A Nonlinear MPC for Physical Human-Aerial Robot Interaction in Collaborative Transportation Tasks
Antonio Gonzalez-Morgado, Jonas Soueidan, Guillermo Heredia, Anibal Ollero, Philippe Fraisse, Marco Tognon, Marco Cognetti
AI summary
Problem
Physical human-aerial robot interaction remains underexplored due to stability, safety, and control complexity during physical contact. Existing aerial approaches lack integrated human-aware planning, limiting effective collaboration in inaccessible environments.
Approach
The authors propose a two-level control framework that pairs a nonlinear model predictive controller (NMPC) with an admittance-based compliant controller. The NMPC optimizes trajectories for both robot and human while prioritizing forward motion for comfort, while the compliant controller limits interaction forces to ensure safety.
Key results
- Two-level control framework integrating NMPC and admittance compliance
- Explicit dynamic modeling of human, robot, and object within NMPC
- Forward-motion prioritization via dynamic cost weighting for human comfort
- Experimental validation showing safe collaborative bar transportation with a hexarotor
Why it matters
Advances safe physical human-aerial collaboration for tasks in inaccessible or cluttered environments where ground robots cannot operate.
Abstract
Aerial robots are transitioning from traditional surveillance and monitoring roles to more advanced tasks involving physical interaction. Despite this progress, physical Human-Aerial Robot Interaction remains largely underex- plored due to the complexity and stability-related issues of such platforms. This paper introduces a novel control framework that enables an aerial platform to cooperatively transport an object with a human operator. The control approach is built on a nonlinear model predictive control (NMPC), integrating the dynamic models of the human, the aerial robot, and the transported object. To ensure safe and robust physical interaction, the NMPC is combined with a compliant controller. Additionally, our controller prioritizes forward motion over lat- eral movements to accommodate the human’s natural direction of motion. We validate this framework through indoor flight experiments, demonstrating how a human operator and a fully actuated hexarotor can effectively collaborate to transport a bar. The results highlight the aerial robot’s ability to assist the human during physical transportation tasks, enhancing efficiency and comfort. NOMENCLATURE FW, F∗ World frame, ∗frame p∗, v∗ Position and velocity of ∗w.r.t. FW WR∗ Rotation matrix of ∗w.r.t. FW η∗, ω∗ Euler angles and angular velocity of ∗w.r.t. FW m∗, J∗ Mass and inertia matrix of ∗ f I R, f I H Interaction force applied on the object by the robot and the human, expressed in FO uH, uA Input of the human, virtual input of the robot MA, BA Virtual inertia and damping of the robot x, u State and control vector in the NMPC Wj Weighting matrix j ∈{pO, ηO, vO, ωO, u} Where ∗∈{R, O, H}, with R: robot, O: object, H: human. Whenever used, the superscript ref denotes a reference value.