The method described here identifies plants by using a machine vision technique. This method achieves effective image detection independent of surrounding conditions, dimensionless image detection in each growth stage, and determination of the critical factor for discriminating individual plants. These are the fundamental factors for successful automatic thinning, cropping, weeding, and harvesting using intelligent agricultural robots. Color, aspect ratio, size, radius permutation in leaf profiles, complexity, and curvature are used to classify each plant. Effective discrimination is obtained by using a quasi-sensor fusion combined with a total occurrence range for decision making.

When we look at a comfortable scene or feel relaxed, our brain waves generally exhibit wave signals in the frequency band of approximately 8 to 13 Hz. These waves particularly exhibit a 1/f fluctuation in which the corresponding power is inversely proportional to frequency f. 1/f -wave signals obtained from test subjects listening to relaxing music was inputted to a robot manipulator. To evaluate the resulting motions, test subjects were asked to complete questionnaires while they watched two types of manipulator motions: a 1/f motion and a white-noise-like motion. The results indicated that 90% of the subjects felt comfortable while watching the 1/f manipulator motions.

Robust sensing and control in fine positioning is a key technology in the presence of various disturbances. For example, the position accuracy of high performance motion control systems is adversely affected by vibrations due to compliance and nonlinear effects such as friction. This paper focuses on a robust friction sensingmethodology based on the sensor fusion via the neural network from AE (acoustic emission) sensors and the feedforward control for the compensation of friction. This compensation is found to be useful for positioning control when frictions applied to the system are adequately and robustly estimated. Encouraging transient response and steady-state control performance were observed in the experimental results of positioning control of a one-dimensional transmission mechanism. The proposed friction sensing and feedforward control can be applied without modifications for nanoscale positioning.