## 1 Introduction
However, due to the random sampling process, the RRT-Connect method still did not optimize their search tree. [PERSON] et al. proposed Fast-RRT which can obtain a new near-optimal path 20 times faster than the RRT* algorithm [21]. To obtain an optimal path, the authors proposed a scheme in which an Improved RRT algorithm is used to quickly search a viable path and a Fast-Optimal algorithm is used to merge it with current best paths. Recently, another kind of important method, which combines two different single algorithms, was proposed to improve the search efficiency. [PERSON] introduced A* algorithm into RRT* algorithm to guide the sampling procedure, which greatly accelerates the convergence rate [22]. However, it is inefficient for A* to find an initial solution in a large-scale scenario. [PERSON] et al. applies a reinforcement learning algorithm to an RRT algorithm to direct the growth of search trees [23]. The algorithm developed the exploration ability of each search tree and improved the search efficiency, but the learning-based methods may not perform well in a new environment. In addition, some other hybrid algorithms were proposed to improve the quality of RRT-based path planning algorithms. [PERSON] et al. proposed the Q-RRT* algorithm which optimized the process of selecting the parent node and connection by using the triangle inequality [24]. The Q-RRT* algorithm obtained a better initial solution and convergence rate than RRT* algorithm. [PERSON] et al. combined a bio-inspired algorithm and RRT algorithm to improve the search speed of finding the initial solution by modifying the sampling process [25]. Their algorithm greatly promoted the convergence rate and saved on memory usage. However, this method led to nonuniform sample distributions. [PERSON] introduced a hybrid RRT method by combining the RRT-Connect algorithm and IRRT* algorithm [26]. Their algorithm combined the advantages of the two algorithms, which can find initial solutions as quickly as the RRT-Connect algorithm and return the near-optimal solutions as fast as IRRT* method. [PERSON] et al. proposed P-RRT* algorithm by introducing the APF method into an RRT* algorithm to guide the exploration direction, which accelerated the convergence speed faster than the RRT* algorithm [27]. However, these algorithms only considered the presence of static obstacles and can be only used in global path planning. Up to now, there are only a few reports on path planning algorithms that can be used in complex static and dynamic environments. In 2015, [PERSON] et al. proposed a pseudo-bacterial potential field (PBPF) method [28] by introducing the pseudo-bacterial genetic (PBG) algorithm into the traditional APF method to optimize the gain parameters based on evolutionary computation. Compared with the traditional APF method, the PBPF algorithm provides a simple and powerful way to automatically obtain an optimal value of attractive and repulsive potential gains. Another advantage of the PBPF method is that it is suitable to work in both static and dynamic environments, and it does not need to consider the global map information. Recently, [PERSON] et al. improved their method by combining the APF method with membrane computing with the genetic algorithm [29]. These new algorithms greatly improved the research efficiency by shortening the path length and execution time. In this paper, we propose a new hybrid APF-IRRT* algorithm by combining the APF algorithm and the IRRT* algorithm. The idea of the virtual force field of APF is introduced into the IRRT* algorithm to guide the random tree to grow towards the goal point, which does not need to model the global environment and avoids random sampling all over the state space. The contributions of the proposed algorithm are: (1) It greatly improves the search efficiency, and reduces the search time by more than 35% and 24% compared with the IRRT* and P-RRT* algorithms, respectively.