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The adaptation to changes in human operator dynamics and changes in working environment dynamics can be an important issue in designing high performance telerobotic systems. This paper describes an approach to force control in telerobotic hand systems in which model reference adaptive control techniques are used to adapt to changes in human operator and working environment dynamics. The techniques have been applied to force-reflective control of a single degree-of-freedom telerobotic gripper system at Wisconsin Center for Space Automation and Robotics (WCSAR). This adaptive gripping system is described in the paper along with results of experiments with human subjects in which the performance of the adaptive system was analysed and compared to the performance of a conventional non-adaptive system. These experiments emphasized adaptation to changes in compliance of gripped objects and adaptation to the on-set of human operator fatigue.

From the March/Micra released in 2002 to the LEAF EV today, on-board electronic systems have come to possess bigger software programs and more complexity in the transition to distributed high-speed ECU network systems. Meanwhile, faster time-to-market requirements have shortened the development period. If a conventional development process like the waterfall model is applied to develop a distributed ECU network system, numerous problems may occur at the vehicle testing stage that requires a lot of rework. To avoid that, we devised a new development methodology of distributed ECU systems and implemented it in our vehicle function development programs to improve development work quality across the entire spectrum of vehicle electronics. This development methodology consists of two principal elements. One is an Electronic Integration Platform (EIPF) and the other one is a Design-EIPF (D-EIPF).

This paper describes a vision-based vehicle detection system for a blind spot warning function. This detection system has been designed to provide ample performance as a driving safety support system, while streamlining the image processing algorithm so that it can be processed using the computational power of an existing ECU. The procedure used by the system to detect a vehicle in a blind spot is as follows. The system consists of four functional components: obstacle detection, velocity estimation, vertical edge detection, and final classification. In obstacle detection, a predicted image is generated under the assumption that the road surface is a perfectly flat plane, and then an object is detected based on a histogram that is created by comparing the predicted image and an actually observed image. The velocity of the object is estimated by tracking the histogram over time, assuming that both the object and the host vehicle are traveling in the same direction.