Variable Admittance Interaction Control of UAVs via Deep Reinforcement Learning

A compliant control model based on reinforcement learning (RL) is proposed to allow robots to interact with the environment more effectively and autonomously execute force control tasks. The admittance model learns an optimal adjustment policy for interactions with the external environment using RL algorithms. The model combines energy consumption and trajectory tracking of the agent state using a cost function. Therein, an Unmanned Aerial Vehicle (UAV) can operate stably in unknown environments where interaction forces exist. Furthermore, the model ensures that the interaction process is safe, comfortable, and flexible while protecting the external structures of the UAV from damage. To evaluate the model performance, we verified the approach in a simulation environment using a UAV in three external force scenes. We also tested the model across different UAV platforms and various low-level control parameters, and the proposed approach provided the best results.