A convenient 3D measurement using amateur digital cameras is enormously expected in various fields with appearance of the low coat and high-resolution amateur digital cameras. In these circumstances, software for digital photogrammetry “3DiVision” was designed to perform convenient 3D measurement using amateur digital cameras. However, there are still problems for efficient digital photogrammetry. These problems include distance measurement for absolute orientation and interior orientation which should be performed beforehand, and these restrictions should be removed for ideal convenient photogrammetry using amateur digital cameras. With this objective, Image Based Integrated Measurement (IBIM) System which consists of mirrors, amateur digital camera, and laser range finder was developed by the authors. The most remarkable point of the system is its ability to calculate both of exterior and interior orientation parameters without scale distance or Ground Control Points which have exact 3D coordinates in object field. This paper describes a camera calibration method for the IBIM System by using the distances from center of the digital camera to object field and evaluation of this camera calibration method.
With advance of an aging society, the persons who are physically handicapped have their respective needs about mobility assist with their living conditions. Moreover, operating an electric wheelchair indoors in confined spaces requires considerable skill. This paper presents an obstacle avoiding support system for an electric wheelchair, using reinforcement learning. The obstacle avoidance is semi-automatically supported by the Minimum Vector Field Histogram (MVFH) method. The MVFH modifies the user manipulation and assists the obstacle avoidance. In the proposed scheme, the modification rate is adjusted by the reinforcement learning according to the environment and the user condition. The newly proposed scheme is numerically evaluated on a simulation example.
This paper proposes a hierarchical architecture for rhythmic movement generation, which suits to a juggling-like task involving sensory-motor coordination. The approach has a bidirectional weak coupling to the environment and it can adapt a robot to a change in the environment owing to the interaction between the robot and the environment at the ball contact. The proposed architecture consists of an active-control mechanism and two passive-control mechanisms which include open-loop stability of the mechano-physical system and entrainment between mechano-physical and neuro-like pattern generating systems. The synergy of these different mechanisms leads to the emergence of dynamic temporal pattern and enables the robot to perform the stable rhythmic movement of the successful task.
Various actual systems include time delays due to measurement and/or computational delays, and transmission and transport lags. In this paper, the authors propose a state predictor for a certain class of multivariable systems including multiple output delays. The predictor consists of full-order observers, each estimates a past state from a delayed output, and finite interval integrators, which compensate the effect of the delays using state transition equations. The error of the state prediction converges to zero at the arbitrary rate adjusted by choosing a finite number of poles of the full-order observers. The output matrix used for observer design is not affected by the delays, whereas that of a conventional observer greatly depends on the length of the delays. Numerical examples of an integral process and an unstable process demonstrate that the errors of numerical computation in the proposed predictor are smaller than those in the conventional observer.