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New Zealand to Deploy Drones for Complex Environments

People can map and survey a variety of environmental factors using drone technology, including land erosion, wildfire risk, invasive species growth, endangered species populations, and more. The data can then be used by organisations to make better, more informed decisions that protect both humans and nature.

Human disturbance can cause unintentional damage to fragile ecosystems. Humans, for example, may unintentionally disrupt the behaviour of endangered species or introduce pathogens into the environment that can affect wildlife through reverse zoonosis. Drones assist researchers in avoiding this type of harm by putting distance between humans and the environment under study.

The increased development and production of unmanned aerial vehicles (UAVs or “drones”) with a higher level of autonomy can help reduce human error and aid in safer, faster, and more accurate inspections. They make it easier to monitor infrastructural changes over time due to their ability to perform repeated inspections, even in challenging and dynamic environments. This will benefit several potential applications, such as civil engineering inspections.

Unmanned aerial vehicles (UAVs), also known as drones, are revolutionising surveying and inspection tasks that were previously performed by manned aircraft. Two academics from New Zealand’s University of Canterbury are currently working to advance this capability, allowing drones to use tools with high precision in mid-air in difficult changeable environments.

University Canterbury’s Computer Science Professor who specialises in artificial intelligence and a Mechanical Engineering Professor who is also an expert in aerospace engineering are working together on the project, which has the potential to drastically change the way drones are used in various industries.

The computer science professor stated in an interview “Our research will go beyond simple surveying and inspection tasks. We will build a platform for UAVs to use precision tools in changing dynamic environments, such as outdoors, around hazardous infrastructure like power pylons, or in construction or forestry, without ladders or scaffolding,”.

“Enabling UAVs to use precision tools is a new technological capability and a radical change in functionality. It will not only transform the way UAVs can be used, but also how and where.” He then added.

They developed their research and implementation initiatives with input from UAV manufacturers, UAV organisations, a diverse range of industrial end-users in arboriculture, silviculture, the electricity infrastructure industry, civil construction, Maori stakeholders, and government and regulatory bodies.

The recent advancement toward autonomous inspections using conventional UAVs outfitted with either image-based or Lidar-based tools can undoubtedly reduce human error and aid in a safer, faster, and more accurate inspection that generates data that is accessible online. Furthermore, UAVs can perform repeated inspections even in challenging and dynamic environments, making it easier to monitor infrastructural changes over time.

Drones are also used in other aspects of the environment. Drones, for example, can easily monitor large swaths of territory, which means there is less impact on the area because no people have to make the trek. Even better, drones provide an aerial bird’s-eye view, so problems that would not be visible from the ground will be obvious.

Numerous trends are influencing the future of autonomous UAV inspection. One advancement will be the use of UAV swarms for inspection tasks, in which UAVs will be dynamically coordinated to interact while in flight. Furthermore, we can anticipate an improvement in the performance of unmanned aerial vehicles (UAVs) that can navigate autonomously in denied environments. This will lead to new opportunities in the industrial sector.

In contrast, new business opportunities will also arise as exploration techniques improve and developments in automated scene understanding are made possible by deep learning techniques used onboard or in the cloud. Another possible development will be the use of augmented and virtual reality in self-driving inspections.


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