Use of AIoT & LiDAR in the marine industry

When it comes to the adoption of innovative technology, the maritime industry has long been an embodiment of the phrase “if it isn’t broken, don’t fix it”. However, both the unexpected global logistics crisis that followed the COVID pandemic and the improvement, on both cost and performance, that innovative technology has had on modern service processes in other industries have shown that the maritime industry needs innovation, fast. Perhaps, it shouldn’t be surprising that within the last 3 years, thousands of teams and start-ups have sprung up aiming to revolutionize maritime processes, with 95 percent of these efforts related to sensor technologies. So, what happens when you connect the best sensors with high-speed connectivity and pair them with highly accurate AI models to build maritime industry specific solutions? This is the question we ache to answer.

Traditional sensors implemented in these solutions are often miniature condition scanners, RFID tags, and camera systems, but a new type of sensor has emerged. LiDAR technology, which stands for Light Detection and Ranging, uses lasers to instantly generate 3D models of the environment it is placed in with extremely high degrees of accuracy up to a range of 600 meters. These LiDAR scanners provide data about the sizes and positions of objects in the real world in an intuitive way, allowing for automation and safety improvements, even at night, while protecting the privacy of people around them. LiDAR is already extensively used in self driving cars, and the total available market for the technology is projected to grow to 13 billion USD by 2030.

It is easy to see why these sensors should play a major role in reshaping the maritime industry, because when these devices are interconnected and paired up with artificial intelligence, innovative maritime use cases emerge. These use cases can be spread across three broad categories: Shipping Operations, Repair and Maintenance, and Safety.

Shipping Operations

In the first category, the combination of Internet of Things (IoT) devices and sensors, AI, and LiDAR has the potential to yield significant improvement to the energy and time efficiency of shipping operations, as well as improve security and supply chain visibility. In energy and time optimization, fuel consumption hardware systems can be implemented alongside deep learning models in order to track spikes in fuel consumption, isolate sources of wastage, and predict ways to improve fuel efficiency. Also, LiDAR scanners can be installed on ports to enhance the operation of rubber-tired gantry cranes and other container loading and unloading processes. This can be achieved by accurately measuring sizes and locations of containers and utilizing that data in AI models to suggest optimal schedules and configurations for loading, offloading, and stacking cargo. Furthermore, these technology mergers form the backbone for autonomous maritime navigation, both in seafaring vessels and in port terminal vehicles.

Repairs, Maintenance, and Insurance

Another category of use cases where these technologies can be implemented is in ship building, repairs, maintenance, and insurance. 3D scans generated by LiDAR sensors create the perfect solution for the endless hours of downtime spent on conducting scheduled checks and maintenance. These checks, which require the affected vessels to be taken off duty, costing enormous amounts of money in the process, can now be performed remotely, using millimeter accurate 3D models of the vessels. These regularly updated 3D models, when paired with AI systems, can provide information on structurally unstable or unsafe components. They can also make insurance claim processes easier because equipment damage can be tracked by inspecting 3D models over time. Also, IoT devices in critical equipment components can feed data back to AI advisory systems which, in turn, predict and warn against equipment failures and hazards.

Health and Safety

One more area of potential AI IoT LiDAR implementation is In improving safety, health, and incident management in the maritime industry.

Safety:

Since more than 75% of reported worldwide shipping incidents are found to be caused by human error, an obvious implementation of these technologies would be to prevent collisions between vessels in the open sea. However, these technology mergers may prove even more useful in preventing accidents when ships are transiting through port fairways or moving under bridges. Port fairways, which are notoriously tight with large amounts traffic, pose very high risks to navigating vessels. These risks can be reduced by implementing LiDAR systems to prevent ships from running into port infrastructure, equipment, and other vessels. Also, accurate measurements from these systems can warn a vessel attempting to pass under a bridge if its height exceeds safe limits.

Health:

Small scale wearables can help monitor health status of seamen and send out beacons during health emergencies or search and rescue operations.

Incident Management:

Implementation of these technologies also ensures that a steady stream of data concerning each vessel or equipment is constantly collected and stored in the cloud. This vast amount of data will prove invaluable when investigating incidents and performing large scale analysis.

Conclusion

The fusion of IoT (interconnected sensor technologies, cloud computing, big data analytics), artificial intelligence, and LiDAR will drive innovation in the maritime industries. The unique characteristics of each technology, put together, make them collectively fit for the industry.

Listed above are only a few of the multiple possible scenarios where this technology merger could improve performance, energy and time optimisation, safety, and cost efficiency in the maritime industry.