It is easy to understand how traditional automated guided vehicles (AGVs) work: they have downward-facing sensors that follow physical markings (like tags or magnetic tape) on the floor However, more modern systems, and AMRs, based on newer navigation technologies such as natural navigation, laser triangulation or vision guidance follow virtual routes.
So, how is this achieved? How do these types of autonomous navigation technology really work?
It’s not magic. Or rocket science. When it comes to mobile robotics, a vehicle’s onboard autonomous navigation technology must know three things:
- Where the vehicle is (its precise current position, also called vehicle ‘localization’)
- Where the vehicle needs to go (its routes) and what to do when it gets there (actions)
- How to get there (vehicle control)
Let’s explore these three requirements in more detail:
1. Where am I?
For an AGV or AMR to be able to navigate effectively, it must first know exactly where it is in the environment. Highly precise positioning data (X, Y coordinates) are essential.
In other words, a vehicle must understand its position with respect to the map of the site it will use to get around. This map might be stored in its onboard computer (as is the case with ANT navigation), or stored server-side (requiring a strong and consistent wireless communication link).
- Natural navigation (also called: free navigation, contour navigation, SLAM navigation, or simply mapping navigation)
- Laser triangulation (also called laser navigation or laser guidance)
- Vision guidance (also called optical navigation)
2. Where I am going (and what should I do when I get there)?
Once the navigation system knows where a vehicle is, the AGV/AMR is then able to move along its pre-defined routes and perform pre-defined ‘actions’ at set points. These routes and action are programmed using the vehicle’s configuration software (such as ANT lab) by the vehicle’s integrator. This may be the vehicle maker or a separate third-party.
To take the example of ANT lab, routes are defined in this software by placing dots (nodes) in the map and then linking these together to create routes. When a specific action is needed, such as stopping the vehicle or raising its forks, the required parameters are programmed into the node. On arriving at this node (or possibly just before/after), the vehicle performs this action as programmed.
3. How do I get there?
Once a vehicle knows its route and what actions to perform along the way, its control system comes into play. For the vehicle to move and perform its assigned tasks correctly, its speed, trajectories and so on must be carefully controlled.
In the case of ANT autonomous navigation technology, this system can control the vehicle in addition to providing localization. The AGV/AMR integrator needs only to define the vehicle’s maximum speed. Thereafter, its specific speed(s) during operation – while making a turn for example – is automatically calculated by the ANT system. The aim always being to optimize the vehicle’s arrival time, while ensuring its safety and the stability of its payload.
Alternatively, AGVs/AMRs may work in response to a signal in their environment. For example, a sensor may indicate a pallet has reached the end of a conveyor, triggering a vehicle to pick it up. With the routes, actions and speed successfully programmed, the vehicle is ready to roll. Vehicles may operate at regular, predefined intervals, or their missions may be manually called by a human operator.
Dealing with obstacles
A common question when it comes to vehicle control is: what happens if there is an obstruction to the robot’s path? For example, a pallet dropped on the floor, or another vehicle parked in the way.
With AGVs and AMRs, one of two navigation approaches is employed: either ‘path following’ (the vehicle stops and waits for someone to remove the blockage), or ‘obstacle avoidance’ (the vehicle moves dynamically around the blockage before returning to its original path).
In most cases, vehicles used in industrial settings (such as automated guided vehicles (AGVs) and automated forklifts) employ a path following approach. This helps ensure on-site safety since the vehicles literally stay in their virtual lanes. Plus, path following is usually – perhaps counterintuitively – the more efficient choice. By contrast, obstacle avoidance is used more in AMR applications.
Learn more about autonomous navigation technology
If you still have questions about what guides an AGV/AMR, or the different types of autonomous navigation in use today, you might find this pros and cons article useful. Alternatively, feel free to get in touch with our expert team.