林立民, 刘成菊, 马璐, 王德明, 陈启军. 基于STFT的仿人机器人手臂轨迹生成方法[J]. 机器人, 2019, 41(5): 591-600. DOI: 10.13973/j.cnki.robot.180784
引用本文: 林立民, 刘成菊, 马璐, 王德明, 陈启军. 基于STFT的仿人机器人手臂轨迹生成方法[J]. 机器人, 2019, 41(5): 591-600. DOI: 10.13973/j.cnki.robot.180784
LIN Limin, LIU Chengju, MA Lu, WANG Deming, CHEN Qijun. Arm Trajectory Generation for Humanoid Robot Based on STFT[J]. ROBOT, 2019, 41(5): 591-600. DOI: 10.13973/j.cnki.robot.180784
Citation: LIN Limin, LIU Chengju, MA Lu, WANG Deming, CHEN Qijun. Arm Trajectory Generation for Humanoid Robot Based on STFT[J]. ROBOT, 2019, 41(5): 591-600. DOI: 10.13973/j.cnki.robot.180784

基于STFT的仿人机器人手臂轨迹生成方法

Arm Trajectory Generation for Humanoid Robot Based on STFT

  • 摘要: 为了提高机器人轨迹生成算法的泛化性,提出了一种基于时间-空间特征模板(STFT,spatio-temporal feature template)的机器人手臂轨迹生成方法.首先,针对机器人示教轨迹往往存在的时间长短和幅度差异较大的问题,采用广义的典型时间规整(generalized canonical time warping,GCTW)方法来统一时间和幅度的变化,从而获取机器人示教轨迹的共同特征模板.其次,基于STFT,引入机器人轨迹生成的平滑性约束、任务约束等因素,设定轨迹生成的目标函数并优化.最终在NAO仿人机器人平台上验证了所提出的轨迹生成算法,基于STFT生成机器人弧形轨迹并完成数字书写.实验结果表明,本文提出的基于STFT的轨迹生成策略可以生成满足期望条件的机器人轨迹,并具有一定的泛化性.

     

    Abstract: To improve the generalization performance of robot trajectory generation algorithms, a manipulator trajectory generation strategy based on spatio-temporal feature template (STFT) is proposed. Firstly, to solve the problem that the manipulation time and amplitude of the demonstration trajectories are different greatly, the generalized canonical time warping (GCTW) method is adopted to unify the variation of the time and amplitude. In this way, the common feature template for demonstration trajectories is obtained. Secondly, based on STFT, smoothness constraints and task constraints of robot trajectory generation are introduced into the objective function for trajectory generation, and the function is then optimized. Finally, the trajectory generation strategy is verified on NAO robot platform. The curved trajectories and digital figure trajectories are generated based on STFT. The experimental results show that the proposed STFT-based trajectory generation strategy can generate robot trajectories satisfying the desired conditions and demonstrating some generalization performance.

     

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