Pose consensus based on dual quaternion algebra with application to decentralized formation control of mobile manipulators

Savino, Heitor J.; Pimenta, Luciano C. A.; Shah, Julie A.; Adorno, Bruno Vilhena

doi:10.1016/j.jfranklin.2019.09.045

Cited by 22 publications

(15 citation statements)

References 43 publications

(72 reference statements)

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“…where W (r) ∈ R 3×3 . Also, since rank − H 4 (r * ) = 4 and rank Q 4 (r) = 3 for all r ∈ S 3 [10], from Corollary 2.5.10 of [11] we have that rank A + rank B − 4 ≤ rank AB ≤ min {rank A, rank B} with A − H 4 (r * ) and B Q 4 (r). Hence, rank W (r) = 3 for all r ∈ S 3 and thus rank W (r) = 3.…”

Section: A Dual Quaternion Logarithm and Its Relationsmentioning

confidence: 95%

“…where ζ = (ω + εv) ∈ H p , with ω, v ∈ H p being the angular and the linear velocities, respectively. More specifically, since there exists a matrix Q 4 (r) ∈ R 4×3 such that vec 4ṙ = Q 4 (r) d dt vec 3 (nφ/2) [10], where vec 3 : H p → R 3 and vec 4 : H → R 4 , and ω = 2ṙr * [8], we find by inspection…”

Section: A Dual Quaternion Logarithm and Its Relationsmentioning

confidence: 99%

“…, which implies a y = vec 6ÿ because Q 8 (x) is full-column rank [10]. Using (19), we obtain the closed-loop error dynamics given by (21), which is asymptotically stable in the Lyapunov sense for the PIS…”

Section: B Inner-loop Controllermentioning

confidence: 99%

“…As shown by Caccavale et al [1], the closed-loop system has two sets of equilibrium points, one stable and the other one unstable. The latter consists 10 We changed the order of the rotational and translation terms in h r d and ζ r rd to be consistent with our notation.…”

Section: Appendixmentioning

confidence: 99%

See 3 more Smart Citations

Coupled Task-Space Admittance Controller Using Dual Quaternion Logarithmic Mapping

Fonseca

Adorno

Fraisse

2020

IEEE Robot. Autom. Lett.

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Section: A Dual Quaternion Logarithm and Its Relationsmentioning

confidence: 95%

Section: A Dual Quaternion Logarithm and Its Relationsmentioning

confidence: 99%

Section: B Inner-loop Controllermentioning

confidence: 99%

Section: Appendixmentioning

confidence: 99%

See 2 more Smart Citations

Coupled Task-Space Admittance Controller Using Dual Quaternion Logarithmic Mapping

Fonseca

Adorno

Fraisse

2020

IEEE Robot. Autom. Lett.

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“…With the rapid development of mathematical theory, researchers have successfully established the kinematic modeling of the manipulator robot based on dual-quaternions to reduce computational complexity. However, the dual-quaternions method is still in the research stage due to incompleteness of the theory [25][26][27]. Therefore, the SDH and MDH methods are the main methods in the kinematic modeling process.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Kinematic Modeling and Self-Collision Detection of a Mobile Manipulator Robot by Considering the Actual Physical Structure

Qiao

Luo

et al. 2021

Applied Sciences

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In this paper, an optimized kinematic modeling method to accurately describe the actual structure of a mobile manipulator robot with a manipulator similar to the universal robot (UR5) is developed, and an improved self-collision detection technology realized for improving the description accuracy of each component and reducing the time required for approximating the whole robot is introduced. As the primary foundation for trajectory tracking and automatic navigation, the kinematic modeling technology of the mobile manipulator has been the subject of much interest and research for many years. However, the kinematic model established by various methods is different from the actual physical model due to the fact that researchers have mainly focused on the relationship between driving joints and the end positions while ignoring the physical structure. To improve the accuracy of the kinematic model, we present a kinematic modeling method with the addition of key points and coordinate systems to some components that failed to model the physical structure based on the classical method. Moreover, self-collision detection is also a primary problem for successfully completing the specified task of the mobile manipulator. In traditional self-collision detection technology, the description of each approximation is determined by the spatial transformation of each corresponding component in the mobile manipulator robot. Unlike the traditional technology, each approximation in the paper is directly established by the physical structure used in the kinematic modeling method, which significantly reduces the complicated analysis and shortens the required time. The numerical simulations prove that the kinematic model with the addition of key point technology is similar to the actual structure of mobile manipulator robots, and the self-collision detection technology proposed in the article effectively improves the performance of self-collision detection. Additionally, the experimental results prove that the kinematic modeling method and self-collision detection technology outlined in this paper can optimize the inverse kinematics solution.

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Task-Space Admittance Controller with Adaptive Inertia Matrix Conditioning

Fonseca

Adorno

Fraisse

2021

J Intell Robot Syst

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Whenrobots physically interact with the environment, compliant behaviors should be imposed to prevent damages to all entities involved in the interaction. Moreover, during physical interactions, appropriate pose controllers are usually based on the robot dynamics, in which the ill-conditioning of the joint-space inertia matrix may lead to poor performance or even instability. When the control is not precise, large interaction forces may appear due to disturbed end-effector poses, resulting in unsafe interactions. To overcome these problems, we propose a task-space admittance controller in which the inertia matrix conditioning is adapted online. To this end, the control architecture consists of an admittance controller in the outer loop, which changes the reference trajectory to the robot end-effector to achieve a desired compliant behavior; and an adaptive inertia matrix conditioning controller in the inner loop to track this trajectory and improve the closed-loop performance. We evaluated the proposed architecture on a KUKA LWR4+ robot and compared it, via rigorous statistical analyses, to an architecture in which the proposed inner motion controller was replaced by two widely used ones. The admittance controller with adaptive inertia conditioning presents better performance than with a controller based on the inverse dynamics with feedback linearization, and similar results when compared to the PID controller with gravity compensation in the inner loop.

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Pose consensus based on dual quaternion algebra with application to decentralized formation control of mobile manipulators

Cited by 22 publications

References 43 publications

Coupled Task-Space Admittance Controller Using Dual Quaternion Logarithmic Mapping

Coupled Task-Space Admittance Controller Using Dual Quaternion Logarithmic Mapping

Optimizing Kinematic Modeling and Self-Collision Detection of a Mobile Manipulator Robot by Considering the Actual Physical Structure

Task-Space Admittance Controller with Adaptive Inertia Matrix Conditioning

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