2019
DOI: 10.1007/s40295-019-00181-4
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Dual Quaternions as a Tool for Modeling, Control, and Estimation for Spacecraft Robotic Servicing Missions

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Cited by 19 publications
(10 citation statements)
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“…The pose error kinematic equation of motion between frames B and D for the dual-quaternion is given by the following [ 28 ]: which accounts for the angular rate of change, as well as the rate of change of displacement. In the same method of combining the translational and rotational equations of motion ( 8 ) and ( 10 ) into dual-quaternions, the relative pose equation in DQ form [ 28 ] is created and embeds both Equations ( 9 ) and ( 11 ). With one equation of computation, both dynamic equations are accounted for.…”
Section: System Dynamics: Proximity Operationmentioning
confidence: 99%
“…The pose error kinematic equation of motion between frames B and D for the dual-quaternion is given by the following [ 28 ]: which accounts for the angular rate of change, as well as the rate of change of displacement. In the same method of combining the translational and rotational equations of motion ( 8 ) and ( 10 ) into dual-quaternions, the relative pose equation in DQ form [ 28 ] is created and embeds both Equations ( 9 ) and ( 11 ). With one equation of computation, both dynamic equations are accounted for.…”
Section: System Dynamics: Proximity Operationmentioning
confidence: 99%
“…In [17][18][19], several adaptive nonsingular terminal sliding mode control laws were designed for the fixed-time, 6-DOF tracking control of noncooperative spacecraft fly-around missions. In addition, there have been also some research studies concerned with 6-DOF integrated controls in spacecraft based on the dual quaternion representation [20][21][22][23][24][25][26][27].…”
Section: Introductionmentioning
confidence: 99%
“…Recently, dual quaternion neural networks (Du-alQNNs) have been shown to be particularly suitable for modelling transformations in 3D space. Due to the 6 degrees of freedom (6DOF) of the the unit dual quaternion representation, DualQNNs can properly model rotations and translations of rigid bodies [19][20][21][22], or knowledge graph embeddings [23,24]. DualQNNs lay their foundations in dual quaternion numbers, which are a composition of two quaternions, thus particularly suitable for encapsulating eight-dimensional inputs.…”
Section: Introductionmentioning
confidence: 99%