NASA space station automation: AI-based technology review. Executive summary

Research and Development projects in automation technology for the Space Station are described. Artificial Intelligence (AI) based technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

Optimal and intelligent decision making in sustainable development of electronic products

Increasing global population and consumption are causing declining natural and social systems. Multi-lifecycle engineering and sustainable development address these issues by integrating strategies for economic successes, environmental quality, and social equity. Based on multi-lifecycle engineering and sustainable development concepts, this doctoral dissertation aims to provide decision making approaches to growing a strong industrial economy while maintaining a clean, healthy environment. The research develops a methodology to complete both the disassembly leveling and bin assignment decisions in demanufacturing through balancing the disassembly efforts, value returns, and environmental impacts. The proposed method is successfully implemented into a demanufacturing module of a Multi-LifeCycle Assessment and Analysis tool. The methodology is illustrated by a computer product example. Since products during the use stage may experience very different conditions, their external and internal status can vary significantly. These products, when coming to a demanufacturing facility, are often associated with incomplete/imprecise information, which complicates demanufacturing process decision making. In order to deal with uncertain information, this research proposes Fuzzy Reasoning Petri nets to model and reason knowledge-based systems and successfully applies them to demanufacturing process decision making to obtain the maximal End-of-Life (BOL) value from discarded products. Besides the BOL management of products by means of product/material recovery to decrease environmental impacts, the concepts of design for environment and sustainable development are investigated. Based on Sustainability Target Method, a sensitivity analysis decision-making method is proposed. It provides a company with suggestions to improve its product\u27s sustainability in the most cost-effective manner

Ground Robotic Hand Applications for the Space Program study (GRASP)

This document reports on a NASA-STDP effort to address research interests of the NASA Kennedy Space Center (KSC) through a study entitled, Ground Robotic-Hand Applications for the Space Program (GRASP). The primary objective of the GRASP study was to identify beneficial applications of specialized end-effectors and robotic hand devices for automating any ground operations which are performed at the Kennedy Space Center. Thus, operations for expendable vehicles, the Space Shuttle and its components, and all payloads were included in the study. Typical benefits of automating operations, or augmenting human operators performing physical tasks, include: reduced costs; enhanced safety and reliability; and reduced processing turnaround time

Robotic disassembly of waste electrical and electronic equipment

Waste electrical and electronic equipment (WEEE) is the world’s fastest growing form of waste. Inappropriate disposal of WEEE causes damage to ecosystems and local communities due to hazardous materials and toxic chemicals present in electronic products. High value metals in small quantities are dissipated and embodied energy from manufacturing are lost in shredding and crushing treatments of WEEE. On the other hand, manual disassembly is costly and presents safety concerns for human workers. Therefore, robotic disassembly is an ideal approach to addressing the treatment of WEEE. Despite extensive research in the field, large variations and uncertainties in product structures, models, and conditions is a major limitation to the implementation of automation and robotics in the waste industry. The ability of a robotic disassembly system to learn new product structures and reason about existing knowledge of product structure is vital to addressing this challenge. This thesis explores robotic disassembly for WEEE by building upon an existing research disassembly rig for LCD monitors and expanding it to address other product families. The updated disassembly system utilizes a modular framework consisting of a Cognition module, Perception module, and Operation module, in order to address the uncertainties present in end-of-life (EoL) products. A novel disassembly ontology is designed and developed with an upper and lower ontology structure to represent generic disassembly knowledge and product-family-specific knowledge respectively. Furthermore, a Learning framework enables automated expansion of the ontology using past disassembly experiences and user-demonstration. These presented methodologies form the main function of the Cognition module, which aids the Perception module and instructs the Operation module. The disassembly ontology and Learning framework are verified independently from the rest of the system prior to being integrated and validated with real disassembly runs of LCD monitors and keyboards. As such, the disassembly system’s ability to address both known and unknown EoL product types, as well as learn new product types, is demonstrated