No abstract

This paper proposes a throwing manipulation strategy for a robot with one revolute joint. The throwing manipulation enables the robot not only to manipulate the object to outside of the movable range of the robot, but also to control the position of the object arbitrarily in the vertical plane even though the robot has only one degree of freedom. In the throwing manipulation, the robot motion is dynamic and quick, and the contact state between the robot and the object changes. These make it difficult to obtain the exact model and solve its inverse problem. In addition, since the throwing manipulation requires more powerful actuators than the static manipulation, we should set the control input by taking consideration of the performance limits of the actuators. The present paper proposes the control strategy based on the iteration optimization learning to overcome the above problems and verifies its effectiveness experimentally.

No abstract

This paper proposes the control strategy for throwing as one of the dynamic manipulation. In dynamic manipulation, the robot's motion is dynamic and quick and there is no constant contact state. The dynamic manipulation has strong nonlinearity between control input and output, which often makes the manipulation unstable. And modeling errors affect the success of its manipulation task seriously. The dynamic manipulation requires more powerful actuator than the static manipulation. We propose the control method using the learning control that can consider the modeling error problem, the performance limits of the actuator and the stability of the learning control.

The present paper proposes the learning control method for the throwing manipulation which can control not only the position but also the orientation of the polygonal object more accurately and robustly by low-degree-of-freedom robotic arm. We show experimentally the validity of the proposed control method with the one-degree-freedom robotic arm. We also demonstrate the usefulness of the throwing manipulation by applying it to sorting task and assembly task on experiments.