ArticleFrontiers in bioengineering and biotechnology2021
Genetic Algorithm-Based Trajectory Optimization for Digital Twin Robots.
Article in Frontiers in bioengineering and biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- Design, analysis, and experimental research on installation robot for hanging column.Scientific reports · 2026Article
- Digital Twin-Based Intelligent Monitoring System for Robotic Wiring Process.Sensors (Basel, Switzerland) · 2025Article
- Multi-strategy collaborative optimization of gravitational search algorithm.Scientific reports · 2025Article
- Long-distance target localization optimization algorithm based on single robot moving path planning.Scientific reports · 2025Article
- Construction and Validation of a Digital Twin-Driven Virtual-Reality Fusion Control Platform for Industrial Robots.Sensors (Basel, Switzerland) · 2025Article
- Modeling, evaluation and metrics performance of the SyncLMKD in distributed kinematics variations.Scientific reports · 2025Article
- Digital Twin Approach for Operation and Maintenance of Transportation System-Systematic Review.Sensors (Basel, Switzerland) · 2024Review
- Obstacle Avoidance and Path Planning Methods for Autonomous Navigation of Mobile Robot.Sensors (Basel, Switzerland) · 2024Review
- Article
- Heuristic computing with active set method for the nonlinear Rabinovich-Fabrikant model.Heliyon · 2023Article
- Local Path Planning for Mobile Robots Based on Fuzzy Dynamic Window Algorithm.Sensors (Basel, Switzerland) · 2023Article
- Using Constrained-Disorder Principle-Based Systems to Improve the Performance of Digital Twins in Biological Systems.Biomimetics (Basel, Switzerland) · 2023Review
- Dynamic Path Planning of AGV Based on Kinematical Constraint A* Algorithm and Following DWA Fusion Algorithms.Sensors (Basel, Switzerland) · 2023Article
- Improved Bayesian Optimization Framework for Inverse Thermal Conductivity Based on Transient Plane Source Method.Entropy (Basel, Switzerland) · 2023Article
- A parallel heterogeneous policy deep reinforcement learning algorithm for bipedal walking motion design.Frontiers in neurorobotics · 2023Article
- Multi-Objective Location and Mapping Based on Deep Learning and Visual Slam.Sensors (Basel, Switzerland) · 2022Article
- Discovering Interdisciplinary Research Based on Neural Networks.Frontiers in bioengineering and biotechnology · 2022Article
- Improved Multi-Stream Convolutional Block Attention Module for sEMG-Based Gesture Recognition.Frontiers in bioengineering and biotechnology · 2022Article
- Multi-Objective Optimization Design of Ladle Refractory Lining Based on Genetic Algorithm.Frontiers in bioengineering and biotechnology · 2022Article
- Prediction and Sensitivity Analysis of COFrontiers in bioengineering and biotechnology · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.