## 1 Introduction  
The purpose of mobile robot path planning is to find a feasible and collision-free optimal path from a start point to a target point in an obstacle working environment according to certain evaluation criteria, such as the shortest path length, the minimum energy consumption or the shortest walking time [1, 2, 3]. As one of the core technologies in mobile robot navigation, path planning ensures that mobile robots can accomplish tasks efficiently, safely and independently, and it has been widely used in motion planning problems, such as in manipulation robots [4], mobile robots [5, 6] and unmanned aerial vehicles [7, 8]. According to the robot's knowledge about the environment, path planning is divided into two types [9]: local path planning and global path planning. In local path planning, the robots sense the current local environment (i.e., the location and geometry of the obstacles) and construct an estimated map of the environment in real time during the whole search process to find a feasible obstacle-free path from a start point to a target point. However, this category of path planning can easily conclude with a local optimal solution or even a non-reachable target due to a lack of prior information about the environment. Common local path planning algorithms are the Artificial Immune algorithm [10], A-star algorithm [11], Fuzzy algorithm [12] and APF algorithm [13], etc. In global path planning, owing to knowledge of prior information about the search space, the robots can acquire a global optimal path using optimization algorithms directly. Common global path planning algorithms are the Bee Colony algorithm [14], Genetic algorithm [15] and RRT algorithm [16], etc. Due to the disadvantage of their non-real time process, these algorithmsare ineffective if the environment changes, such as an unknown obstacle appearing. Up to now, how to overcome their respective shortcomings and how to realize advantage complementation of different single algorithms remains a technical challenge. The RRT algorithm, which is considered as one of the most popular global path planning algorithms, is an efficient incremental sampling-based method to find the global solution proposed by [PERSON] of Iowa State University in 1998 [16]. The main idea of the RRT algorithm is to construct a search tree rooted at the starting point and to grow the tree by random samples from the search space. The RRT algorithm is simple and can find a solution in any complex environment, and has been widely used in path planning for mobile robots. However, due to the random sampled process, the RRT algorithm usually provides non-optimal solutions. To combat the disadvantages of RRT algorithm, an optimized RRT inversion called RRT star (RRT*) was proposed [17] in 2010. Different from RRT, the RRT* algorithm does not stop sampling after the first solution is found. It continues iteration to optimize the current solution until a near-optimal solution is found. The increased sampling and iteration process also leads to inefficiency of the algorithm. In 2014, [PERSON] from the University of Toronto further optimized the RRT* algorithm, which is called IRRT* algorithm, by restricting the search area to an ellipsoidal subset of the state space [18; 19]. This method expedited the optimization process and improved the rate of convergence while retaining the same probabilistic completeness and asymptotic optimality as the RRT* algorithm. However, it still has the disadvantage of the RRT-based algorithm, in which it is difficult to avoid unnecessary extension and find the optimal solution due to the randomness of sampling and non-real time process. Due to the disadvantages mentioned above, many algorithms were proposed to improve the quality of RRT-based algorithms. [PERSON] proposed the RRT-Connect algorithm in which two search trees were constructed in the start state and target state, respectively, and grew toward each other [20]. This method greatly reduced the search time and improved the efficiency rate.