Abstract
Autonomous mobile robots (AMRs) are increasingly used in modern intralogistics systems as complexity and performance requirements become more stringent. One way to increase performance is to improve the operation and cooperation of multiple robots in their shared environment. The paper addresses these problems with a method for off-line route planning and on-line route execution. In the proposed approach, pre-computation of routes for frequent pick-up and drop-off locations limits the movements of AMRs to avoid conflict situations between them. The paper proposes a reinforcement learning approach where an agent builds the routes on a given layout while being rewarded according to different criteria based on the desired characteristics of the system. The results show that the proposed approach performs better in terms of throughput and reliability than the commonly used shortest-path-based approach for a large number of AMRs operating in the system. The use of the proposed approach is recommended when the need for high throughput requires the operation of a relatively large number of AMRs in relation to the size of the space in which the robots operate.
Highlights
Autonomous mobile robots (AMRs) are increasingly being used in modern intralogistics systems instead of autonomously guided vehicles (AGVs)
The results show that the proposed approach performs better in terms of throughput and reliability than the commonly used shortest path-based approach in systems with a higher number of AMRs
Another aspect of performance is the scalability of a multiAMR system, which could be defined as the number of robots in the system without serious conflicts between the robots that would prevent the completion of the given tasks
Summary
Autonomous mobile robots (AMRs) are increasingly being used in modern intralogistics systems instead of autonomously guided vehicles (AGVs). They are often used when additional flexibility is required in cargo transportation tasks, since AMRs, unlike AGVs, are capable of moving in free space without additional floor markings such as painted lines or magnetic tapes Even though they can move freely, it is often necessary to restrict the areas in which they are allowed to move in order to improve the safety and efficiency of their operation. The routes should be short, and on the other, they should intersect as little as possible, since intersections are sources of lost efficiency due to vehicle encounters requiring evasive manoeuvres and potential deadlocks This is an extremely challenging problem, since, in addition to finding possible routes between pick-up and drop-off locations, it is necessary to check how each route intersects with the others. The disadvantages are that the computational complexity increases with the number of routes to be found, pick-up/drop-off locations must be determined in advance, and generated routes are static
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