# Path Planning of Intelligent Mobile Robots with an Improved RRT Algorithm
## 5 Discussion  
This article optimizes the RRT algorithm based on random sampling for autonomous driving path planning. First, in response to the randomness of the traditional RRT algorithm, a variable steering angle strategy is proposed to reduce the number of search nodes in the random tree. Second, for collision point detection, a collision detection method combined with Kalman filtering is proposed to filter out collision points and eliminate redundant nodes, which reduces the number of valid points on the planned path and makes the path planning more precise. Finally, Bezier curves are used to smooth the path and control its curvature to ensure smoothness. The feasibility of the optimized algorithm is verified through simulations in five different map environments. Based on the results of five sets of simulations, the optimized algorithm reduced the average path length by 13.43% and the planning time by 54.24%. Compared to the RRT algorithm, the accuracy of path planning decisions and the planning time have both been significantly improved. Further validation of the algorithm in real-world scenarios demonstrates its feasibility and accuracy.  
Through data comparison, it can be seen that the optimized algorithm in this paper performs better than the traditional RRT algorithm. In the future, the optimization of the algorithm will mainly focus on the following aspects:  
1. Due to differences between the algorithm in the simulation experiment and real-world scenarios, the robot may not be able to capture the more complex situations of the real world during its movement, which presents certain limitations. The robustness of the algorithm in real-world conditions can be further improved in future work.
2. Optimizing robot hardware in real-world scenarios can address the limitations of sensor precision, computational power, and response time, thereby further improving the performance of the algorithm. For example, increasing sensor precision can reduce measurement errors, enabling the algorithm to make more accurate decisions and plan more effective paths in real-world conditions. Additionally, optimizing and enhancing the robot's processor performance, processing speed, and response time can reduce the time required for path planning. Finally, using more reliable hardware can greatly help the robot mitigate the impact of temperature conditions, sensor conditions, and sensor noise in real-world environments.
3. Regarding whether the algorithm can be executed in autonomous driving systems, we will conduct further testing to ensure that the improved algorithm can be correctly executed on real-world roads. Additionally, we will use better hardware to address potential limitations and challenges related to sensor accuracy, environmental factors, and computational power. Finally, we plan to conduct further research on filtering algorithms in the future to optimize the algorithm and enhance its adaptability.
4. For the improved algorithm in this paper, we will continue research and explore its application and deployment in fields beyond autonomous driving, such as industrial automation and navigation planning, to enhance the algorithm's robustness and performance in a wider range of applications.  
**Author Contributions:** Conceptualization, G.Q.; Methodology, W.Z.; Validation, G.Q.; Formal analysis, W.Z.; Investigation, W.Z.; Resources, G.Q.; Data curation, G.Q.; Writing--original draft, G.Q.; Writing--review and editing, W.Z.; Funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.