Since path planning for multi-arm manipulators is a complicated high-dimensional problem, effective and fast path generation is not easy for the arbitrarily given start and goal locations of the end effector. Especially, when it comes to deep reinforcement learning-based path planning, high-dimensionality makes it difficult for existing reinforcement learning-based methods to have efficient exploration which is crucial for successful training. The recently proposed soft actor–critic (SAC) is well known to have good exploration ability due to the use of the entropy term in the objective function. Motivated by this, in this paper, a SAC-based path planning algorithm is proposed. The hindsight experience replay (HER) is also employed for sample efficiency and configuration space augmentation is used in order to deal with complicated configuration space of the multi-arms. To show the effectiveness of the proposed algorithm, both simulation and experiment results are given. By comparing with existing results, it is demonstrated that the proposed method outperforms the existing results.
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.
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