Manufacturers today are recognizing the significant benefits that autonomous mobile robots can offer. AMRs can automate repetitive and injury-prone material transportation without requiring expensive and disruptive reconfiguring of their infrastructure. Conventional logistics solutions like forklifts and conveyor belts — as well as traditional automated guided vehicles — haven’t allowed this level of flexibility or adaptability. Manufacturers and logistics companies trying to accommodate ever-changing customer demands will have difficulty if they rely solely on autonomous guided vehicles and other systems.
As a result, manufacturers satisfied with one mobile robot have started to implement multiple AMRs in hopes of expanding them to internal logistics applications they hadn’t realized could benefit from their use.
To facilitate the process, they are using fleet management software, which offers centralized control of the robots from a single station. The most advanced tools are able to eliminate any bottlenecks and downtime with 24/7 mobile robot operation.
Once an AMR is programmed, the fleet management system manages the priorities and selects the most suitable robot to the operation that needs to be carried out, based on position, availability, and top module.
The system also monitors robot battery levels and automatically manages recharging. In addition, it can control the robots’ traffic patterns by coordinating critical zones with multiple robot intersections. Sounds smart, right?
These robots and the fleet management software that manage them are becoming even smarter with new artificial intelligence capabilities, coupled with strategically placed cameras that function as an extended set of sensors.
Today, mobile robots use sensors and software both for control (to define where and how the robot should move) and perception (to allow the robot to understand and react to its surroundings). Data comes from integrated laser scanners, 3D cameras, accelerometers, gyroscopes, wheel encoders, and more to produce the most efficient decisions for each situation.
These technologies give AMRs many of the capabilities similar to those being developed for automobiles. Mobile robots are able to dynamically navigate using the most efficient routes, have environmental awareness so they can avoid obstacles or people in their paths, and can automatically charge when needed.
How AI is changing the game
Without AI, however, the robots react the same way to all moving obstacles, slowing and attempting to navigate around the person or object if possible, or stopping or backing up if there is no safe way to maneuver around it. The AMR’s standard approach is appropriate for many situations, but just as AI is powering new capabilities for self-driving cars and intelligent drones, it is also poised to dramatically change robotics.
AI for collaborative robots today is focused primarily on machine learning and vision systems, which are dramatically extending earlier sensor-based capabilities.
In addition, AMRs are benefiting from advances such as smaller and more powerful sensors, cloud computing with broadband wireless
communications, and new AI-focused processor architectures. These advances are widely available at lower costs, making it easier than ever to pull data from a robot’s immediate, extended, and anticipated environment, as well as internal conditions.
Using these capabilities, fleets of robots are able to learn while they are online, like a group of online students, and then perform without constant access to online content. Low-power, AI-capable devices, and efficient machine learning techniques support new robotic systems with low latency and fast reaction times, high autonomy, and low power consumption — all key elements for success.
AI improves AMR path planning, environmental interaction
The new AI capabilities in mobile robots help maintain the robots’ safety protocols and drive improved efficiency in path planning and environmental interaction.
With MiR’s MiRFleet, for example, new advanced learning algorithms are implemented in the robot’s software as well as in remote, connected cameras that can be mounted in high-traffic areas or in the paths of fork trucks or other automated vehicles. The cameras are equipped with small, efficient embedded computers that can process anonymized data and run sophisticated analysis software to identify whether objects in the area are humans, forklifts, or other mobile devices, such as autonomous guided vehicles.
The cameras then feed this information to the robot, extending the robot’s understanding of its surroundings so it can adapt its behavior appropriately, even before it enters an area. The AI-capable network helps the robot avoid high-traffic areas during specific times, such as when goods are regularly delivered and transferred by fork truck, or when large crowds of workers are present, such as during breaks or shift changes.
For example, the robots will continue driving as usual if they detect a person but will park if they detect an AGV so the AGV can drive by. The robot can also predict blocked areas or highly trafficked areas in advance and re-route instead of entering the blocked area and then rerouting.
While the robots’ built-in safety mechanisms will stop it from colliding with an object, person, or vehicle in its path, other vehicles like forklifts may not have those capabilities, leaving the risk of one of them running into the robot.
With the AI-powered AMRs able to detect high-traffic areas before they arrive and identify other vehicles and behave appropriately to decrease the risk of collision, they are improving their own behavior and adapting to other vehicles’ limitations. With these smarter and more collaborative mobile robots, manufacturers should find it even easier to optimize the transportation of all types of materials as they strive to succeed on a global scale.
About the author:
Ed Mullen is the vice president of sales in the Americas at Mobile Industrial Robots. The Danish manufacturer of collaborative autonomous mobile robots recently launched its MiR1000, along with the industry’s first AI-based navigation capabilities for its entire fleet of mobile robots.
Filed Under: Student programs, AI • machine learning, The Robot Report, Robotics • robotic grippers • end effectors