论文标题
在小容器之间移动机器人操纵器的分层控制策略
Hierarchical Control Strategy for Moving A Robot Manipulator Between Small Containers
论文作者
论文摘要
在本文中，我们研究了模型预测控制器（MPC）在高度不确定的环境中进行对象操作的任务（例如，从一系列密集的箱子中挑选对象）。作为实时感知驱动的反馈控制器，MPC对在这种环境中的不确定性非常强大。但是，我们的实验表明，由于其近视性质，MPC无法控制机器人在严重遮挡的环境中完成一系列动作。添加一项高级政策将受益，该策略可自适应地调整MPC的优化问题。
In this paper, we study the implementation of a model predictive controller (MPC) for the task of object manipulation in a highly uncertain environment (e.g., picking objects from a semi-flexible array of densely packed bins). As a real-time perception-driven feedback controller, MPC is robust to the uncertainties in this environment. However, our experiment shows MPC cannot control a robot to complete a sequence of motions in a heavily occluded environment due to its myopic nature. It will benefit from adding a high-level policy that adaptively adjusts the optimization problem for MPC.
