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首页> 外文期刊>Zeitschrift fur Angewandte Mathematik und Mechanik >Stochastic programming methods in adaptive optimal trajectory planning for robots
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Stochastic programming methods in adaptive optimal trajectory planning for robots

机译：机器人自适应最优轨迹规划中的随机规划方法

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In the optimal control of industrial, field, or service robots, the standard procedure is to determine first off-line a feedforward control and a reference trajectory, based on some selected nominal values of the unknown stochastic model parameters, and to correct then the inevitable and increasing deviation of the state or performance of the robot from the prescribed state or performance of the system by on-line measurement and control actions. Due to the stochastic variations of the model parameters, increasing measurement and correction actions are needed during the process. By optimal stochastic trajectory planning (OSTP), based on stochastic optimization methods, the available a priori and sample information about the robot and its working environment is incorporated into the control process. Consequently, more robust reference trajectories and feedforward controls are obtained which cause much less on-line control actions. In order to maintain a high quality of the guiding functions, the reference trajectory and the feedforward control can be updated at some later time points such that additional information about the control process is available. After the presentation of the Adaptive Optimal Stochastic Trajectory Planning (AOSTP) procedure based on stochastic optimization methods, several numerical techniques for the computation of robust reference trajectories and feedforward controls under real-time conditions are presented. [References: 49]
机译：在工业，现场或服务机器人的最佳控制中，标准程序是根据未知的随机模型参数的某些选定标称值，首先确定离线前馈控制和参考轨迹，然后进行纠正。通过在线测量和控制操作，使机器人的状态或性能与系统的规定状态或性能的偏差增加。由于模型参数的随机变化，在此过程中需要增加测量和校正操作。通过基于随机优化方法的最佳随机轨迹计划（OSTP），有关机器人及其工作环境的可用先验信息和样本信息将合并到控制过程中。因此，获得了更可靠的参考轨迹和前馈控制，从而导致更少的在线控制动作。为了保持高质量的引导功能，可以在以后的某个时间点更新参考轨迹和前馈控制，以便获得有关控制过程的其他信息。在介绍了基于随机优化方法的自适应最优随机轨迹规划（AOSTP）过程之后，提出了几种在实时条件下计算鲁棒参考轨迹和前馈控制的数值技术。 [参考：49]

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