#### Article
###### Abstract
However, the planning process is time consuming and thus impractical for planning tasks that have certain time requirements. Many methods [13; 14; 15] have been subsequently proposed to accelerate the convergence of RRT*, but the computational time still cannot meet the requirements of the actual application. This paper focuses on the latter purpose of algorithm improvement, namely to decrease the computational time of the algorithm on the premise that the algorithm can be applied to complex environments. The usual approach involves reducing the number of nodes in the tree and the number of collision detections. RRT-biased [16] is a simple way to improve efficiency. As shown in Algorithm 2, it improves the performance of the algorithm through goal bias sampling with a certain probability (usually 5-10%). However, although the number of nodes in the random tree is appropriately reduced, the computational cost remains large. [PERSON] et al. [17] proposed a method based on goal-oriented sampling for the motion planning of a manipulator; this method can limit the distribution of sampling points to improve the search speed, but the invalid exploration area is large. [PERSON] et al. [18] proposed a utility function for selecting the extension node and direction; this function, which determines the maximum expected extension step of the planner on the basis of the obtained state space information, has improved efficiency to some extent. In addition, [PERSON] et al. [19] described a method of adaptive body selection based on the complexity of planning; this method provides another description of the variable-dimensional C-space for high-DOF articulated robots, thereby improving efficiency from the perspective of dimensionality reduction. ```
1:\(num\leftarrow\) RandomNumber; RandomNumber \(\in[0,1]\)
2:if\(num<\) k then
3:\(q_{rand}=q_{goal}\);
4:else
5:\(q_{rand}=\) RandomSample();
6:endif
7:return\(q_{rand}\);
```  
**Algorithm 2** GoalBiasedSample()  
These methods and many existing RRT variants that guide sampling and expansion have improved efficiency from different aspects, but there is no good general solution for complex environments, such as \"narrow\" ones. [PERSON] et al. [20] proposed an improved method combining the regression and boundary expansion mechanisms; this technique improves efficiency by providing a corresponding solution process for a specific problem. However, although the boundary node can be identified during the exploration process, this method also limits the use of boundary nodes. In addition, some methods [21; 22; 23] dedicated to improving the success rate of the algorithm by changing the sampling strategy have been proposed specifically to solve the problem of narrow environments, but their universality is also weak. It can be seen from the above that an efficient and universal algorithm that is suitable for complex environments is needed to ensure the completion of planning tasks. Therefore, this paper proposes an autonomous path planning algorithm of Node Control based on the architecture of RRT (NC-RRT). Firstly, a method of gradually changing the sampling area is proposed to guide exploration, thereby effectively improving the search speed. In addition, unlike existing methods, which aim to explore new sampling strategies, a node control mechanism is proposed to constrain the extended nodes of the tree and, thus, enhance the applicability of the environment. The results reveal that the algorithm greatly reduces the invalid exploration in the C-space and significantly improves planning efficiency. Moreover, compared with most existing RRT algorithms, NC-RRT is universal for different environments by appropriately adjusting the parameters. For the convenience of explanation, the algorithm will be first described and verified in a two-dimensional space and then applied in a high-dimensional space. The rest of the paper is organized in the following manner. Section 2 introduces the proposed improved RRT algorithm in two parts.