Baxter研究机器人使用主动参数识别的动态任务执行
Dynamic Task Execution using Active Parameter Identification with the Baxter Research Robot
摘要：
本文介绍了实验结果的实时参数估计的系统模型和随后的轨迹优化的动态任务，使用Baxter研究机器人从Rethink机器人。一个积极的估计最大限度地提高费舍尔信息被用于实时与闭环，非线性控制技术，称为顺序动作控制。Baxter的任务是估计连接到从夹具悬挂的负载的字符串的长度，其中称重传感器为估计器提供单一的反馈源。在主动估计之后，使用trep软件包生成轨迹，该软件包控制Baxter将悬挂负载动态摆动到箱子中。几个试验不同的初始估计表明，估计需要获得足够的开环轨迹来完成规定的任务。附带的视频中还显示了使用和不使用主动估计的一次试验的结果。
英文摘要：
This paper presents experimental results from real-time parameter estimation of a system model and subsequent trajectory optimization for a dynamic task using the Baxter Research Robot from Rethink Robotics. An active estimator maximizing Fisher information is used in real-time with a closed-loop, non-linear control technique known as Sequential Action Control. Baxter is tasked with estimating the length of a string connected to a load suspended from the gripper with a load cell providing the single source of feedback to the estimator. Following the active estimation, a trajectory is generated using the trep software package that controls Baxter to dynamically swing a suspended load into a box. Several trials are presented with varying initial estimates showing that estimation is required to obtain adequate open-loop trajectories to complete the prescribed task. The result of one trial with and without the active estimation is also shown in the accompanying video.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
