  The paper proposes a novel concept of docking drones to make this process as
safe and fast as possible. The idea behind the project is that a robot with the
gripper grasps the drone in midair. The human operator navigates the robotic
arm with the ML-based gesture recognition interface. The 3-finger robot hand
with soft fingers and integrated touch-sensors is pneumatically actuated. This
allows achieving safety while catching to not destroying the drone's mechanical
structure, fragile propellers, and motors.
  Additionally, the soft hand has a unique technology of providing force
information through the color of the fingers to the remote computer vision (CV)
system. In this case, not only the control system can understand the force
applied but also the human operator. The operator has full control of robot
motion and task execution without additional programming by wearing a mocap
glove with gesture recognition, which was developed and applied for the
high-level control of DroneTrap.
  The experimental results revealed that the developed color-based force
estimation can be applied for rigid object capturing with high precision
(95.3\%). The proposed technology can potentially revolutionize the landing and
deployment of drones for parcel delivery on uneven ground, structure
inspections, risque operations, etc.
