## 1 Introduction
### Related Work  
Usually, it is assumed that the path planner knows all the information about the environment before path planning [14]. Among traditional path planning algorithms, the Dijkstra algorithm plans the path so that the sum of weights of the nodes between the starting point and the goal point is the smallest [15]. This method is inefficient as the number of nodes increases since the amount of required computation increases. The \(A^{*}\) algorithm is the improved Dijkstra algorithm [11]. This algorithm can only be used in a fixed environment, that is when the obstacles do not move. PRM is representative of the sampling-based algorithms. RRT (rapidly exploring random tree) is another popular sampling-based path planning algorithm [16]. Although there are many merits in RRT, it needs enough sampling and can not guarantee the optimality of the path.  
Artificial intelligent-based path planning has been developed and overcome the weak points of existing results [17; 18; 19; 20]. Recently, deep reinforcement learning (RL) is also used to devise path planning [21; 22; 23]. The advantage of deep reinforcement learning-based path planning is that it can create a smoother path over existing results, and successfully enables path planning even in dynamic environments. This is demonstrated in [22]. In order to obtain successful deep reinforcement learning, it is of the utmost importance to consider how to check for collisions. To this end, the OBB (oriented bounding box) is mainly used in path planning. Considering the situation in the real factory, a common case is that obstacles move along a predetermined path in the workspace. Hence, the path planning algorithms have to consider not only the fixed obstacle but also the moving obstacles.  
In order to devise a path planning taking both kinds of obstacles into account, a deep reinforcement learning-based algorithm is proposed in [22]. In the algorithm in [22], there are two key elements leading to successful path planning results. The first is HER (hindsight experience replay) which helps the RL algorithm overcome the sparse reward property of path planning. Note that since path planning is a typical sparse reward problem in terms of RL, it is not easy to train the deep RL agent. The second is that, in addition to the joint values of the manipulator, the current position of the moving obstacle is used as a part of the state. Hence, the current position of the moving obstacle is also used as the input to the neural network for RL-based path planning. In [22], such RL-based path planning can deal with the moving obstacle successfully. The rationale behind the RL-based path planning in [22] is that the neural network for the RL-based path planning agent computes the collision-free path by estimating the future position of the moving obstacle considering the current position of the moving obstacle. In other words, the future position of the moving obstacle is implicitly computed. In the algorithm in [22], it is not possible to check whether the RL-based path planning correctly estimates the future position of the moving obstacle or not. This paper intends to overcome this weak point of the previous result by employing the LSTM in order to predict the future position of the moving obstacle explicitly. It is shown that the prediction by the LSTM based on the current and past obstacle points predicts the future position of the obstacle well and leads to successful path planning.