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6D Physical Interaction with an Omnidirectional Aerial Robot
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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.
Title: 6D Physical Interaction with an Omnidirectional Aerial Robot
Description:
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.
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