Combining locating systems for a more accurate positioning
Combining locating systems for a more accurate positioning
The media often talk about autonomous vehicles. However, it is obvious that driverless cars won’t take over our streets anytime soon. What is talked about a lot less, is that in industrial environments, autonomous applications have been integrated a lot more already. AGVs or Automated Guided Vehicles can execute different tasks in a production environment: transport, stock-taking, locating, etc. For this, they need to know where they are, where they want to go and what is the best route – taking into account other static or moving obstacles on the way.
The design and calibration of the positioning systems of those AGVs is therefore crucial. And certainly not obvious. Indeed, AGVs are usually deployed for indoor applications. GPS tracking, which would work outdoors, is not sufficient indoors. The tracking systems have three important functions:
- Keeping the vehicle on the right track, taking into account the environment, other (autonomous) vehicles and objects and/or persons.
- Establishing the most efficient route and distributing the tasks between the different vehicles in a fleet.
- Preventing collisions with other vehicles, objects and/or persons.
Slow, expensive and inaccurate
Unfortunately, the existing methods on which positioning systems are based do not meet industrial requirements for accuracy, update speed, robustness and cost. Therefore, Flanders Make partnered up with industrial businesses such as Bekaert and Octinion to develop an innovative approach combining different existing positioning techniques, i.e.:
- UWB (ultra-wide-band) sensors
- Accelerometers and gyroscopes
- Visual positioning using cameras
Depending on the application and preconditions such as cost, environment, scalability, etc., several combinations are possible. Therefore, a design framework was developed that makes it possible to carry out a quantitative comparison of the techniques in a relatively simple way. This enables developers to rapidly simulate the impact of adding/removing a certain technique or of adapting a series of parameters. As a result, the optimal combination can be established a lot faster.
