Depicting the future strategic plans of the Royal Australian Navy using a roadmapping framework as a visual composite canvas

Strategic planning can be an arduous and complex task; and, once a plan has been devised, it is often quite a challenge to effectively communicate the principal missions and key priorities to the array of different stakeholders. The communication challenge can be addressed through the application of a clearly and concisely designed visualisation of the strategic plan - to that end, this paper proposes the use of a roadmapping framework to structure a visual canvas. The canvas provides a template in the form of a single composite visual output that essentially allows a 'plan-on-a-page' to be generated. Such a visual representation provides a high-level depiction of the future context, end-state capabilities and the system-wide transitions needed to realise the strategic vision. To demonstrate this approach, an illustrative case study based on the Australian Government's Defence White Paper and the Royal Australian Navy's fleet plan will be presented. The visual plan plots the in-service upgrades for addressing the capability shortfalls and gaps in the Navy's fleet as it transitions from its current configuration to its future end-state vision. It also provides a visualisation of project timings in terms of the decision gates (approval, service release) and specific phases (proposal, contract, delivery) together with how these projects are rated against the key performance indicators relating to the technology acquisition process and associated management activities. © 2013 Taylor & Francis.

A modular planar robotic manipulandum with end-point torque control.

Robotic manipulanda are extensively used in investigation of the motor control of human arm movements. They permit the application of translational forces to the arm based on its state and can be used to probe issues ranging from mechanisms of neural control to biomechanics. However, most current designs are optimized for studying either motor learning or stiffness. Even fewer include end-point torque control which is important for the simulation of objects and the study of tool use. Here we describe a modular, general purpose, two-dimensional planar manipulandum (vBOT) primarily optimized for dynamic learning paradigms. It employs a carbon fibre arm arranged as a parallelogram which is driven by motors via timing pulleys. The design minimizes the intrinsic dynamics of the manipulandum without active compensation. A novel variant of the design (WristBOT) can apply torques at the handle using an add-on cable drive mechanism. In a second variant (StiffBOT) a more rigid arm can be substituted and zero backlash belts can be used, making the StiffBOT more suitable for the study of stiffness. The three variants can be used with custom built display rigs, mounting, and air tables. We investigated the performance of the vBOT and its variants in terms of effective end-point mass, viscosity and stiffness. Finally we present an object manipulation task using the WristBOT. This demonstrates that subjects can perceive the orientation of the principal axis of an object based on haptic feedback arising from its rotational dynamics.