Glove2UAV: A Wearable IMU-Based Glove for Intuitive Control of UAV

This paper presents Glove2UAV, a wearable IMU-glove interface for intuitive UAV control through hand and finger gestures, augmented with vibrotactile warnings for exceeding predefined speed thresholds. To promote safer and more predictable interaction in dynamic flight, Glove2UAV is designed as a lightweight and easily deployable wearable interface intended for real-time operation. Glove2UAV streams inertial measurements in real time and estimates palm and finger orientations using a compact processing pipeline that combines median-based outlier suppression with Madgwick-based orientation estimation. The resulting motion estimations are mapped to a small set of control primitives for directional flight (forward/backward and lateral motion) and, when supported by the platform, to object-interaction commands. Vibrotactile feedback is triggered when flight speed exceeds predefined threshold values, providing an additional alert channel during operation. We validate real-time feasibility by synchronizing glove signals with UAV telemetry in both simulation and real-world flights. The results show fast gesture-based command execution, stable coupling between gesture dynamics and platform motion, correct operation of the core command set in our trials, and timely delivery of vibratile warning cues.

DroneVLA: VLA based Aerial Manipulation

As aerial platforms evolve from passive observers to active manipulators, the challenge shifts toward designing intuitive interfaces that allow non-expert users to command these systems naturally. This work introduces a novel concept of autonomous aerial manipulation system capable of interpreting high-level natural language commands to retrieve objects and deliver them to a human user. The system is intended to integrate a MediaPipe based on Grounding DINO and a Vision-Language-Action (VLA) model with a custom-built drone equipped with a 1-DOF gripper and an Intel RealSense RGB-D camera. VLA performs semantic reasoning to interpret the intent of a user prompt and generates a prioritized task queue for grasping of relevant objects in the scene. Grounding DINO and dynamic A* planning algorithm are used to navigate and safely relocate the object. To ensure safe and natural interaction during the handover phase, the system employs a human-centric controller driven by MediaPipe. This module provides real-time human pose estimation, allowing the drone to employ visual servoing to maintain a stable, distinct position directly in front of the user, facilitating a comfortable handover. We demonstrate the system's efficacy through real-world experiments for localization and navigation, which resulted in a 0.164m, 0.070m, and 0.084m of max, mean euclidean, and root-mean squared errors, respectively, highlighting the feasibility of VLA for aerial manipulation operations.