Precision Pouring into Unknown Containers by Service Robots Service robots have developed considerably in recent years. This study aims to propose two approaches for controlling the motion of a service robot as it pours liquid precisely from an unknown container into another unknown container without the need of any external tools. To realize this task, we must resolve two subtasks, which are determining the poured volume and controlling the pouring action. Our first proposal concentrates on the target container. The poured volume is calculated using a model of target container and the height of liquid in the target container. The action is controlled using a proportionalderivative controller, which considers the angular speed of the pouring container as a process variable and the poured volume as a control variable. Our second method concentrates on the pouring container. The poured volume is calculated using the relation between the angle of the pouring container and poured volume. The action is controlled with a simple proportional controller that takes the angular speed of the pouring container as a process variable and target angle as a control variable. A point cloud is used to model the two containers. These two methods were implemented in a dualarm robot system for testing, and the results show that both methods are effective for controlling precise pouring tasks.

Haptic Perception of Liquids Enclosed in Containers Service robots will require several important manipulation skills, including the ability to accurately measure and pour liquids. Prior work on robotic liquid pouring has primarily focused on visual techniques for sensing liquids, but these techniques fall short when liquids are obscured by opaque or closed containers. This paper proposes a complementary method for liquid perception via haptic sensing. The robot moves a container through a series of tilting motions and observes the wrenches induced at the manipulatorxe2x80x99s wrist by the liquidxe2x80x99s shifting center of mass. That data is then analyzed with a physicsbased model to estimate the liquidxe2x80x99s mass and volume. In experiments, this method achieves error margins of less than lg and 2mL for an unknown liquid in a 600mL cylindrical container. The model can also predict the viscosity of fluids, which can be used for classifying water, oil, and honey with an accuracy of 98%. The estimated volume is used to precisely pour 100mL of water with less than 4% average error.