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Drones in Mining: Technology Trends

By GlobalData Thematic Research 15 Jan 2021 (Last Updated January 15th, 2021 14:18)

Drone technology has demonstrated a varied range of mining applications from exploration, surveying and mapping to maintaining safety and enhancing security. These unmanned aerial vehicles (UAVs) are equipped with different sensors and cameras based on their type.

Drones in Mining: Technology Trends
Credit: 2days, Shutterstock.com.

The popularity of drones across the mining industry has grown immensely in recent years, with GlobalData’s 2019 survey showing 26% of mines with considerable investments in drone technology.

Technology Trends

Listed below are the key technology trends impacting the drone industry, as identified by GlobalData.

Scalability

To improve flight performance and expand the capabilities of their drones, drone manufacturers are working on scaling drone technology up on one hand, to deliver greater carrying capacity and endurance, and down on the other to deliver low cost, small footprint drones for surveillance. The miniaturisation of sensors helps to cut down the overall size and weight of drones and reduce their power requirements.

Processor chips

Microprocessors serve as the control centres for drones, providing a platform for control and communications software that integrates with collision avoidance sensors, high definition cameras, and other sensors. Advances in chip design are leading to smaller chips with higher performance and lower cost, which in turn helps to drive down the manufacturing cost of drones.

3D technology

The ability of 3D modelling technologies to consume drone data in the form of imagery and radar/LIDAR data and convert it into complete topological models makes it possible to survey and monitor the landscape and the objects within it. Whether the application is the surveying of structures like bridges, buildings, factories or oil rigs, or the monitoring of farmland or forestry, drones are increasingly being integrated with improved sensors, high definition cameras and computer algorithms that can condense the images into 3D virtual images and enable easy assessment of anomalies.

Artificial intelligence (AI)

The growing volume of data gathered by drones will create demand for increasingly sophisticated analysis of that data. To effectively process incoming sensor data and draw meaningful conclusions drone solutions need to make use of the latest data analytics technologies.

AI enables ‘continued learning’ for drones through techniques like machine learning, in order to enable complex capabilities like autonomous flying and obstacle recognition and avoidance. While the industrial sector is already proving to be a significant market for drones with AI capabilities, service sector companies are also vying for AI-enabled drones to develop new business models.

Manned Unmanned Teaming

Manned Unmanned Teaming (MUM-T) is described by the US Army Aviation Centre (USAACE) as: ‘The synchronised employment of soldier, manned and unmanned air and ground vehicles, robotics, and sensors to achieve enhanced situational understanding, greater lethality, and improved survivability.’

Currently, MUM-T capabilities are most commonly deployed on rotary platforms such as the AH-64E, which receives a range of data from unmanned platform, expanding the capabilities of the team as a whole.

Drone swarm technology

The need to manage and control multiple drones in close proximity will become more acute as the number of active drones grows. Cisco is promoting the concept of connected drones that can be controlled via a cloud-based infrastructure. Currently most of the data generated by drones is transferred to cloud systems for users to access and analyse, often not in real-time.

Augmented reality (AR)

As the capabilities of AR technologies improve, drone makers are increasingly incorporating AR functionality into their products to enhance the user experience and make the application of drone technology more effective. The European Space Agency (ESA) has backed a French start-up, Sysveo, to integrate user made AR into a drone’s video streams.

Anti-collision technology

While the relatively small scale of today’s commercial drone deployments means that there is currently little risk of collision between drones, the widespread application of drone technology will require effective anti-collision systems to ensure that they can be operated safely in public places.

The European Union (EU) has a USpace programme under the Single European Sky Air Traffic Management Research (SESAR) project and the US’ NextGen programmes are aiding the development of anti-collision measures and also validating the feasibilities of high demanding beyond visual line of sight (BVLOS) operations.

Battery technology

Most of today’s drones are powered by lithium polymer (LiPo) batteries, which are known to deliver sufficient energy required to perform standard drone flights. However, a growing demand for longer flight times and greater carrying capacity is driving drone manufacturers to explore alternative technologies such as hydrogen cells, gasoline powered solutions, solar batteries, gas-electric hybrid solutions, and laser solutions.

Edge and fog computing

Fog computing is a computing model which permits collected data to be analysed within the drone itself, prior to interacting with the central point of control.

As the volume of data that is gathered and analysed by drone increases, the ability to perform this analysis at the point of collection will grow in importance.

The use of fog computing will enable drone operators to reduce latency and limit the amount of data that needs to be transmitted from the drone to the controlling application. Other technologies like adaptive video streaming, parallel successive refinement-based streaming, and networked camera drones will become more common in future thanks to the increasing processing power that is deployed at the edge.

Drones as a service (DaaS)

Over the next two years a number of specialist service companies will emerge, offering a turnkey solution for drone-based surveying, monitoring, and delivery. Consequently, organisations will be able to rent drone services on an as-needed basis.

Unmanned aircraft system traffic management (UTM)

As the adoption and application of drone technology becomes more widespread, the need for autonomous UTM system, which can ensure safety, security and control of drones in low-altitude airspaces, will grow significantly. In addition, the need for UTM is identified as a key enabler for future autonomous passenger drones, vertical take-off and landing (VTOL) air systems and BVLOS operations.

Drone delivery

Drone delivery is the most anticipated, and hyped, commercial application of drone technology. Encouraged by Amazon’s vision of drone powered package deliveries, the global drone community has shown great interest in this new model of distribution.

Some companies are looking at hyper-local/hyper-personal drone delivery by applying AI, 3D, and AR. For instance, IBM’s drones can recognise a person’s need for coffee using inputs from his/her wearable gadget and deliver it from a nearby coffee counter. Volocopter and Ehang have also demonstrated a new, cost effective mode of transport via drones. Meanwhile, Uber has initiated the Uber Elevate programme to evaluate passenger-carrying VTOL drones.

This is an edited extract from the Drones in Metals and Mining – Thematic Research report produced by GlobalData Thematic Research.

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