System identification of a free floating telerobot using Kalman filtering and a stereoscopic vision sensor

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references (leaves 78-79).Issued also on microfiche from Lange Micrographics.A telerobot has been acquired that floats on air bearings and is intended to simulate the dynamics of a spacecraft in a two-dimensional plane. The robot was delivered without a computer, sensors or documentation so an effort has been launched to determine how the apparatus worked and to identify the model parameters associated with mass, moment of inertia and thrust. The robot has been modified to accommodate a laptop as the onboard computer and a unique stereoscopic vision sensor as a navigation system. The unknown model parameters are then identified using both least squares estimation and Kalman filtering. The unique stereoscopic vision sensor system is based on one-dimensional position sensing diodes (PSD's) and active targets that broadcast a modulated signal. The active targets are mounted at known points in the robot frame of reference and broadcast their signal to the PSD sensors that are stationary in the inertial coordinate frame. This system enables real-time attitude measurements with no moving parts. The robot's mass, moment of inertia and the forces generated by its thrusters are identified using direct measurements and the well known linear least squares estimation algorithm. Identification using these techniques required experiments specifically designed to characterize the system. Some of the parameters may change over time, so a means of conducting on-line system identification was developed. A Kalman filter was designed which could simultaneously perform state estimation and parameter identification on-line. This technique did not require an experiment specifically designed for identification purposes and could accurately find the unknown model parameters during normal robot maneuvering

Push, Pull, Shrink, Grow: The co-share workplace interior that reflects changing spatial needs

This thesis explores how co-working offices emerged as a solution to the shift in the social expectations of the workplace. It studies how the rise in the number of freelancers and entrepreneurs has resulted in the materialisation of co-working offices. It examines how co-working offices offer flexibility in terms of membership plans, but how their interior environments do not yet reflect this. In short it aims to investigate how these workplace interiors can adapt to meet residents needs. This research embraces the multi-functionality of the co-working office and the demands of residents who occupy these spaces. Three local case studies and international precedents are explored which give insight and offer opportunities on materiality, site context and multi-functional spaces. It explores how to engage residents by challenging how best to design co-working offices. This project considers the requirements of the co-working office and how co-working interiors are occupied throughout the day. The design proposes a kit of parts ‘space making’ solution, which enables co-working offices to meet resident’s needs. This research contributes to the limited published discussion of understanding interior space in the context of co-working offices. This research explores through interior architecture, how co-working offices can be designed to reflect its resident’s individual ways of working and co-workings varying spatial needs. Although based around co-working spaces, the researcher recognises the implications for findings based around corporate office environments

Four principles for practising and evaluating co-production - a view from sustainability research.

The co-production paradigm has become commonplace across many disciplines as a means of orchestrating the production of useful knowledge aligned to different social needs. Drawing on the expertise of 36 co-production practitioners in the field of sustainability research, Dr Albert Norström, Dr Chris Cvitanovic, Dr Marie F. Löf, Dr Simon West and Dr Carina Wyborn, present a new working definition of co-produced research and suggest how different elements of successfully co-produced knowledge can be understood and evaluated

Guest Editorial: Special issue Rescuing Legacy data for Future Science

Research and discovery in the natural sciences, particularly for documenting changes in our planet, is empowered by gathering, mining, and reusing observational data. However much of the data required, particularly data from the pre-digital era, are no longer accessible to science. The data are hidden away in investigators’ desks on printed paper records, or are no longer readable as they are on deteriorating or outdated media, and are not documented in a way that makes them re-usable. Special initiatives are required to rescue them and preserve such data so that they can contribute to the scientific debates of today and those of the future. Data rescue efforts are key to making data resources accessible that are at risk of being lost forever when researchers retire or die, or when data formats or storage media are obsolete and unreadable

RDM+PM Checklist: Towards a Measure of Your Institution’s Preparedness for the Effective Planning of Research Data Management

A review at our institution and a number of other Australian universities was conducted to identify an optimal institutional-wide approach to Research Data Management (RDM). We found, with a few notable exceptions, a lack of clear policies and processes across institutes and no harmonisation in the approaches taken. We identified limited methods in place to cater for the development of Research Data Management Plans (RDMPs) across different disciplines, project types and no identifiable business intelligence (BI) for auditing or oversight. When interviewed, many researchers were not aware of their institution’s RDM policy, whilst others did not understand how it was relevant to their research. It was also discovered that primary materials (PM), which are often directly linked to the effective management of research data, were not well covered. Additionally, it was unclear in understanding who was the data custodian responsible for overall oversight, and there was a lack of clear guidance on the roles and responsibilities of researchers and their supervisors. These findings indicate that institutions are at risk in terms of meeting regulatory requirements and managing data effectively and safely. In this paper, we outline an alternative approach focusing on RDM ‘Planning’ rather than on RDMPs themselves. We developed simple-to-understand guidance for researchers on the redeveloped RDM policy, which was implemented via an online ‘RDM+PM Checklist’ tool that guides researchers and students. Moreover, as it is a structured tool, it provides real-time business intelligence that can be used to measure how compliant the organisation is and ideally identify opportunities for continuous improvement

Seeps, springs and wetlands: San Juan Basin, Colorado. Social-ecological climate resilience project

Prepared for: North Centeral Climate Adaptation Science Center.Social-Ecological Climate Resilience Project, 2016.Includes bibliographical references

Developing an e-Research infrastructure for Australian earth sciences: the NCRIS 5.13 AuScope Grid

On the 27 November 2006 the Minister for the Department of Education, Science and Technology announced that under the National Collaborative Research Infrastructure Strategy (NCRIS) $42.8 million would go to Australian Earth Sciences to help build an integrated national infrastructure system called AuScope. A key element of AuScope is the AuScope Grid, which comprises an Earth Science Data Grid and a Compute Grid. Combined both provide a distributed computational e-Research infrastructure that will enable the construction of a dynamic updateable 4D Australian Earth Model. The goal of the AuScope Compute Grid is to facilitate quantitative geoscience analysis by providing an infrastructure and tools for advanced data mining, simulation and computational modelling. The AuScope Earth Science Data Grid is a proposed national geoscience data network, which aims to use international standards to allow real time access to data, information and knowledge stored in distributed repositories from academia, industry and government. The Data grid will be also built on ‘end-to-end’ Science principles (aka open access principles) whereby there will be access to the highly processed information and knowledge, as well as the original raw data and the processing programs used to generate the results