Baggage make-up is one of the most pressured resources in the baggage handling process, with multiple flights requiring make-up at a given time, while all of this takes place within the same busy make-up area. As already tight resources face increasing challenges from predicted passenger growth and emerging staff health and safety regulations, it is becoming clear that innovation is needed in this area.
As the work required in the make-up hall is largely physical and the space is tight, there is a need to rethink how the process works to cope with the demand level. Advances in technology have meant that there is now a promising solution at hand: robotic loading devices.
Although robots have been trialled in baggage handling previously, the recent advances in technology, combined with the constraints on physical labour, have resulted in them being a feasible reality, rather than a hypothetical future.
Robot solutions have been variably successful in trials previously, due to a high rate of errors in their work, which has had a knock-on impact of a lack of trust in the technology. Understandably, airports have not wanted to invest in solutions with a low rate of accuracy that require significant assistance to perform their tasks.
However, this has changed significantly due to multiple advances in technology that impact various parts of the robots, meaning that they have a higher level of accuracy and efficiency in given tasks. Firstly, the advances in computer programming and artificial intelligence have meant that the computation behind the robots is much faster, smarter and more accurate. This means the robots have less chance of error and function in a better manner.
Secondly, the technology required for various parts of the robots has also improved. One example of this is that the camera vision technology inside the robots is more advanced, meaning that it can recognise both baggage and surrounding objects more clearly, making it less prone to errors. The sensor technology combines a camera with computer algorithms and AI to function to a level of sophistication that was not previously possible. As a result of this, robots can stack more efficiently, segment the bags and even orient handles in the right direction.
Similarly, the gripper technology for the arms of the robot used to be problematic and prone to difficulties. Now, it has advanced to a level that matches the actual operational requirements.
As the rate of technological advance, particularly in AI, continues, things that were impossible a year ago are commonplace today, meaning that it is time for robotic loaders to be considered by those working in baggage make-up.
Traditionally, baggage make-up is physically strenuous work, with manual labour needed to pick up and stack every single item of baggage as it goes from the baggage handling system onto the correct ULD or trolley that transports it to the aircraft. The process requires a significant amount of walking and lifting within a tight time period.
Innovations to help this before have included semi-automated solutions, in which a joystick-controlled operator moves the ramp to deliver each bag to the next position. This minimises the physical load that is required, but is no longer the extent of how automation can assist make-up.
Robot loading takes the place of the semi-automated aid by transporting the bags without the need for human intervention. Following on from the sorting that occurs as part of batch building, a robot loader automates the loading of bags into ULDs or carts and does this in an accurate and high-speed manner. Essentially, robotic loaders offer the possibility of providing fully automatic loading from the receiving conveyor onto the ULD or trolley that will then be transported to the aircraft.
Why is robot loading a good solution for make-up difficulties?
Airports are facing difficulties in baggage handling, which are only increasing. As new laws come into force relating to health and safety, the time frames within which baggage handlers can work and the amount of work that is possible within them become much more pressured. This comes on top of a declining workforce, high staff turnover and airports that are only set to become busier.
Baggage needs to be transported quickly and efficiently for each flight, and the limits of what can be achieved with human means and linear scaling seem to have been reached. Innovative airports are rethinking their processes, adding automation and digitalisation in a way that offers them greater control and efficiency.
Robot loaders require just one operator per three build cells (or stations where the bags are loaded), offering a significant improvement over the semi-automated set-up, which still requires one operator per build cell, or manual loading, which requires the same.
Robots were very expensive some years ago, but as the technology has evolved and the demand has increased, their cost has decreased.
Today, the payback period is only a few years. In part, this is due to the cost of labour and the rise in health and safety regulations, which have increased the cost of manual labour and made a robot a cost-effective solution. Many airports will also consider the savings that come with not having to expand the make-up area physically as part of the calculation of what the robots can help save on.
Beyond the initial cost of the robot are the internal benefits that are provided. The baggage operators’ job will change, including upskilling to monitor robot activities and the ability to focus on the bigger perspective of baggage logistics, offering yet more benefits.
Robot loaders provide significant benefits and also represent a shift in airport baggage handling. Where before, an isolated product has been looked at as a solution to challenges in baggage make-up, robot loaders are part of an ecosystem, providing benefits that are future-proof and able to adapt as airports scale operations up or down.
For this reason, they are most successful when trials include the operators, who understand not just how to use the robots but exactly where and how they make a difference and how to manage them around different scenarios – both now and for the future.
