Reinforcement Learning Control for Multi-axis Rotor Configuration UAV

Dai, Yi-Wei; Pi, Chen-Huan; Hu, Kai-Chun; Cheng, Stone

doi:10.1109/aim43001.2020.9159034

Cited by 4 publications

(1 citation statement)

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“…However, the moment of inertia cannot vary too much in their method, and the command of each motor is still determined directly, meaning that the trained policy can only be applied to one type of multirotor because of the fixed number of rotors during the training process. Dai et al [ 22 ] proposed a control policy which generates moment and force command, which improves the flexibility of the training based RL controller. However, the force and moment control commands depend on the multirotor dynamics in learning process and has difficulties when applied to different vehicles with physical parameters.…”

Section: Introductionmentioning

confidence: 99%

General Purpose Low-Level Reinforcement Learning Control for Multi-Axis Rotor Aerial Vehicles

Dai

et al. 2021

Sensors

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This paper proposes a multipurpose reinforcement learning based low-level multirotor unmanned aerial vehicles control structure constructed using neural networks with model-free training. Other low-level reinforcement learning controllers developed in studies have only been applicable to a model-specific and physical-parameter-specific multirotor, and time-consuming training is required when switching to a different vehicle. We use a 6-degree-of-freedom dynamic model combining acceleration-based control from the policy neural network to overcome these problems. The UAV automatically learns the maneuver by an end-to-end neural network from fusion states to acceleration command. The state estimation is performed using the data from on-board sensors and motion capture. The motion capture system provides spatial position information and a multisensory fusion framework fuses the measurement from the onboard inertia measurement units for compensating the time delay and low update frequency of the capture system. Without requiring expert demonstration, the trained control policy implemented using an improved algorithm can be applied to various multirotors with the output directly mapped to actuators. The algorithm’s ability to control multirotors in the hovering and the tracking task is evaluated. Through simulation and actual experiments, we demonstrate the flight control with a quadrotor and hexrotor by using the trained policy. With the same policy, we verify that we can stabilize the quadrotor and hexrotor in the air under random initial states.

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Section: Introductionmentioning

confidence: 99%