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Related Experiment Video

Updated: Jan 29, 2026

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
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Automatic Grasping System and Hybrid Controller Towards Multi-Drone Parcel Delivery.

Bruno J Guerreiro1,2, Francisco Azevedo2, Paulo Oliveira2,3

  • 1DEEC/CTS/LASI, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal.

Sensors (Basel, Switzerland)
|January 28, 2026
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Summary
This summary is machine-generated.

This study introduces an autonomous grasping mechanism for drone delivery, enabling in-flight package transfers. The system uses a 3D-printed gripper and hybrid model predictive control (MPC) for precise package handling.

Keywords:
3D printingdrone deliverygripperhybrid MPCpose estimation

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Area of Science:

  • Robotics and Automation
  • Aerospace Engineering
  • Control Systems

Background:

  • Drone-based parcel delivery faces challenges in automated package handling and transfer.
  • In-flight package transfer requires precise control of both the drone and a grasping mechanism.
  • Existing systems often lack the integration of mechanical design, control, and perception for autonomous grasping.

Purpose of the Study:

  • To develop and validate an autonomous grasping mechanism for drone-based parcel delivery.
  • To enable in-flight package transfer capabilities for unmanned aerial vehicles.
  • To integrate a novel hybrid Model Predictive Control (MPC) architecture for coordinated drone and gripper operation.

Main Methods:

  • Development of a 3D-printed mechanical gripper with integrated sensors.
  • Implementation of a parcel pose estimation method for accurate grasping.
  • Formulation of a hybrid dynamical model combining gripper mechanics and simplified drone dynamics.
  • Design of a hybrid Model Predictive Control (MPC) framework for trajectory planning.

Main Results:

  • Successful development of a functional 3D-printed autonomous grasping prototype.
  • Validation of the hybrid MPC approach for coordinating gripper and drone movements in simulation.
  • Demonstration of the system's capability to handle distinct operational phases: acquisition, transport, and release.
  • Experimental validation of the gripper prototype and pose estimation system.

Conclusions:

  • The developed autonomous grasping mechanism is a viable solution for drone-based parcel delivery.
  • The hybrid MPC architecture effectively manages complex in-flight package transfer operations.
  • This research advances the automation capabilities of delivery drones, paving the way for efficient aerial logistics.