Aerial grasping has many applications in the fields of search-and-rescue, maintenance, inspection, and the delivery industry. However, there are still many limitations to be overcome, including better and more lightweight gripper solutions, and more efficient payload transportation methods. Soft grippers offer the advantage of being lightweight, compliant, suitable for delicate objects, and requiring simple control. While soft robotic grippers have been explored for aerial grasping, the combination of fast grasping and soft grippers has not been demonstrated. Moreover, multi-modality for aerial grasping is under-explored in the literature. This work presents a novel approach to aerial grasping using a kirigami-based ultra-lightweight soft gripper with a fast actuation system and a hybrid, multi-modal OmniRotor vehicle.
A platform belonging to the OmniRotor class, is a hybrid, multimodal vehicle that combines the advantages of Unmanned Aerial Vehicles (UAV) and Unmanned Ground Vehicles (UGV), being capable of continuous omnidirectional thrust vectoring and with both aerial and ground manipulation capabilities. This work demonstrates how kirigami grippers can be used for aerial grasping, proposes the design of a spring-loaded, fast-release, lightweight actuation mechanism for the kirigami gripper, and demonstrates how an OmniRotor platform can be used for efficient, single-platform aerial and ground manipulation in a construction environment. The contributions of this work can help overcome the limitations of aerial grasping and enable efficient multi-modal manipulation with unmanned vehicles.

This paper was nominated for the Best Paper Award at ICUAS 2024, which is the largest academic conference dedicated to Unmanned Aerial Vehicles.

Publications

[1] J. Buzzatto and M. Liarokapis. ”On Dexterous Aerial and Ground Manipulation Using a Multi‑Modal OmniRotor Platform Equipped with a Fast, Soft, Kirigami Gripper.” In 2024 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 564‑571. IEEE, 2024. ‑ Nominated to Best Paper Award.