# The Path Planning of Mobile Robots Based on an Improved Genetic Algorithm
## 4 Experimental Results and Analysis
### Mobile-Robot Navigation Experiment  
To further validate the performance of the improved algorithm in real-world scenarios, this paper conducts a real-world navigation experiment using the mobile-robot platform shown in Figure 11.  
The mobile robot is equipped with an Intel Core i7 series processor, running Ubuntu 18.04 and an ROS1 system. It uses CAN bus communication for driver communication. The robot obtains surrounding information through laser and ultrasonic radar, with a maximum measurement range of 8 unitand a blind spot of 0.2 unit. The radar is connected to the industrial computer via Ethernet for communication. The power system consists of a stable and reliable servo driver, a servo motor, and a 1:40 reducer. The power management system consists of an onboard control board, which manages the power supply to the vehicle. The hardware communication of the mobile robot is shown in Figure 12.  
To ensure consistency between the experimental tests and simulation results, we implemented several matching measures: using physical walls to simulate the simulation environment; unifying the test starting and ending points; ensuring the simulation pa  
Figure 11: Mobile-robot platform.  
Figure 12: Schematic diagram of mobile-robot hardware communication.  
gorithm. By using a dichotomy-based multi-step method for population initialization, a tournament selection strategy, an adaptive crossover strategy, a dual-layer encoding mutation strategy, and Bezier curve optimization for the optimal path, the method significantly improves path length, smoothness, iteration speed, and computational efficiency. Experimental results show that the path planning performance of the improved algorithm outperforms traditional genetic algorithms and existing improvement methods, demonstrating high practical value.  
However, the improved genetic algorithm proposed in this paper still has certain limitations. Although the improved strategies effectively enhance path quality in static environments, the current experiments mainly focus on static obstacle environments, and the adaptability to moving obstacles has not been fully validated. While the algorithm has been preliminarily validated for feasibility in navigation experiments on a mobile-robot hardware platform (Figure 11 and Table 3), it has not yet been applied in extreme resource-constrained environments, such as low-power microcontrollers. Future research should focus on lightweight algorithm design to reduce high hardware demands and integrate the dynamic window method into the genetic algorithm to achieve dynamic obstacle avoidance.  
Methodology, Z.Z. and H.Y.; Software, S.Z.; Validation, C.X.; Formal analysis, X.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Youth Science and Technology Innovation Talent Plan of Hubei Province grant number 2023 DJC006.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.