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Aerial Manipulation Using Hybrid Force and Position NMPC Applied to Aerial Writing

Abstract

Aerial manipulation aims at combining the maneuverability of aerial vehicles with the manipulation capabilities of robotic arms. This, however, comes at the cost of the additional control complexity due to the coupling of the dynamics of the two systems. In this paper we present a Nonlinear Model Predictive Controller (NMPC) specifically designed for Micro Aerial Vehicles (MAVs) equipped with a robotic arm. We formulate a hybrid control model for the combined MAV-arm system which incorporates interaction forces acting on the end effector. We explain the practical implementation of our algorithm and show extensive experimental results of our custom built system performing multiple `aerial-writing' tasks on a whiteboard, revealing accuracy in the order of millimetres.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Mathematics & Optimization
🧭 Keyword Pioneer — hybrid force control
🐝 Cross-Pollinator — Artificial Intelligence, Computer Vision, Deep Learning, Machine Learning, Mathematics & Optimization, Reinforcement Learning, Robotics