This paper presents a deep reinforcement learning-based path planning algorithm for the multi-arm robot manipulator when there are both fixed and moving obstacles in the workspace. Considering the problem properties such as high dimensionality and continuous action, the proposed algorithm employs the SAC (soft actor-critic). Moreover, in order to predict explicitly the future position of the moving obstacle, LSTM (long short-term memory) is used. The SAC-based path planning algorithm is developed using the LSTM. In order to show the performance of the proposed algorithm, simulation results using GAZEBO and experimental results using real manipulators are presented. The simulation and experiment results show that the success ratio of path generation for arbitrary starting and goal points converges to 100%. It is also confirmed that the LSTM successfully predicts the future position of the obstacle.
<p>Hwayang Bridge is linking Hwayang-myeon with Jobal-do, Hwajeong-myeon of Yeosu, Jeollanam- do, Korea. Hyundai E&C was awarded the turnkey contract in May 2011, and the construction has started since December 2011. Hwayang Bridge is a concrete cable-stayed bridge with the total span of 854m, composed of a main span of 500m and side spans of 177m. It is ranked the 2nd longest concrete cable-stayed bridge in the world in terms of the main span length. PWS(Parallel Wire Strand) cables with the high strength of 1,860MPa are used and the half cylinder plate is used for the caisson installation. This paper gives brief introduction of the planning and detailed design of Hwayang Bridge, focusing on the overall structural configurations of the bridge.</p>
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