Abstract
In this study, a semantic segmentation network is presented to develop an indoor navigation system for a mobile robot. Semantic segmentation can be applied by adopting different techniques, such as a convolutional neural network (CNN). However, in the present work, a residual neural network is implemented by engaging in ResNet-18 transfer learning to distinguish between the floor, which is the navigation free space, and the walls, which are the obstacles. After the learning process, the semantic segmentation floor mask is used to implement indoor navigation and motion calculations for the autonomous mobile robot. This motion calculations are based on how much the estimated path differs from the center vertical line. The highest point is used to move the motors toward that direction. In this way, the robot can move in a real scenario by avoiding different obstacles. Finally, the results are collected by analyzing the motor duty cycle and the neural network execution time to review the robot’s performance. Moreover, a different net comparison is made to determine other architectures’ reaction times and accuracy values.
Highlights
Industry 4.0 requires fully connected factories, and a fully automated production process
The main goal of the present study is to develop indoor navigation for an autonomous mobile robots (AMRs) by implementing The main goal of the present study is to develop indoor navigation for an AMR by implementing a convolutional neural network (CNN) that segments the image to determine the navigable zone and calculates the steering and a CNN that segments the image to determine the navigable zone and calculates the steering and speed speed commands by applying different mathematical operations
The main goal of the present study was to develop an indoor navigation for an AMR by implementing segments thethe image to determine a navigable zone and calculate the steering implementing aaCNN
Summary
Industry 4.0 requires fully connected factories, and a fully automated production process. As Facchini et al [1] explained, this new age in industry provides an opportunity to optimize and reorganize all company structures. This new era requires new methods and tools, such as mobile robots. These mobile robots, known as automated guided vehicles (AGVs) or autonomous mobile robots (AMRs) are being implemented to automate logistics and handmade production processes. An AGV follows a magnetic field, while AMRs have the ability to interact with the area and adapt to each trajectory when an obstacle appears due to the use of different sensors and algorithms to modify each navigation instance
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