Name
Abstract | ||
|---|---|---|
In this paper, we present a deep reinforcement learning method for quadcopter bypassing the obstacle on the flying path. In the past study, algorithm only control the forward direction about quadcopter. In this letter, we use two function to control quadcopter. One is quadcopter navigating function. It is based on calculating coordination point and find the straight path to goal. The other function is collision avoidance function. It is implemented by deep Q-network model. Both two function will output rotating degree, agent will combine both output and turn direct. Besides, deep Q-network can also make quadcopter fly up and down to bypass the obstacle and arrive at goal. Our experimental result shows that collision rate is 14% after 500 flights. Based on this work, we will train more complex sense and transfer model to real quadcopter. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/IC3.2018.00013 | 2018 1st International Cognitive Cities Conference (IC3) |
Keywords | DocType | Volume |
deep reinforcement learning,obstacle avoid,quadcopter control | Journal | abs/1811.04584 |
ISBN | Citations | PageRank |
978-1-5386-5060-8 | 0 | 0.34 |
References | Authors | |
2 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tung-Cheng Wu | 1 | 0 | 0.34 |
| Shau-Yin Tseng | 2 | 173 | 24.85 |
| Chin-Feng Lai | 3 | 973 | 74.85 |
| Chia-Yu Ho | 4 | 0 | 0.34 |
| Ying-Hsun Lai | 5 | 0 | 0.34 |