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- W4387123546 abstract "In this paper, we present an efficient approach to real-time collision-free navigation for mobile robots. By integrating deep reinforcement learning with model predictive control, our aim is to achieve both collision avoidance and computational efficiency. The methodology begins with training a preliminary agent using deep Q-learning, enabling it to generate actions for next time steps. Instead of executing these actions, a reference trajectory is generated based on them, which avoids obstacles present on the original reference path. Subsequently, this local trajectory is employed within an MPC trajectory-tracking framework to provide collision-free guidance for the mobile robot. Experimental results demonstrate that the proposed DQN-MPC hybrid approach outperforms pure MPC in terms of time efficiency and solution quality." @default.
- W4387123546 created "2023-09-29" @default.
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- W4387123546 date "2023-08-26" @default.
- W4387123546 modified "2023-10-18" @default.
- W4387123546 title "Collision-Free Trajectory Planning of Mobile Robots by Integrating Deep Reinforcement Learning and Model Predictive Control" @default.
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- W4387123546 doi "https://doi.org/10.1109/case56687.2023.10260515" @default.
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