Augmented Reality (AR) offers the possibility of new opportunities with high revenue potential. Among them, logistics automation and industrial asset maintenance and control markets are promising fields, given the growing importance of decreasing the error rate while complexity and the need for flexibility grow (e.g. due to requirements of individualised production → one piece flow) in tasks where manpower and personnel still play an important role.
Unfortunately, while traditional barcode scanners, RFID handhelds and connected mobile devices are already used as part of the broader logistics automation solution and industrial asset management, AR technologies -that can potentially enhance operational efficiencies at every point in supply chain- have not addressed so far the requirements to be implemented in actual environments. Parallel to that, new technological solutions based on wearable devices (e.g. Smart Glasses, wearable RFID readers) offer novel opportunities for mobile process related AR applications.
One of the major problems is the lack of a common, platform-independent optimized framework: Even though in recent years there have been new and innovative ways of interaction between Head Mounted Devices (HMDs) and the real world around the concept of AR, these new techniques have generated a multitude of small applications that become obsolete in the very short-term, with very poor connection with already running solutions (like ERPs, CRMs, monitoring and sensoring solutions, and remote-controlled machinery interfaces), and therefore, with little impact on target sectors. This situation is leading to an excessive dispersion of functionality and devices, reducing the satisfactory progress of the concept.
Currently, the selection of the technology is conditioned by the specific domain where the project is going to be deployed and by the real use cases that will be addressed. That is, the environment and conditions of the workplace (indoor, outdoor, light conditions, noise conditions, etc.), workwear, security considerations, speed and accuracy of object recognition (when talking about Computer Vision algorithms), communication with different systems –i.e. legacy systems as ERPs, CRMs, etc.- and devices, the need of customized reports or specific data, the required interaction with the user, etc., must be considered to select a specific framework and vendor, and specific devices, and the customization and integration effort can’t be usually reused in new projects, as the conditions may differ and a new device or SDK implies the generation of new components almost from scratch.
Additionally, the solutions that are currently being developed are lacking of a real commercial perspective, not being compliant with already EU consolidated directives in terms of Prevention of Occupational Risks, where –for instance- protective and insulated gloves are needed to avoid risks due to different reasons (weather, risk of electrical damage, biohazards…), that are not compatible with the input interfaces currently developed: voice commands like those used with Google Glass are clunky, poorly suited for public use outdoor, and inefficient; and even though new UIs based on Computer Vision and Motion Sensing are providing new and promising ways of interaction, again, the lack of a real commercial perspective and the absence of a platform-independent optimized framework, combined with a fast-changing environment with a new emerging technologies, means a great effort for integration and a imply a high risk of vendor lock-in.
➢ AR-LEAN project aims to define a new open architecture, and a common, platform-independent optimized framework for a wide range of HMDs and further wearable devices, generating components that will cover the main elements of logistics, paying attention to specific tasks where manpower and personnel play an important role -such as order picking, navigation (both within the warehouse and outside), traceability and identification- and industrial asset management– taking into account monitoring, sensoring, and maintenance of industrial assets, and remote-controlled machinery-.
➢ AR-LEAN will also generate specific components to integrate consolidated solutions (ERPs, CRMs, Ticket trackers, Sensoring and Monitoring solutions) and will generate a comprehensive user interface, providing a set of components to easily use instructions in three dimensional space, that will take into account different technologies used by different HMDs and further wearable devices.
➢ AR-LEAN aims to decrease the error rate and decision-making time, simplifying the processes where human resources are involved, by taking care of stressful situations and improving working routines and real, industrial environments, paying special attention to EU directives for Prevention of Occupational Risks.