Abstract
Path planning and obstacle avoidance problems are now the focus of robotics research. This paper uses the Dyna-Q reinforcement learning algorithm to implement an obstacle avoidance and a path planning algorithm for unmanned ground vehicle(UGV) under urban environment. Using the reinforcement learning algorithm, we calculate the waypoints of the unmanned vehicle and achieve obstacle avoidance tasks and path planning using a vector field. Finally, we use a PID controller on unmanned aerial vehicle (UAV) to realize the air-ground collaboration task. The algorithms and the agents' modeling in this paper are implemented in the lab's simulation platform.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2022 Chinese Automation Congress, CAC 2022 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 6776-6781 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781665465335 |
| DOIs | |
| State | Published - 2022 |
| Event | 2022 Chinese Automation Congress, CAC 2022 - Xiamen, China Duration: 25 Nov 2022 → 27 Nov 2022 |
Publication series
| Name | Proceedings - 2022 Chinese Automation Congress, CAC 2022 |
|---|---|
| Volume | 2022-January |
Conference
| Conference | 2022 Chinese Automation Congress, CAC 2022 |
|---|---|
| Country/Territory | China |
| City | Xiamen |
| Period | 25/11/22 → 27/11/22 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Dyna-Q
- UGV
- path planning
- simulation platform
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