This study proposes a harvesting robot capable of plucking tea leaves. In order to harvest high-quality tea, the robot is required to pluck the petiole of the leaf without cutting it using blades. To pluck the leaves, it is necessary to reproduce a complicated human hand motion of pulling while rotating. Furthermore, the rotation and pulling of the hand, and the time taken, vary greatly depending on conditions that include the maturity of the leaves, thickness of the petioles, and thickness and length of the branches. Therefore, it is necessary to determine the amount of translational and rotational movements, and the length of time of the motion, according to each situation. In this study, the complicated motion is reproduced by learning from demonstration. The condition is judged in terms of the stiffness of the branches, which is defined as the force received from the branches per unit length when the gripped leaf is slightly pulled up. Combining the learned motions probabilistically at a ratio determined by the branch stiffness, the appropriate motion is generated, even for situations where no motion is taught. We compared the motions generated by the proposed method with the motions taught by humans, and verified the effectiveness of the proposed method. It was confirmed by experiment that the proposed method can harvest high-quality tea.

Falling is a common problem in the growing elderly population, and fall-risk assessment systems are needed for community-based fall prevention programs. In particular, the timed up and go test (TUG) is the clinical test most often used to evaluate elderly individual ambulatory ability in many clinical institutions or local communities. This study presents an improved leg tracking method using a laser range sensor (LRS) for a gait measurement system to evaluate the motor function in walk tests, such as the TUG. The system tracks both legs and measures the trajectory of both legs. However, both legs might be close to each other, and one leg might be hidden from the sensor. This is especially the case during the turning motion in the TUG, where the time that a leg is hidden from the LRS is longer than that during straight walking and the moving direction rapidly changes. These situations are likely to lead to false tracking and deteriorate the measurement accuracy of the leg positions. To solve these problems, a novel data association considering gait phase and a Catmull–Rom spline-based interpolation during the occlusion are proposed. From the experimental results with young people, we confirm that the proposed methods can reduce the chances of false tracking. In addition, we verify the measurement accuracy of the leg trajectory compared to a three-dimensional motion analysis system (VICON).