The lifecycle cost of Industry 4.0 technology can be prohibitively expensive for organisations, particularly when low utilisation, high supply chain costs and the overhead of highly skilled teams are taken into account. The potential of modularity to improve automation adoption across a wider System of Systems was investigated in support of the UN Sustainable Development Goals.
SmartAccess is a startup that provides footpath accessibility audits to empower disabled individuals to plan a safe journey around our built environment. Councils also utilise the insights to prioritise infrastructure investment. Traditionally, these audits are performed by technicians taking photos, recording features and manually measuring inclines and ramp angles. This process is labour-intensive, susceptible to human error and difficult to scale.
A 10-day engagement was utilised, leveraging a modular architecture from a horticulture robotics programme. The architecture consisted of image recognition on the edge, a navigation subsystem, sensor fusion, an observation data interface based on ISO19156 and a digital twin built on the Azure cloud.
The prototype geo-spatially maps terrain, raw vibration data and 4 of the desired 40 features for SmartAccess. The project uncovered new requirements in terms of data capture tool usability, the need for feature tracking to eliminate duplicates and a data streaming toolset to translate raw terrain and acceleration data into a form suitable for end-user consumption. The key insight, that has subsequently been implemented, is the capability for a human to enhance the data as an affordable mechanism to evolve the system while gaining incremental value in production.
Notwithstanding the drawbacks associated with core module design complexity, the use case has demonstrated the potential for modular Industry 4.0 systems to support UN Sustainable Development Goals, especially when coupled with human-machine teaming to enable incremental improvement through small, affordable projects that take artificial intelligence-based systems through an apprenticeship.