## 1 Introduction
### Background and Motivation  
In the autonomous robot framework, there are three fundamental concepts such as perception, planning, and control [4]. The perception aims to obtain information about the environment using various sensors. The planning schedules a sequence of valid configurations for the robot arms or wheels in order to perform a given task. When the path achieving the goal is given, the controller adjusts actuators so that, for example, the configurations of the robot manipulators follow the planned path as close as possible.  
Although the robot manipulator operation is quite diverse in the workspace, most operations can simply be interpreted as moving the robot from a starting point to a goal point. Hence, it is utmost essential for the manipulator to move without collision with any obstacles in the workspace. In practice, there are typically two kinds of obstacles: static and periodically moving obstacles. In the case of static obstacles, their locations do not change in the middle of the robot operation, and most existing path planning methods focus on the static obstacles. For the robot operation with the static obstacles, it is a common situation in practice that human experts design the path for the robot manipulators in order to perform a given task. Recent research effort is directed to make this procedure in an automatic manner. In other words, the human experts' path planning is replaced with various automatic algorithms [5]. The other is the periodically moving obstacle. For the purpose of dealing with the moving obstacles, this paper considers the situation where every dynamic object in the workspace moves by plan. Consequentially, it is assumed in this paper that there are no unexpected obstacles, and that all dynamic obstacles are moving periodically. In the case of industrial robots in a real factory, this is the case. Moreover, since one robot manipulator is viewed as an obstacle to the other robots, and the entire operation on a factory is usually batch production, many obstacles can be regarded as periodically moving obstacles if they are not static obstacles. Since existing path planning algorithms deal with such a moving obstacle using any methods for static obstacles, the results are conservative. In other words, to deal with the moving obstacles, those algorithms view the whole area where the obstacle moves as one big artificial static obstacle, which is inevitably conservative.  
In robot path planning research, the sampling-based algorithm is representative [6]. In order to compute the path connecting the initial and goal points, the sampling-based algorithm samples the nodes from the collision-free space and connects those nodes. Hence, the method relies on how to sample the nodes. In the case of multi-arm manipulators, the problem dimension increases quadratically as the number of arms increases, which makes it difficult for the planner to find an optimal path. Moreover, if the obstacle moves, it is not easy to use the sampling-based algorithms. Existing path planning algorithms, especially sample-based approaches, work well for a single robot manipulator. However, if the target manipulator is a multi-arm manipulator with moving obstacles, the path planning problem becomes difficult since the problem dimension is high and it is not trivial to sample the configuration space.  
Hence, the focus of this paper is placed on devising a deep reinforcement learning-based path planning algorithm which can generate collision-free paths when the multi-arm manipulator has both static and periodically moving obstacles.