“Robotics, AVs and AI will combine to help make our Baggage 4.0 vision a reality in the future”
Sven Platschorre
Director of Airports Solutions
Labour shortages, rising operational costs and the need to be more sustainable are just three of the challenges facing airports today. The opportunity to run a fully automated baggage handling process could help operators cope with these issues. One of the keys to unlocking such levels of seamless automation – what we call Baggage 4.0 – is to integrate different technologies, including robotics, AVs and AI.
Robotic technologies capable of performing traditionally manual and repetitive tasks already exist, for example for the loading of bags in ULDs in the make-up area. However, today’s systems are limited, as their perception technology only “sees” simply shaped bags and their programming does not allow them to act as effectively as humans – yet.
Improved capabilities
A wide range of bag types pass through airports that robots must be able to recognise and handle – from suitcases to duffel bags. This variety has driven us to further develop our robotic solutions, which will also play a key role in the creation of our next generation of baggage handling.
Advances in perception techniques have allowed us to create robotic work cells with a more accurate understanding of different bag types, through the accurate portrayal of shape and characteristics (like hard case, duffel bag, etc.) . In turn, this allows the robot to make more informed decisions about where to accurately place a bag. Alongside significant technological advancements in the robotic gripper, which allows for precise bag positioning, the integration of the recognition software delivers multiple benefits, including a higher fill rate of carriers, less operator interventions and increased operational capacity.
With robotic solutions in place, one operator would be able to supervise multiple stations, reducing the dependency on people to manually lift bags – while creating a more engaging and challenging job role. We believe that robotics will increase the productivity per square metre, while allowing a site to balance the workload of the robot according to fluctuations in demand.
Completing the baggage journey with AVs
As we can see from various trials of driverless vehicles now being undertaken on public roads, AV technologies are becoming more sophisticated. They have a role to play at airports, storing and transporting ULDs and carts within the baggage hall and to the aircraft and back again.
AVs also offer airports so much operational flexibility and scalability because their deployment can be tailored to meet changing baggage demands. On busy days, more vehicles can be put into service, and then stood down during quieter periods. They can also be put to work to deal with big events and support major system overhauls.
Combining AVs with other technologies can deliver value and efficiency. For example,
as point-to-point flows are managed by BHS conveyors, AVs can be used to help with sortation or buffering. And to complete the automation of the ground handling process, the robotic solutions already mentioned can take what the AVs bring them to the aircraft, helping to further optimise the last mile in baggage flow.
At Vanderlande, we use AV and robotic solutions with sustainability in mind, paying attention to issues such as ergonomics, safety and energy consumption. In addition, our AVs can be remanufactured and recycled, which contributes to building a circular economy.
AI and predictive maintenance
In our previous blogs, we discussed predictive maintenance and anomaly detection – and this is where AI can also be effectively implemented to optimise an airport’s BHS. AI’s ability to detect and recognise the outcome of patterns goes hand in hand with the predictability requirement of our Baggage 4.0 vision. And by integrating AI software with modern robotic work cells and other automation processes, we can offer a system that will optimise the efficiency of airport operations.
The same principle can be applied to routing within a BHS. AI can be trained to detect optimal routes through pattern learning, improving the efficiency of the system. Similarly, it can also be used to recognise specific luggage.
With advanced computer system, an AI model will be able to recognise a bag, identify its attributes, tell us where in the BHS it is, and even detail its orientation. This technology will also be able to detect when bags are too close together.
Therefore, robotics, AVs and AI will combine to help make our Baggage 4.0 vision a reality in the future – while also tackling key issues like labour scarcity, sustainability, BHS downtime, market instability and operational cost management.