Passivity-Based Pose Regulation and Jacobian-Based Force Distribution of a Cable-Driven Parallel Robot

Cheah, Sze Kwan; Caverly, Ryan James

doi:10.23919/acc50511.2021.9482862

Cited by 5 publications

(1 citation statement)

References 19 publications

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“…The concept of pose optimization is extensively discussed in scientific circles. For instance, Cheah and Caverly's research [21] focuses on the pose regulation of a six-degreeof-freedom cable-driven parallel robot (CDPR), analyzing it from a passivity perspective. Similarly, Gao et al's work [22] delves into dynamic Jacobian identification for robots operating in unstructured environments.…”

Section: Discussionmentioning

confidence: 99%

Posture Optimization of the TIAGo Highly-Redundant Robot for Grasping Operation

Bajrami,

Palpacelli,

Carbonari

et al. 2024

Robotics

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This study explores the optimization of the TIAGo robot’s configuration for grasping operation, with a focus on the context of aging. In fact, featuring a mobile base and a robotic arm, the TIAGo robot can conveniently aid individuals with disabilities, including those with motor and cognitive impairments in both domestic and clinical settings. Its capabilities include recognizing visual targets such as faces or gestures using stereo cameras, as well as interpreting vocal commands through acoustic sensors to execute tasks. For example, the robot can grasp and lift objects such as a glass of water and navigate autonomously in order to fulfill a request. The paper presents the position and differential kinematics that form the basis for using the robot in numerous application contexts. In the present case, they are used to evaluate the kinematic performance of the robot relative to an assigned pose in the search for the optimal configuration with respect to the higher-order infinite possible configurations. Ultimately, the article provides insight into how to effectively use the robot in gripping operations, as well as presenting kinematic models of the TIAGo robot.

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Section: Discussionmentioning

confidence: 99%