MOV.ai disrupts Autonomous Mobile Robot development with a Robotics Engine Platform that contains everything needed to quickly build, deploy and operate intelligent robots.
The global market for Autonomous Mobile Robots (AMRs) is expected to reach over $10.5 billion by 2028. Mobile robots are no longer a technological curiosity you’d see at a futuristic trade show; they’re an integral part of production and distribution processes.
But in order to perform complex tasks efficiently and deliver tangible value to the business, AMRs need to interact with the entire environment.
Interoperability is the key to successful AMR deployment. This term refers to the capacity of mobile robots to interoperate and collaborate with both robotic and human systems in factories.
As the number of AMRs in use grows, along with the number of automation processes, there will be more touchpoints between robots within a system, and sooner or later, companies will run into trouble if they don’t address the need for interoperability.
You can enable interoperability by building a system that allows you to:
Let’s dive into each of these aspects to understand how businesses can future-proof their AMR setups for flexibility and scalability.
Technology advances and reductions in price mean a growing number of processes are automated using mobile robots. In an industrial environment, such automation involves numerous systems that need to be integrated. These include other automation technologies such as conveyor belts, elevators, scanners, robots, and management systems like ERP, WMS, fleet management systems, or Automation Management Systems.
To address the need for different systems to work in unison, various protocols such as:
More and more protocols are utilized to connect and allow mobile robot systems to communicate with their environment; but even when using one of these protocols, challenges may arise due to the different system makes and the languages and protocols used, which can make seamless integration difficult.
Enabling open integration through one of these is crucial to ensure the mobile robot system can communicate effectively, not only now but also in the future.
Integration is a complicated and time-consuming process. Leading robot manufacturers understand that black-box AMR systems are no longer an option. Open AMRs that are future-proof come equipped with an easy way to enable project-specific integrations. This includes:
Here is an example of how to integrate an AMR with an elevator, in order to allow it to move autonomously in a multi-storied warehouse, using a visual ROS IDE and open interfaces:
Here’s another common integration challenge for autonomous pallet movers. Each package has a barcode on it. When the packages are dropped, a machine scans the whole pallet to check its contents. This information helps the company track items for transportation.
These two examples demonstrate how the integration works. Once the interaction with external systems is finalized, it can be reused independently of the robot’s type and functionality. This means that any robot that requires this type of integration already has the boilerplate work done and minimal tweaks are needed to add support for additional systems.
The more robots we deploy on factory floors, the more touchpoints they’ll have with humans. Robots that operate in heterogeneous environments (which are the majority) need to not just work safely alongside robots but to work collaboratively. One of the key drivers behind robot deployments is to augment human work, not replace it. To do that, robots need to communicate with humans on an ad hoc basis. It’s about working with people and executing tasks on demand, not just pre-planned fully automated models.
To work efficiently and safely in the presence of a human operator, mobile robots need to be equipped with:
Let’s consider an example scenario in an automotive production line where an operator needs a red steering wheel and a red wheel. The operator uses a pre-programmed user interface, which is part of its AMR management system. It is customized to present relevant options for the specific station.
The order is sent directly to the robot management system, which processes and relays it to the fleet. The system takes into account various factors such as robot proximity, battery level, traffic congestion, or even barred areas due to human traffic at lunchtime. Based on these factors, the mobile robot autonomously calculates the optimal route and proceeds to the first picking point.
The operator sees the request for a red wheel displayed on the robot’s interface, places the required item in the appropriate place, and marks the item as obtained through the interface
Following the input, the robot moves on to the next mission or picking point.
Once all the required items have been gathered, the robot returns to the production line and delivers the materials to the operator who initially called for them, completing the task. You need to have the possibility to create the interfaces in all the systems and the robots. The scenarios and the systems will evolve with time, With the growing implementation of robots and automation creates more touch points between humans and robots. In every such touch point, there is a need for a way to communicate in order to allow the added value above just mere basic transport.
For safety purposes, AMRs need to be able to detect and avoid people. If a human worker crosses the robot’s path, the robot needs to overcome the obstacle by moving to a different side and changing the path. Note that obstacles can be fixed or dynamic.
Another important aspect is the ability to deal with dynamic obstacles created by people. Some robots can only follow a preprogrammed trajectory and stop whenever they encounter obstacles. Others can figure out the best route considering all the obstacles along the way from point A to point B.
Facility workers may not know what the robot is expected to do or how it operates. The more information we provide them on what the robot is doing and what it will do next, the smoother and safer the collaboration between robots and humans will be. Calling a robot to perform a task is like calling an Uber—the human operator can be confident that the robot is coming to carry out the task.
Installing different types of robots is becoming easier, and as facility operators experience the benefits, the natural progression is to introduce more AMRs to perform more tasks.
A cart tugger, an autonomous forklift, and a floor scrubber may all operate in the same space.
The question is how to manage different AMR fleets from different vendors, with different logic inside. This raises challenges such as:
This is where interoperability comes in. It allows all the robots to communicate within a single management system. There are several ways to achieve interoperability and quite a few things that robots and management systems need to support in order to side-by-side without issues.
The following example demonstrates how heterogeneous fleet management can work from a traffic management perspective, specifically, to prevent traffic conflicts.
The adoption of AMRs has progressed during the past few years to reach multiple at-scale deployments we can today see across various sectors. To fully benefit from implementing mobile robots in a facility, companies must seamlessly integrate them with other systems, devices, and humans. This needs to be done with scalability and flexibility in mind.
To achieve interoperability and collaboration, there are certain capabilities the robots need to have, along with tools to define the changing interaction between AMRs and their environment.
By building these capabilities into your AMRs and fleet management systems, you’ll be on your way to future-proofing your AMR.
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