Titlebook: Highly Redundant Sensing in Robotic Systems; Julius T. Tou,Jens G. Balchen Conference proceedings 1990 Springer-Verlag Berlin Heidelberg 1

天空

Highly Redundant Sensing in Robotics — Analogies From Biology: Distributed Sensing and Learning. By postulating hypothetical delay elements, we show how a laterally inhibited sensor net can function as a tunable low-pass filter. Thus distributed sensor networks’ dynamic properties provide filters appropriate for sensory input to motor learning.

亞當(dāng)心理陰影

凹室

奇思怪想

0258-1248 sensory systems for robotic control and manipulation. In an unstructural environment, robotic sensing translates measurements and characteristics of the environment and working objects into useful information. A robotic system is usually equipped with a variety of sensors to perform redundant sensin

暫時(shí)中止

An Overview of Visual and Tactile Sensor Technologyly important issues of sensors for robotics which represent one of the most important enabling technologies for the subject as a whole. The references included at the end of the paper allow the reader to research the contents more completely than is possible in just a few pages.

cornucopia

The multisensory system of the KAMRO robotdata with the help of a knowledge based system. The results of the interpretation of the sensor data are input into the planing process. Thereby, it is possible to respond to the working conditions of the robot.

武器

Blasphemy

Tools for Multisensor Data Fusion in Autonomous Robots of data to the system components. The interface mechanism supports both shared memory and message passing communications facilities. The development support tools all take advantage of the power of the representation and control components to ease the configuration, implementation and debugging of the system software components.

招惹

Obstacle

A Knowledge-based System for Redundant and Multi Sensing in Intelligent Robotscessor, integration, and analysis. The goals for redundant and multi sensing are explained. The architecture for redundant and multi sensing system is discussed. For achieving information integration in systems with redundant sensors, we suggest Boolean fusion, probabilistic fusion, and Markov renewal analysis in addition to geometrical fusion.