Genetic Algorithm-Based Trajectory Optimization for Digital Twin Robots

TLDR

Mobile robots play a crucial role in manufacturing material handling, and accurate trajectory execution is essential for efficient operation. This study proposes a trajectory optimization method for mobile robots that leverages a digital twin. A Unity-based digital twin is trained in a virtual environment, generating trajectory plans that are transferred to the physical robot and dynamically refined using real‑world movement data. Genetic algorithms applied to the virtual‑physical loop reduce trajectory errors and enable effective mapping of learned paths from simulation to the real robot.

Abstract

Mobile robots have an important role in material handling in manufacturing and can be used for a variety of automated tasks. The accuracy of the robot’s moving trajectory has become a key issue affecting its work efficiency. This paper presents a method for optimizing the trajectory of the mobile robot based on the digital twin of the robot. The digital twin of the mobile robot is created by Unity, and the trajectory of the mobile robot is trained in the virtual environment and applied to the physical space. The simulation training in the virtual environment provides schemes for the actual movement of the robot. Based on the actual movement data returned by the physical robot, the preset trajectory of the virtual robot is dynamically adjusted, which in turn enables the correction of the movement trajectory of the physical robot. The contribution of this work is the use of genetic algorithms for path planning of robots, which enables trajectory optimization of mobile robots by reducing the error in the movement trajectory of physical robots through the interaction of virtual and real data. It provides a method to map learning in the virtual domain to the physical robot.