Learning-based Trajectory Tracking for Bird-inspired Flapping-Wing Robots
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912130708537344 |
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| author | Cai, Jiaze Sangli, Vishnu Kim, Mintae Sreenath, Koushil |
| author_facet | Cai, Jiaze Sangli, Vishnu Kim, Mintae Sreenath, Koushil |
| contents | Bird-sized flapping-wing robots offer significant potential for agile flight in complex environments, but achieving agile and robust trajectory tracking remains a challenge due to the complex aerodynamics and highly nonlinear dynamics inherent in flapping-wing flight. In this work, a learning-based control approach is introduced to unlock the versatility and adaptiveness of flapping-wing flight. We propose a model-free reinforcement learning (RL)-based framework for a high degree-of-freedom (DoF) bird-inspired flapping-wing robot that allows for multimodal flight and agile trajectory tracking. Stability analysis was performed on the closed-loop system comprising of the flapping-wing system and the RL policy. Additionally, simulation results demonstrate that the RL-based controller can successfully learn complex wing trajectory patterns, achieve stable flight, switch between flight modes spontaneously, and track different trajectories under various aerodynamic conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_15130 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning-based Trajectory Tracking for Bird-inspired Flapping-Wing Robots Cai, Jiaze Sangli, Vishnu Kim, Mintae Sreenath, Koushil Robotics Systems and Control Bird-sized flapping-wing robots offer significant potential for agile flight in complex environments, but achieving agile and robust trajectory tracking remains a challenge due to the complex aerodynamics and highly nonlinear dynamics inherent in flapping-wing flight. In this work, a learning-based control approach is introduced to unlock the versatility and adaptiveness of flapping-wing flight. We propose a model-free reinforcement learning (RL)-based framework for a high degree-of-freedom (DoF) bird-inspired flapping-wing robot that allows for multimodal flight and agile trajectory tracking. Stability analysis was performed on the closed-loop system comprising of the flapping-wing system and the RL policy. Additionally, simulation results demonstrate that the RL-based controller can successfully learn complex wing trajectory patterns, achieve stable flight, switch between flight modes spontaneously, and track different trajectories under various aerodynamic conditions. |
| title | Learning-based Trajectory Tracking for Bird-inspired Flapping-Wing Robots |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2411.15130 |