 中国科学院自动化研究所 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Chang WK,Li P,Yang CY,et al. Self-modeling Tracking Control of Crawler Fire Fighting Robot Based on Causal Network[C],2019. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Self-modeling Tracki(5170KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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