Abstract
In the workspace of robot manipulators in practice, it is common that there are both static and periodic moving obstacles. Existing results in the literature have been focusing mainly on the static obstacles. This paper is concerned with multi-arm manipulators with periodically moving obstacles. Due to the high-dimensional property and the moving obstacles, existing results suffer from finding the optimal path for given arbitrary starting and goal points. To solve the path planning problem, this paper presents a SAC-based (Soft actor–critic) path planning algorithm for multi-arm manipulators with periodically moving obstacles. In particular, the deep neural networks in the SAC are designed such that they utilize the position information of the moving obstacles over the past finite time horizon. In addition, the hindsight experience replay (HER) technique is employed to use the training data efficiently. In order to show the performance of the proposed SAC-based path planning, both simulation and experiment results using open manipulators are given.
Highlights
In the fourth industrial revolution, the operation of an autonomous multi-robot in a complicated workspace has been an important challenge for modern smart factories [1].It is important to replace the human workforce with robots, to collaborate with robots, and to deploy robots in an efficient manner [2,3]
This paper presents a deep reinforcement learning-based path planning algorithm for multi-arm manipulators with both static and periodically moving obstacles
The recently developed soft actor–critic (SAC) is employed for the deep reinforcement learning since the SAC can compute the optimal solution for the high-dimensional problem and the path planning problem for the multi-arm manipulators is essentially high dimensional
Summary
In the fourth industrial revolution, the operation of an autonomous multi-robot in a complicated workspace has been an important challenge for modern smart factories [1]. This paper presents a deep reinforcement learning-based path planning algorithm to deal with periodically moving obstacles
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