Navigating Assistance System for Quadcopter with Deep Reinforcement Learning
| Autor: | Tung-Cheng Wu, Chia-Yu Ho, Ying-Hsun Lai, Shau-Yin Tseng, Chin-Feng Lai |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Quadcopter Computer science Computer Science - Artificial Intelligence Work (physics) Function (mathematics) Systems and Control (eess.SY) Artificial Intelligence (cs.AI) Control theory Obstacle Path (graph theory) FOS: Electrical engineering electronic engineering information engineering Reinforcement learning Computer Science - Systems and Control Point (geometry) Collision avoidance |
| DOI: | 10.48550/arxiv.1811.04584 |
| Popis: | In this paper, we present a deep reinforcement learning method for quadcopter bypassing the obstacle on the flying path. In the past study, the algorithm only controls the forward direction about quadcopter. In this letter, we use two functions to control quadcopter. One is quadcopter navigating function. It is based on calculating coordination point and find the straight path to the goal. The other function is collision avoidance function. It is implemented by deep Q-network model. Both two function will output rotating degree, the 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 the goal. Our experimental result shows that the collision rate is 14% after 500 flights. Based on this work, we will train more complex sense and transfer model to the real quadcopter. Comment: conference |
| Databáze: | OpenAIRE |
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