Minimum Jerk Guidance for Autonomous Soft-Landing of an Unmanned Aerial Vehicle on a Moving Platform

This paper introduces a new approach to achieving autonomous soft-landing of an unmanned aerial vehicle (UAV) on a moving platform. The proposed method incorporates terminal constraints on position, velocity, and acceleration by utilizing a minimum jerk-based guidance strategy. An optimal landing time is computed using an acceleration-minimizing cost function. Software-in-the-loop simulations are conducted using the PX4-ROS2-Gazebo environment to demonstrate successful landing on a moving platform. The results indicate that the proposed strategy generates smooth trajectories, enabling the UAV to autonomously achieve soft-landing on the moving platform, even when perturbations in the platform's motion are introduced.