Design of Sensor Referenced Action Planning and Control for Robotic Systems in Workstations

该奖项支持在工作站设置中对机器人系统的传感器参考行动计划和控制问题进行调查，叠加基于模型的计划和控制，这已经成为机器人系统控制的规范。该范式是对任务空间中的已知事物进行建模，对未知事物进行感知，并据此实施规划和控制。任务将根据任务空间中感知到的事件来制定，在许多情况下，这些事件由来自不同传感器的融合信息的模式表示。最终，这种机器智能方法将为自动化提供高水平的决策能力，促进系统集成，并为机器人工作站提供更多用户友好的规划和决策/控制系统。要做到这一点，必须解决一些问题。首先，研究将解决一种新型的运动描述和机器人手臂的运动学和动态动作能力的有效算法公式。运动描述使用相空间方法（速度vs位置）而不是通常的状态空间方法（位置vs时间）。初步结果表明，相空间的运动描述与规划与基于时间的传感器参考控制更兼容。行动能力的算法公式使用了已知的武器和任务环境参数，以便为基于传感器的智能行动规划和控制提供基于模型的基础。

This award supports investigation of the problem of sensor- referenced action planning and control for robotic systems in a workstation setting, superimposed on model-based planning and control which has been the norm for robotic system control. The paradigm is to model the known and sense the unknown in the task space and implement the planning and control accordingly. Tasks will be formulated in terms of events sensed in the task space, which in many cases are represented by a pattern of fused information from different sensors. Ultimately, this machine intelligence approach will provide high-level decision making capability for automation, promote system integration, and lead to more user-friendly planning and decision/control systems for robotic workstations. To accomplish this, a number of problems must be addressed. Initially, the research will address a new type of motion description and efficient algorithmic formulation of kinematic and dynamic action capabilities of robot arms. The motion description uses a phase-space approach (velocity vs. position) instead of the usual state-space (position vs. time) approach. Preliminary results indicate that motion description and planning in phase-space is more compatible with sensor referenced control that is time-based planning and control. The algorithmic formulation of action capabilities used the known parameters, both of the arms and the task environment in order to provide a model-based foundation for sensor-referenced intelligent action planning and control.