Search Results for author: David Navarro-Alarcon

Found 6 papers, 1 papers with code

Robust Integral Consensus Control of Multi-Agent Networks Perturbed by Matched and Unmatched Disturbances: The Case of Directed Graphs

no code implementations30 Sep 2023 Jose Guadalupe Romero, David Navarro-Alarcon

This work presents a new method to design consensus controllers for perturbed double integrator systems whose interconnection is described by a directed graph containing a rooted spanning tree.

Follow the Curve: Robotic-Ultrasound Navigation with Learning Based Localization of Spinous Processes for Scoliosis Assessment

no code implementations11 Sep 2021 Maria Victorova, Michael Ka-Shing Lee, David Navarro-Alarcon, Yongping Zheng

After the scanning, the acquired data is used to reconstruct the coronal spinal image, where the deformity of the scoliosis spine can be assessed and measured.

Anatomy

On Radiation-Based Thermal Servoing: New Models, Controls and Experiments

no code implementations24 Dec 2020 Luyin Hu, David Navarro-Alarcon, Andrea Cherubini, Mengying Li

In this paper, we introduce a new sensor-based control method that regulates (by means of robot motions) the heat transfer between a radiative source and an object of interest.

Robotics Systems and Control Systems and Control

A Point Cloud-Based Method for Automatic Groove Detection and Trajectory Generation of Robotic Arc Welding Tasks

no code implementations26 Apr 2020 Rui Peng, David Navarro-Alarcon, Victor Wu, Wen Yang

In this paper, in order to pursue high-efficiency robotic arc welding tasks, we propose a method based on point cloud acquired by an RGB-D sensor.

Robotics

Force-Ultrasound Fusion: Bringing Spine Robotic-US to the Next "Level"

1 code implementation26 Feb 2020 Maria Tirindelli, Maria Victorova, Javier Esteban, Seong Tae Kim, David Navarro-Alarcon, Yong Ping Zheng, Nassir Navab

Processed force and ultrasound data are fused using a 1D Convolutional Network to compute the location of the vertebral levels.

A Self-Organizing Network with Varying Density Structure for Characterizing Sensorimotor Transformations in Robotic Systems

no code implementations1 May 2019 Omar Zahra, David Navarro-Alarcon

The proposed method has self-organizing and associative properties that enable it to autonomously obtain these relations without any prior knowledge of either the motor (e. g. mechanical structure) or perceptual (e. g. sensor calibration) models.

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