Learning Swing-free Trajectories for UAVs with a Suspended Load in Obstacle-free Environments

Attaining autonomous flight is an important task in aerial robotics. Often flight trajectories are not only subject to unknown system dynamics, but also to specific task constraints. We are interested in producing a trajectory for an aerial robot with a suspended load that delivers the load to a destination in a swing-free fashion. This paper presents a motion planning framework for generating trajectories with minimal residual oscillations (swing-free) for rotorcraft carrying a suspended load. We rely on a finite-sampling, batch reinforcement learning algorithm to train the system for a particular load. We find the criteria that allows the trained agent to be transferred to a variety of models, state and action spaces and produce a number of different trajectories. Through a combination of simulations and experiments, we demonstrate that the inferred policy is robust to noise and to the unmodeled dynamics of the system. The contributions of this work are 1) applying reinforcement learning to solve the problem of finding a swing-free trajectory for a rotorcraft, 2) designing a problem-specific feature vector for value function approximation, 3) giving sufficient conditions that need to be met to allow successful learning transfer to different models, state and action spaces, and 4) verification of the resulting trajectories in simulation and to autonomously control quadrotors.