1 research outputs found
Actuator Trajectory Planning for UAVs with Overhead Manipulator using Reinforcement Learning
In this paper, we investigate the operation of an aerial manipulator system,
namely an Unmanned Aerial Vehicle (UAV) equipped with a controllable arm with
two degrees of freedom to carry out actuation tasks on the fly. Our solution is
based on employing a Q-learning method to control the trajectory of the tip of
the arm, also called end-effector. More specifically, we develop a motion
planning model based on Time To Collision (TTC), which enables a quadrotor UAV
to navigate around obstacles while ensuring the manipulator's reachability.
Additionally, we utilize a model-based Q-learning model to independently track
and control the desired trajectory of the manipulator's end-effector, given an
arbitrary baseline trajectory for the UAV platform. Such a combination enables
a variety of actuation tasks such as high-altitude welding, structural
monitoring and repair, battery replacement, gutter cleaning, skyscrapper
cleaning, and power line maintenance in hard-to-reach and risky environments
while retaining compatibility with flight control firmware. Our RL-based
control mechanism results in a robust control strategy that can handle
uncertainties in the motion of the UAV, offering promising performance.
Specifically, our method achieves 92% accuracy in terms of average displacement
error (i.e. the mean distance between the target and obtained trajectory
points) using Q-learning with 15,000 episode