Evaluation of Robot Manipulator Link Velocity and Acceleration Observer

Birjandi, Seyed Ali Baradaran; Fortunić, Edmundo Pozo; Haddadin, Sami

doi:10.1016/j.ifacol.2023.10.1583

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Cited by 3 publications

References 21 publications

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Real-time observer designs for elastic-joint industrial robots: Experimental comparison and new strategies

Reyhani,

Marko,

Janisch

et al. 2024

Mechatronics

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Real-time observer designs for elastic-joint industrial robots: Experimental comparison and new strategies

Reyhani,

Marko,

Janisch

et al. 2024

Mechatronics

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A Stable Adaptive Extended Kalman Filter for Estimating Robot Manipulators Link Velocity and Acceleration

Baradaran Birjandi,

Khurana,

Billard

et al. 2023

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Machine learning–driven self-discovery of the robot body morphology

Díaz Ledezma,

Haddadin

2023

Sci. Robot.

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The morphology of a robot is typically assumed to be known, and data from external measuring devices are used mainly for its kinematic calibration. In contrast, we take an agent-centric perspective and ponder the vaguely explored question of whether a robot could learn elements of its morphology by itself, relying on minimal prior knowledge and depending only on unorganized proprioceptive signals. To answer this question, we propose a mutual information–based representation of the relationships between the proprioceptive signals of a robot, which we call proprioceptive information graphs (π-graphs). Leveraging the fact that the information structure of the sensorimotor apparatus is dependent on the embodiment of the robot, we use the π-graph to look for pairwise signal relationships that reflect the underlying kinematic first-order principles applicable to the robot’s structure. In our discussion, we show that analysis of the π-graph leads to the inference of two fundamental elements of the robot morphology: its mechanical topology and corresponding kinematic description, that is, the location and orientation of the robot’s joints. Results from a robot manipulator, a hexapod, and a humanoid robot show that the correct topology and kinematic description can be effectively inferred from their π-graph either offline or online, regardless of the number of links and body configuration.

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Evaluation of Robot Manipulator Link Velocity and Acceleration Observer

Cited by 3 publications

References 21 publications

Real-time observer designs for elastic-joint industrial robots: Experimental comparison and new strategies

Real-time observer designs for elastic-joint industrial robots: Experimental comparison and new strategies

A Stable Adaptive Extended Kalman Filter for Estimating Robot Manipulators Link Velocity and Acceleration

Machine learning–driven self-discovery of the robot body morphology

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