The intralogistics operating system by SYNAOS offers unified control and optimization of intralogistics. This increasingly involves controlling fleets of self-driving transport units. A typical fleet includes AGVs and AMRs – but what is the actual difference between these two vehicle types?
In logistics centers and production halls, there are always a lot of pallets, crates, mesh boxes, racks and numerous other objects that must be transported. This task can be accomplished by forklifts with human operators behind the steering wheel. Increasingly, driverless transport systems (DTS) are being used to move goods autonomously from A to B.
These driverless transport vehicles include Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs). Although they both accomplish the same tasks, these abbreviations should not be used synonymously: the two vehicle types are different and each of them has specific characteristics.
The A in AGV stands for Automated, while the A in AMR stands for Autonomous: a small difference with major significance. As the name suggests, AMRs operate autonomously, for instance by evading obstacles that suddenly block their path. On the other hand, AGVs travel on fixed routes and can only accomplish pre-defined tasks by following automated instructions. In contrast, AMRs make their own decisions when a situation requires.
Follow the magnetic strip!
The first driverless vehicles introduced in the 60s found their way using photoelectric control, that is, a colored strip on the floor that they followed stubbornly. Nowadays, the control of AGVs is also accomplished inductively or magnetically, for example using magnetic strips that are laid out on hall floors. They are cost-effective and very simple to set up, and this method has proven itself over many years. Nevertheless, subsequent changes to routes generate a certain expense, since the magnetic strips have to be repositioned, which limits the flexibility of the driving routes. The repair of these guiding lines also involves a greater expense.
AGVs are controlled by software to move material through halls without accidents. Sensors act as their “eyes and ears” in the process. The vehicles also require physical control elements that guide them along their routes – for instance, the magnetic strips already mentioned, or individual magnets and transponders for grid navigation. With laser navigation and guidance using virtual guiding lines, reflector marks are required as reference points in the environment. These are attached to walls or columns, for example, and reflect the incoming laser light back to the sensor. The vehicle can determine its current position using triangulation.
AGVs are pre-programmed and always follow fixed routes. They detect obstacles using LiDAR sensors: If they detect a worker who is suddenly in the vehicle’s path, they stop and wait until the path is free again. Since AGVs only travel on pre-defined routes, they cannot simply drive around the obstacle. If a pallet falls on the path, the vehicle will stop and wait until an employee moves the obstacle out of the way. Only then will the route continue.
Such disruptions extend transport times and can also have a far-reaching impact on production: For example, if an urgently required component does not arrive at the production line on time, an expensive production shutdown may result. SYNA.OS LOGISTICS – the intralogistics operating system by SYNAOS – helps to react quickly to unforeseen events: As soon as a route is blocked, the software finds another path and redirects AGVs in time.
Free navigation for AMRs
AMRs are considered a technical enhancement of AGVs. They represent more or less the next stage of evolution among driverless transport vehicles. Mobile robots operate with their own algorithms to find the right route. They do not require magnetic guiding strips on the floor, permanently attached reflectors or stationary transponders. Instead, AMRs rely on their own sensors, computer vision and their own software. As a result, their driving routes are more dynamic and flexible than AGV routes.
Due to their capabilities, AMRs can handle a wide range of tasks. They are particularly suited for dynamic environments in warehouses or factories. The vehicles are capable of making decisions autonomously and selecting the best solution for the current situation out of a variety of potential options. AMRs receive their orders – like AGVs – from a master control system or fleet manager.
Through machine learning, AMRs become increasingly better at fulfilling their orders rapidly. They get to know their environment and can reach almost any corner in a factory or warehouse. They operate more efficiently and accurately than AGVs and are also more flexible if problems occur or unexpected situations arise. But the vehicle computer has to be powerful to do this, because they have to process a large volume of data in real time that is delivered by their sensors.
If a person or object blocks their path, AMRs react more dynamically than AGVs: they also stop first, but then drive around the obstacle and seek out a new path. Fast re-routing saves time and reduces disruptions to the process. Human intervention is not required.
It all depends on the software
Different levels of autonomy are possible. This also depends on the installed camera and sensor technology and their capacity to detect the spatial conditions. Of course, the quality of the software and algorithms is also an important factor: Ultimately, they have to solve difficult tasks and perform repeatedly.
A digital indoor map of the environment stored in the vehicle facilitates navigation in the operational environment. The AMR can also generate this map completely independently by carrying out orientation routes. This form of localization and mapping is referred to as SLAM (Simultaneous Localization and Mapping). The SLAM algorithms also process deviations in the real world and compare them with the stored map information and make adjustments. So when a new shelf is set up in a warehouse, the AMR adds this to its map.
Summed up again
In general, AMRs can be seen as an enhancement of AGVs. Because these vehicles have a higher degree of autonomy, they are more flexible to use. Some manufacturers view SLAM navigation as a unique characteristic of AMRs. However, the reality is that not all vehicles can be clearly classified into either category – the available options are too fragmented. In some places, the abbreviation AGV has become established as a general overarching term that also includes AMRs or LGVs (Laser Guided Vehicles).
One thing is certain, though, whether AGV or AMR and all forms in between: Crucial for success is the choice of the right higher-level control and optimization software! With its hardware-independent design, SYNA.OS LOGISTICS is per DNA predestined for this complex challenge. The OS monitors, controls and optimizes everything in intralogistics – AGVs, AMRs, forklifts and order pickers. The algorithms distribute pending orders holistically across all vehicles, enabling them to process the entire pool much more efficiently. SYNAOS is making the difference.