# Path Planning of Intelligent Mobile Robots with an Improved RRT Algorithm
## 1 Introduction  
In recent years, the development of mobile intelligent robots has been flourishing and has been widely applied in various fields to complete different types of tasks [1]. Among them, the rapid development of robots cannot be separated from the support of autonomous driving, which is achieved on the basis of path (trajectory) planning. How to make path planning more accurate, reasonable, and optimal core technology has always been a prominent research topic on autonomous and controllable mobile intelligent robots [2]. At present, path planning is one of the autonomous driving decision-making modules, which can be generally divided into five categories: graph search-based, sampling-based, curve interpolation, numerical optimization, and deep learning algorithms. The trajectory planning algorithms based on graph search mainly include [PERSON]'s algorithm [3] and A * algorithm [4, 5, 6]. Among them, A * algorithm, as a heuristic algorithm, combines priority search and [PERSON]'s algorithm to better balance the relationship between search and utilization. It has fast search speed and ideal search results, but both algorithms will encounter local optimal solutions in the process of path planning, resulting in low search efficiency and greatly affecting planning efficiency. The path planning algorithm based on curve interpolation [7] includes two methods: curve element combination method and interpolation fitting method. Its implementation is simple and the calculation speed is fast, making it suitable for smooth path generation. However, it cannot handle obstacles in complex environments, resulting in poor planning accuracy. The planning algorithm based on numerical optimization [8] uses numerical optimization methods to be solved online, which can find the optimal path and consider complex constraints, but it has high computational complexity and requires a lot of computation time and resources. With the development of data science and big data technology, deep learning algorithms [9; 10; 11] are gradually being applied to autonomous driving and mainly divided into two categories: simulation learning and reinforcement learning. Simulation learning trains a dataset after collecting driving data to form a neural network, realizing the mapping between environmental states and driving. Reinforcement learning drives the reinforcement learning agent to explore the strategy space and obtain the optimal or suboptimal trajectory that can complete the driving task in the process of interacting with the environment by setting a reward function [12; 13; 14]. However, due to the certain gap between the virtual environment simulated by computers and the real environment, namely uncertain variable factors such as light, friction, and slippage, interacting with the environment faces huge challenges. If real robots are directly used for data exchange, the collection price is high. Therefore, it cannot be widely applied. The Rapidly Exploring Random Tree algorithm, as a sampling-based path planning algorithm, operates by expanding and growing new branches within an unknown environment, akin to a tree. It continuously searches and determines whether collisions occur in the surrounding environment while planning the route, ultimately reaching the target range to complete the path planning. Owing to its high search efficiency and strong probabilistic completeness, the RRT algorithm is also applicable to multi-dimensional environments and not just two-dimensional ones, frequently serving as one of the preferred algorithms for path planning in fields like robotic arms and autonomous driving [15; 16]. In response to the aforementioned issues, numerous scholars have proposed improvement measures for traditional RRT algorithms. [PERSON] et al. [17] introduced a sampling strategy based on dynamic probability and a random tree expansion strategy with variable step sizes. This approach reduces the number of sampling points, preventing the robot from getting stuck in local minima during path planning. [PERSON] et al.