# Path Planning of a Mobile Robot Based on the Improved RRT Algorithm
## 1 Introduction
In [14], the improved particle swarm optimization (PSO) algorithm is utilized to optimize the key nodes after finding the initial path. Similarly, in [15], a set of paths is treated as a population, and the GA is utilized to calculate the optimal solution. However, these methods have some parameters that need to be set in advance. The RRT algorithm uses a uniform sampling strategy to explore the whole configuration space; this process is random and inefficient. In other words, the samples are selected without purpose, as most of them have no impact on the final path. In order to make the exploration more efficient, in [16], the RRT-Connect algorithm is proposed, which grows two trees from the initial and target states alternately and combines a greedy strategy to enhance the algorithm's speed, but it still applies the uniform sampling strategy. In [17], the Bias-RRT algorithm chooses a target state as the sample with a certain probability to guide the tree to reach the goal quickly, but it is easy to fall into a local minimum using this strategy. The 1-0 Bg-RRT algorithm [18] is an enhanced version of the Bias-RRT algorithm, in which the goal bias probability is changed by two values, 0 and 1. Although the algorithm's performance in simple situations has been improved to some extent, it still retains the same issues in complex situations. [PERSON] et al. [19] introduced the greedy sampling space reduction and environmental judgment strategies that can handle complex scenarios such as narrow passages, but their algorithm lacks stability. In combination with other algorithms, in [20], the authors combined the potential field idea of the APF algorithm to guide the expansion process of the RRT algorithm, keeping the path away from obstacles, but there is obvious oscillation in the path. [PERSON] et al. [21] used the generalized voronoi graph (GVG) to find the initial path and then applied the multiple potential functions (MPF) to generate a non-uniform distribution for guiding the sampling process. However, their algorithm faces challenges, as the GVG takes a lot of time to find the initial path in complex scenarios. Learning-based methods have become popular in recent years. In [22], a neural network was trained to replicate human decision making to drive a mobile robot in uncertain scenarios. [PERSON] et al. [23] trained a conditional variational autoencoder (CVAE) model to predict non-uniform sampling from latent space. [PERSON] et al. [24] utilized a neural network to predict cost functions and solve motion planning problems with kinodynamic constraints. [PERSON] et al. [25] applied a convolutional neural network (CNN) model, usingthe A* algorithm planning cases as the dataset, to predict the optimal path distribution in an environment. Learning-based methods have good scalability and show some promising prospects, but the success of these methods depends on the quality of the dataset. There is still plenty of room for improvement in terms of the performance of the model. All of these points indicate that algorithms used to generate optimal paths require significant computing resources, while the algorithms used to generate paths quickly cannot guarantee the quality of the path. Moreover, many algorithms exhibit low efficiency in some complex environments. In practical applications, planning algorithms are often required to be able to quickly find solutions in different environments while meeting certain quality requirements. The balance between the computing cost and performance is a key consideration of this work. To overcome this issue, we proposed the APSD-RRT method, which is a two-stage algorithm. In the first stage, the planner is designed to quickly achieve a feasible solution using adjustable probability and sampling area strategies. In the second stage, the optimizer is designed to enhance the quality of the path generated by the planner; it is equipped with the Dijkstra pruning and interpolation strategies.