Name
Abstract | ||
|---|---|---|
Rocket recovery requires advanced guidance algorithms to achieve pinpoint landing while satisfying multiple stringent constraints. In this paper, we design a guidance law based on reinforcement learning for the powered landing phase of vertical take-off and vertical landing reusable rocket. To this end, we apply the proximal policy optimization algorithm to develop a control policy that drives the rocket to land at a specified location. The policy parameterized using a neural network is updated by performing gradient ascent algorithm. After abundant amount of training, the learned policy is evaluated in a simulation of the rocket powered landing scenario considering aerodynamic drag, and the result demonstrates the ability of the proposed guidance method to successfully land the rocket from a random initial state.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3351917.3351935 | Proceedings of the 2019 4th International Conference on Automation, Control and Robotics Engineering |
Keywords | DocType | ISBN |
Powered landing guidance, Proximal policy optimization, Reinforcement learning, Reusable rocket | Conference | 978-1-4503-7186-5 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yifan Chen | 1 | 58 | 19.82 |
| Lin Ma | 2 | 0 | 0.34 |