Learning Obstacle Avoidance using Double DQN for Quadcopter Navigation
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911317831450624 |
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| author | Doshi, Nishant Sutavani, Amey Gujar, Sanket |
| author_facet | Doshi, Nishant Sutavani, Amey Gujar, Sanket |
| contents | One of the challenges faced by Autonomous Aerial Vehicles is reliable navigation through urban environments. Factors like reduction in precision of Global Positioning System (GPS), narrow spaces and dynamically moving obstacles make the path planning of an aerial robot a complicated task. One of the skills required for the agent to effectively navigate through such an environment is to develop an ability to avoid collisions using information from onboard depth sensors. In this paper, we propose Reinforcement Learning of a virtual quadcopter robot agent equipped with a Depth Camera to navigate through a simulated urban environment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18734 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Obstacle Avoidance using Double DQN for Quadcopter Navigation Doshi, Nishant Sutavani, Amey Gujar, Sanket Robotics One of the challenges faced by Autonomous Aerial Vehicles is reliable navigation through urban environments. Factors like reduction in precision of Global Positioning System (GPS), narrow spaces and dynamically moving obstacles make the path planning of an aerial robot a complicated task. One of the skills required for the agent to effectively navigate through such an environment is to develop an ability to avoid collisions using information from onboard depth sensors. In this paper, we propose Reinforcement Learning of a virtual quadcopter robot agent equipped with a Depth Camera to navigate through a simulated urban environment. |
| title | Learning Obstacle Avoidance using Double DQN for Quadcopter Navigation |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.18734 |