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November 10, 2017

Business environments ranging from manufacturing plants to retail warehouses are increasingly deploying ‘smart’ machines for commercial and retail purposes. The machines address a variety of problems, ranging from remote tele-presence to physically manipulating the scene in the real world. Unmanned aerial vehicles (UAVs, or drones) are a good example of smart machines that help businesses map or inspect environments that might be inaccessible to humans.

Over the past 16 months, my team and I have been working to develop commercial-grade UAVs that act as data collection agents and solve a variety of targeted mapping and inspection problems for field operations teams. I foresee that such machines will have a strong impact on the work lives of the operations workforce at different businesses.

Depending on the business objectives, we adapted several types of drones to conform to different operating requirements. Jobs that are repetitive (thereby producing fatigue) or involve work in challenging or hazardous environments are best done by UAVs, which can be monitored by humans from a safer work environment.

While improving business efficiency is one of the outcomes of such projects, a more important result is the unshackling of work environments and activities that were earlier inaccessible to humans. However, the demands of different operating environments on drones, and the associated sensory payload, vary considerably. Which is why the need of the hour is to design, deploy, and sustain fit-for-purpose UAVs in the field.

Fit-for-purpose Drones

So, what exactly are the purposes these drones serve? Are drones employed in a warehouse environment similar to the ones flown against wind turbines or along power lines? Here are some examples of scenarios where the need for selecting a fit-for-purpose drone has been demonstrated in a real-world operating environment.

Warehouses are an ideal setting for drones because of the repetitive nature of the tasks involved (for instance, inventory verification). They reduce the manual workload in such environments and increase efficiency and associated outcomes.

Consider a 2-million-sq-ft warehouse with 42 ft-high racks and constrained 10 ft-wide aisle corridors. Here, a lightweight small form factor drone that will generate minimal prop wash effects against the inventory placed on the shelves would be ideal. Further, the drone needs to navigate safely at the warehouse, considering it is a GPS-denied environment.

Adaptability Is Key

Let us look at the other environmental factors that must be kept in mind while employing fit-for-purpose drones. For instance, aerial vehicles that are used to inspect wind turbines need to be rock steady against the obviously persistent and heavy gusts of air. This requires the drone to rapidly sense and respond to changing wind gusts and stabilize itself even before it can start capturing useful data for inspection.

For the development of these fit-for-purpose UAVs, I leverage a rich ecosystem of partners and researchers. We are exploring several ways of adding to the fit for purpose narrative and the associated enterprise solutions. Without the aid of this research network, it is difficult to achieve favorable results towards the advancement of UAVs.

In this field, there is a lot of scope for innovation and disruption by reimagining the current planning and operational processes. We need to work towards enabling a safer and more efficient work environment for the industrial workforce. A holistic approach by the enterprise to address aspects of data management, IT processes and systems integration will have to be deployed.

Have you seen other requirements or scenarios for fit-for-purpose drones? What constraints and challenges have you experienced on this front? Let us know what you think.

Mahesh Rangarajan is a practicing Platforms and Enterprise Architect and heads the Drones Incubation Program at TCS. He focuses on delivering advanced automation solutions leveraging increasingly sophisticated unmanned aerial vehicles. For this process, he taps into advanced data processing algorithms, associated solution pipelines across a variety of industries, and planning and operations automation opportunities. His current areas of study include the intersections between man, machine, and material collaboration, and associated real-world problems worth solving.


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