Self-modeling Tracking Control of Crawler Fire Fighting Robot Based on Causal Network | |
Chang WK(常文凯); Li P(李朋); Yang CY(杨彩云); Lu T(鲁涛); Cai YH(蔡莹皓); Wang S(王硕) | |
2019 | |
会议日期 | 2019年11月4日至8日 |
会议地点 | 中国澳门 |
英文摘要 | 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. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/40633] |
专题 | 智能机器人系统研究 |
通讯作者 | Chang WK(常文凯) |
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]. 见:. 中国澳门. 2019年11月4日至8日. |
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