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This paper addresses the problem of establishing efficient intralogistic systems, focusing on the generation of roadmaps on a given layout and the coordination of multiple Automated Guided Vehicles (AGVs). A simulation-based approach for automatic roadmap design is proposed. An event-based simulator is developed that uses ant-colony inspired optimization to generate roadmaps tailored to the specific characteristics of a given intralogistic problem, i.e., the plant layout, fleet size, statistical description of tasks, dispatching algorithm, etc. The generated solutions are evaluated with a Multi-Agent Path Finding (MAPF) simulator that uses a Safe Interval Path Planning (SIPP) algorithm. By analysing the system throughput, the optimal fleet size for the system is proposed. The approach is validated through various examples and benchmarked against existing methods in the literature. Note to Practitioners—Creating efficient roadmaps plays a key role in improving the efficiency of multi-AGV systems. Usually, roadmaps are designed by experts, which is time consuming and may lead to suboptimal solutions due to the complexity of the problem. The main objective of this work is to develop a methodology that allows the automatic generation of efficient roadmaps taking into account the specific characteristics of an intralogistic problem. The main advantage is the fast generation of solutions and their validation in terms of throughput. At the same time, the presented methodology allows a quick correction of the solution if one of the problem characteristics changes, without the need to change the layout itself. This provides industry practitioners with an effective tool that facilitates their work in setting up multi-AGV systems, thus saving time and costs.

Miniature mobile robots in multirobotic systems require reliable environmental perception for successful navigation, especially when operating in a real-world environment. One of the sensors that have recently become accessible in microrobotics due to their size and cost-effectiveness is a multizone time-of-flight (ToF) sensor. In this research, object classification using a convolutional neural network (CNN) based on an ultralow-resolution ToF sensor is implemented on a miniature mobile robot to distinguish the robot from other objects. The main contribution of this work is an accurate classification system implemented on low-resolution, low-processing power, and low-power consumption hardware. The developed system consists of a VL53L5CX ToF sensor with an $8\times8$ depth image and a low-power RP2040 microcontroller. The classification system is based on a customized CNN architecture to determine the presence of a miniature mobile robot within the observed terrain, primarily characterized by sand and rocks. The developed system trained on a custom dataset can detect a mobile robot with an accuracy of 91.8% when deployed on a microcontroller. The model implementation requires 7 kB of RAM, has an inference time of 34 ms, and an energy consumption during inference of 3.685 mJ.