Deep Reinforcement Learning Based Trajectory Planning Under Uncertain Constraints.
| Autor: | Chen L; Department of Computer Science, Technische Universität München, Munich, Germany., Jiang Z; Department of Computer Science, Technische Universität München, Munich, Germany., Cheng L; College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China., Knoll AC; Department of Computer Science, Technische Universität München, Munich, Germany., Zhou M; Department of Computer Science, Technische Universität München, Munich, Germany.; College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China. |
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| Jazyk: | angličtina |
| Zdroj: | Frontiers in neurorobotics [Front Neurorobot] 2022 May 02; Vol. 16, pp. 883562. Date of Electronic Publication: 2022 May 02 (Print Publication: 2022). |
| DOI: | 10.3389/fnbot.2022.883562 |
| Abstrakt: | With the advance in algorithms, deep reinforcement learning (DRL) offers solutions to trajectory planning under uncertain environments. Different from traditional trajectory planning which requires lots of effort to tackle complicated high-dimensional problems, the recently proposed DRL enables the robot manipulator to autonomously learn and discover optimal trajectory planning by interacting with the environment. In this article, we present state-of-the-art DRL-based collision-avoidance trajectory planning for uncertain environments such as a safe human coexistent environment. Since the robot manipulator operates in high dimensional continuous state-action spaces, model-free, policy gradient-based soft actor-critic (SAC), and deep deterministic policy gradient (DDPG) framework are adapted to our scenario for comparison. In order to assess our proposal, we simulate a 7-DOF Panda (Franka Emika) robot manipulator in the PyBullet physics engine and then evaluate its trajectory planning with reward, loss, safe rate, and accuracy. Finally, our final report shows the effectiveness of state-of-the-art DRL algorithms for trajectory planning under uncertain environments with zero collision after 5,000 episodes of training. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Chen, Jiang, Cheng, Knoll and Zhou.) |
| Databáze: | MEDLINE |
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