Chiranjeev Prachand

Barrier-Shaped Recurrent Reinforcement Learning for Autonomous Landing on a Heaving Ship Deck

This paper addresses autonomous landing of unmanned aerial vehicle (UAV) rotorcrafts on a heaving ship deck using a recurrent policy trained via asymmetric actor-critic reinforcement learning, validated using a parallel-manipulator-platform-based deck emulator and a quadcopter UAV with an onboard camera.