6D Physical Interaction with an Omnidirectional Aerial Robot

In this paper, we present a physical interaction scheme for omnidirectional multirotor aerial vehicles (MRAVs) equipped with fixedly tilted non-coplanar propellers, based on an admittance control architecture. An external wrench observer is employed to estimate the interaction wrench at the end-effector, hence eliminating the need for an additional force/torque sensor. We show that using the nominal allocation matrix in this class of admittance controllers can lead to a contact loss during complex interaction scenarios due to unmodeled and state-dependent aerodynamics effects. To address this issue, we propose a method for identifying the wrench map across different regions of the vehicle’s orientation in SO(3) using free-flight experimental data. This is achieved by formulating a Quadratic Programming (QP) optimization whose solution provides the best approximation of the wrench map for a given orientation of the MRAV. The effectiveness of this approach is experimentally demonstrated, including static point contacts at various orientations, sliding contact, and peg-in-hole tasks.

Authors

Institutions

Publication Details

Journal
Drones
Published
2026-02-13
DOI
https://doi.org/10.3390/drones10020129
Primary Topic
Adaptive Control of Nonlinear Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

6D Physical Interaction with an Omnidirectional Aerial Robot

Ahmed Hussein Ali, Ruben Veenstra, Antonio Franchi
Drones
Adaptive Control of Nonlinear Systems
article

6D Physical Interaction with an Omnidirectional Aerial Robot

Ahmed Hussein Ali, Ruben Veenstra, Antonio Franchi
article en

Abstract

In this paper, we present a physical interaction scheme for omnidirectional multirotor aerial vehicles (MRAVs) equipped with fixedly tilted non-coplanar propellers, based on an admittance control architecture. An external wrench observer is employed to estimate the interaction wrench at the end-effector, hence eliminating the need for an additional force/torque sensor. We show that using the nominal allocation matrix in this class of admittance controllers can lead to a contact loss during complex interaction scenarios due to unmodeled and state-dependent aerodynamics effects. To address this issue, we propose a method for identifying the wrench map across different regions of the vehicle’s orientation in SO(3) using free-flight experimental data. This is achieved by formulating a Quadratic Programming (QP) optimization whose solution provides the best approximation of the wrench map for a given orientation of the MRAV. The effectiveness of this approach is experimentally demonstrated, including static point contacts at various orientations, sliding contact, and peg-in-hole tasks.

DronesVol. 10(2)
Sapienza University of Rome (IT), University of Twente (NL)
Openalex Percentile: Top 58%
Adaptive Control of Nonlinear Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.