2018 marked the second key milestone for the industry, by which time most independent mobile robot manufacturers had developed one or two mature products. They then began selecting various industries and scenarios for project promotion and implementation. While promoting and implementing these projects, they accumulated experience, improved and iterated their products, and sought the most suitable application scenarios for their robots.
Now in 2020, after the implementation of numerous autonomous mobile robot projects over the past two years, autonomous mobile robots have become widely known. Different robot manufacturers have also chosen different focuses in upgrading their mobile platforms. So, what aspects should we pay attention to regarding these fleets of robots traversing industrial settings?
The hardware quality of a mobile robot plays a decisive role in its overall quality. However, currently, most robot chassis components are outsourced to robot manufacturers, so this article will not discuss the impact of hardware on autonomous mobile robots. Instead, it will focus on the software aspect to explore the key factors influencing the competitiveness of AMR robot fleets.
1. Navigation capabilities
Whether using laser SLAM or V-SLAM, the autonomous navigation algorithms of various robot manufacturers each have their own strengths and weaknesses from a technical perspective. Some excel in high repeatability, while others excel in smoothness through clever use of soft guidance. However, from the customer's perspective, the differences are no longer significant. Especially after the intense market promotion in 2019 and 2020, the smoothness, freedom, and obstacle avoidance flexibility of the basic mobile chassis in the AMR industry have become largely similar in terms of their overall performance and perceived ease of use.
Demonstrating machine chassis performance in standalone mode without the need for a scheduling system is something almost all manufacturers can do seamlessly nowadays.
The precise docking stage during secondary local positioning is a crucial test of navigation algorithms. Many robotics companies require additional methods to achieve precise docking at this stage, as they cannot rely solely on lasers and navigation algorithms to achieve near-millimeter-level docking. In such cases, robot manufacturers can only assist robot docking by using physical beacons such as reflectors, QR codes, or magnetic strips. This scenario is relatively rare in the warehousing field, and is more common in a few manufacturing scenarios requiring precise workstation docking. The number of these auxiliary beacons used reflects the manufacturer's current capabilities in navigation algorithms. Manufacturers with outstanding navigation capabilities can achieve high-precision docking in such scenarios with only a few beacons.
In practical applications, regardless of whether the navigation method is visual, laser, or software-guided, as long as the machine can perform smooth business operations in the scenario and has a low failure rate, it will be more easily accepted by customers. With navigation technologies no longer showing significant differences, the ability to implement solutions in business scenarios has become the key competitive advantage that differentiates mobile robot manufacturers.
2. Scheduling System
As the core of all mobile robot fleets, the scheduling system plays a crucial role in scenarios where multiple AMRs operate simultaneously.
A dispatching system, also known as a dispatch management system, is generally divided into four main parts: dispatching services, system management, task management, and traffic control. It connects autonomous mobile robots with the customer's internal logistics management system. Whether a fleet of robots can operate as required in a real-world scenario, and whether it can operate in an orderly manner, depends on the strength of the dispatching system possessed by the robot manufacturers.
As the traffic command center for a fleet of robots, the traffic control system is particularly important. Developing a complete scheduling system is no faster than developing navigation algorithms. As mentioned above, almost all manufacturers can currently demonstrate the performance of a single robot chassis smoothly without the need for a scheduling system. However, when multiple robots begin operating in the same scenario, not every robot manufacturer can achieve such smooth operation.
When two or more vehicles meet on the same path at the same intersection, determining vehicle passability, priority levels, and response times becomes a crucial issue that traffic control systems must coordinate. In this common multi-vehicle operation scenario, even a slight delay in the dispatch system's response time can result in noticeable pauses in operations. Furthermore, a flawed dispatch system can lead to chaotic vehicle movement, collisions, and loss of obstacle avoidance capabilities—all potentially serious consequences.
In the competition among robot fleets, the scheduling system will be of paramount importance.
The AMR industry has begun a fierce battle for market share, and we shall wait and see which company's robot fleet will emerge victorious.
