A3C Based Motion Learning for an Autonomous Mobile Robot in Crowds
| Autor: | Hiroshi Takemura, Yoko Sasaki, Syusuke Matsuo, Asako Kanezaki |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Mobile robot 02 engineering and technology 010501 environmental sciences 01 natural sciences Mobile robot navigation Motion (physics) Computer Science::Robotics 020901 industrial engineering & automation Crowds Asynchronous communication Robot Reinforcement learning Computer vision Motion planning Artificial intelligence business 0105 earth and related environmental sciences |
| Zdroj: | SMC |
| DOI: | 10.1109/smc.2019.8914201 |
| Popis: | The paper proposes a motion planning method using a deep reinforcement learning algorithm, Asynchronous Advantage Actor-Critic (A3C). For mobile robot navigation tasks in crowds, existing path planning based approaches are limited because the surrounding environments change dynamically. The correct motion in such a dynamic environment is underspecified, and a reinforcement learning approach is suitable for generating applicable motion. We propose an A3C based motion planning method for acquiring robot motion for a robot moving through crowds. The proposed method is evaluated in simulated crowds of pedestrians. The experiment section shows the basic performance depending on training parameters and some generated motion examples in the simulator. The learning results using real pedestrian motion are also shown. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|