Conference on Intelligent Robotics in Field, Factory, Service, and Space (CIRFFSS 1994), volume 1

The AIAA/NASA Conference on Intelligent Robotics in Field, Factory, Service, and Space (CIRFFSS '94) was originally proposed because of the strong belief that America's problems of global economic competitiveness and job creation and preservation can partly be solved by the use of intelligent robotics, which are also required for human space exploration missions. Individual sessions addressed nuclear industry, agile manufacturing, security/building monitoring, on-orbit applications, vision and sensing technologies, situated control and low-level control, robotic systems architecture, environmental restoration and waste management, robotic remanufacturing, and healthcare applications

Infrared Technologies for Defence Systems

 Infrared technology has seen phenomenal growth since its inception during World War II. Defence applications have been the main driver of infrared technology development all over the world. Infrared systems have been mainly developed for night vision, all weather surveillance, search and tracking and missile seeker applications. Ever demanding defence system requirements have facilitated considerable investment. Research has been mainly directed towards the product development. Medical applications such as thermographs, transportation applications such as enhanced vision systems for airplanes, helicopters, sea vehicles, and automobiles, law enforcement applications in drug prevention and criminal tracking, managing forest fires and environmental monitoring are some of the spin-offs. Infrared technology has proven to be a force multiplier in war as well as low intensity conflict situation. Intelligent vision sensor development covering visible-infrared spectrum for automated surveillance, change detection, 3D machine vision systems, dynamic particle metrology, missile and ballistic testing/imaging, faster, more precise and more manoeuvrable robotic applications will drive the future research

A comparative study on the performance of neural networks in visual guidance and feedback applications

Vision-based systems increase the flexibility of industrial automation applications by providing non-touching sensory information for processing and feedback. Artificial neural networks (ANNs) help such conformities through prediction in overcoming nonlinear computational spaces. They transform multiple possibilities of outcomes or regions of uncertainty posed by the system components towards solution spaces. Trained networks impart a certain level of intelligence to robotic systems. This paper discusses two applications of machine vision. The 3 degrees of freedom (DOF) robotic assembly provides an accurate cutting of soft materials with visual guidance using pixel elimination. The 6-DOF robot combines visual guidance from a supervisory camera and visual feedback from an attached camera. Using a switching approach in the control strategy, pick and place applications are carried out. With the inclusion of ANN to make the strategies intelligent, both the systems performed better with regard to computational time and convergence. The networks make use of the extracted image features from the scene for different applications. Simulation and experimental results validate the proposed schemes and show the effectiveness of ANN in machine vision applications