Drones in Construction: Technology Trends

There are a number of uses for drones in construction, such as the ability to measure projects through the use of drones versus more traditional surveying methods.

Technology trends

The following are the key technology trends affecting the drone industry as identified by GlobalData.

Scalability

To improve flight performance and expand the capabilities of their drones, drone manufacturers are working to scale up drone technology on the one hand to provide greater carrying capacity and endurance, and on the other hand to provide low-cost drones with a small footprint for surveillance. The miniaturization of sensors helps to reduce the overall size and weight of drones and to reduce their energy requirements.

Processor chips

Microprocessors serve as control centers for drones and provide a platform for control and communication software that can be integrated with sensors for collision avoidance, high-resolution cameras and other sensors. Advances in chip design, driven to a large extent by the cellular industry, are leading to smaller chips with higher performance and lower cost, which in turn helps reduce the cost of manufacturing drones.

3D technology

The ability of 3D modeling technologies to process drone data in the form of images and radar / light detection and range (LIDAR) data and convert it into full topological models enables the surveying and monitoring of the landscape and the objects within it. Whether the application is in the measurement of structures such as bridges, buildings or the monitoring of farmland or forestry, drones are increasingly being integrated with improved sensors, high-resolution cameras and computer algorithms that condense the images into virtual 3D images and allow anomalies to be easily assessed .

Artificial Intelligence (AI)

The growing amount of data collected by drones will require ever more sophisticated analysis of this data. In order to effectively process incoming sensor data and draw meaningful conclusions, drone solutions must use the latest data analysis technologies. In addition, through techniques such as machine learning, AI enables “continuous learning” for drones to enable complex capabilities such as autonomous flying and obstacle detection and avoidance.

Manned unmanned teaming (MUM-T)

MUM-T is described by the US Army Aviation Center (USAACE) as: “The synchronized deployment of soldiers, manned and unmanned aerial and ground vehicles, robotics and sensors to achieve improved understanding of the situation, greater lethality and improved survivability.” Currently, MUM-T functions are most commonly used on rotating platforms such as the AH-64E, which receives a range of data from an unmanned platform and expands the capabilities of the entire team.

Drone swarm technology

The need to manage and control multiple drones in close proximity becomes more pressing as the number of active drones increases. Cisco promotes the concept of connected drones that can be controlled via a cloud-based infrastructure. The company argues that the ability to manage multiple drones at the same time will enable faster data collection over vast areas, coupled with simultaneous data processing to provide timely and accurate data. Currently, most of the data generated by drones is transferred to cloud systems for users to access and analyze, often not in real time.

Augmented Reality (AR)

As the capabilities of AR technologies improve, drone manufacturers are increasingly integrating AR capabilities into their products to improve the user experience and make the application of drone technology more effective. The European Space Agency (ESA) has helped a French start-up, Sysveo, to integrate custom AR into a drone’s video streams. This integration is intended to enable real-time analysis of the collected data in order to improve operational efficiency and also provide improved anti-collision measures.

Anti-collision technology

While the relatively small scale of commercial drone use today means that there is currently little risk of collision between drones, the widespread use of drone technology requires effective anti-collision systems to ensure they can operate safely in public places. Various sensor payloads are being developed to enable improved management and control of drones to convince regulators and insurers that drones can operate safely and autonomously.

Battery technology

Most of today’s drones are powered by lithium polymer (LiPo) batteries, which are known to provide enough power to fly normal drone flights. However, the ability to transport increasingly heavy payloads and perform more demanding missions in different environments is limited by the fact that current drones are limited in their endurance. The growing demand for longer flight times and greater carrying capacity is driving drone manufacturers to research 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 computational model that makes it possible to analyze collected data within the drone itself (the edge) before it interacts with the central control point. Because of the cost, complexity, and latency associated with transmitting large amounts of sensor data from drones to a central point for analysis, there can be a significant delay between the perception of an event and the resulting action. The use of fog computing will enable drone operators to reduce latency and limit the amount of data that has to be transmitted from the drone to the controlling application.

Drones as a Service (DaaS)

A number of specialized service companies will emerge over the next two years, offering a turnkey solution for drone-based surveying, monitoring and provisioning. Instead of having to develop drone skills in-house, companies can rent drone services as needed.

Traffic management for unmanned aircraft (UTM)

As drone technology becomes more widespread and applied, the need for autonomous UTM systems that can ensure the safety and control of drones in low airspaces will increase significantly. In addition, the need for UTM is identified as a key factor for future autonomous passenger drones, vertical take-off and landing systems (VTOL) and BVLOS operations.

Drone delivery

The deployment of drones is the most anticipated and hyped commercial application of drone technology. Encouraged by Amazon’s vision of drone-powered parcel deliveries, the global drone community has shown great interest in this new distribution model. With numerous initiatives currently in pilot trials around the world, proponents promise that this will cause significant disruption to existing industrial distribution channels.

This is an edited excerpt from the Drones in Construction – Thematic Research Report prepared by GlobalData Thematic Research.

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