A meta-reinforcement learning method for adaptive payload transportation with variations

The safe transport of cable-suspended payloads by a group of Unmanned Aerial Vehicles (UAVs) depends on their capacity to effectively respond to fluctuations in the dynamics caused by external variations, such as wind gusts. For group transportation with obstacles, internal variations, such as changes in formation, can also alter the space occupancy of the system related to collision detection. However, traditional adaptive learning methods are challenging to adapt to these two variations. In this paper, we present a learning-based method for collision-free dual-UAV-payload transportation in the presence of varied wind force and formation change. It consists of an adaptive trajectory tracking controller based on meta-model-based reinforcement learning with online adaptation and a novel correction policy, and a path planner that can sample collision-free goal states of the system for the controller based on the meta-collision predictor. The simulation results demonstrate that the proposed trajectory tracking controller outperforms state-of-the-art model-free, model-based, and variational inference methods in terms of payload tracking error reduction and robustness when dealing with the variations mentioned above. Specifically, the proposed controller reduces the average payload tracking error to less than 0.1 metres in most tasks without obstacles. Furthermore, by following the adapted paths generated by the path planner, the trajectory tracking controller can effectively track the payload while ensuring collision-free safety of the dual-UAV-payload system during navigation among obstacles. The success rate of the proposed method is more than 80% in all scenarios with obstacles. Our project website can be seen at https://sites.google.com/view/meta-payload-fly/ and the source code is available at https://github.com/wawachen/Meta-load-fly.