Tactile and proximity sensing can be efficiently used to perform automatic crack detection. This dataset was acquired with a fibre optic based finger-shaped sensor which slided on a total of 10 different 3D printed surfaces for a total of 50 acquistions. The acquired data can be used for fracture detection on surfaces using both tactile and proximity features.
Localising and recognising the presence of mechanical fractures is an important task necessary in hazardous environments during waste decommissioning. It is especially useful to avoid spillage from containers keeping chemical and radioactive waste or to identify concrete fractures at early stages, to prevent their growth which may lead to larger macro-scale catastrophic failures. This dataset consists of 3000 labelled images of fractures and 24000 labelled augmented images. The acquired data can be used for fracture localisation with object detection.
Published in 2017 International Conference on Rehabilitation Robotics (ICORR), 2017
The main goals of this paper are to explore repeatability in sEMG data and to release a repeatability database with the recorded experiments. The data are recorded from 10 intact subjects repeating 7 grasps 12 times, twice a day for 5 days. The data are publicly available on the Ninapro web page as the 6th database. The analysis for the repeatability is based on the comparison of movement classification accuracy in several data acquisitions and for different subjects.
Recommended citation: Palermo, Francesca. (2017). "Repeatability of grasp recognition for robotic hand prosthesis control based on sEMG data" 2017 International Conference on Rehabilitation Robotics (ICORR)
Published in 2019 Annual Conference Towards Autonomous Robotic Systems (TAROS), 2019
In this paper we describe how a generic interoperability telerobotics protocol can be applied for master-slave robotic systems operating in position-position, position-speed and hybrid control modes.
Recommended citation: Omarali, Bukheikhan. (2019). "Position and Velocity Control for Telemanipulation with Interoperability Protocol" 2019 Annual Conference Towards Autonomous Robotic Systems (TAROS)
Published in 2019 Annual Conference Towards Autonomous Robotic Systems (TAROS), 2019
This work presents one of the first portable augmented reality environment for transradial amputees that combines two devices available on the market: the Microsoft HoloLens and the Thalmic labs Myo. In the augmented environment, rendered by the HoloLens, the user can control a virtual hand with surface electromyography.
Recommended citation: Palermo, Francesca. (2019). "An Augmented Reality Environment to Provide Visual Feedback to Amputees During sEMG Data Acquisitions" 2019 Towards Autonomous Robotic Systems (TAROS)
Published in 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020
This paper demonstrates how tactile and proximity sensing can be efficiently used to perform automatic crack detection. A custom-designed integrated tactile and proximity sensor is implemented. It measures the deformation of its body when interacting with the physical environment and distance to the environment’s objects with the help of fibre optics.
Recommended citation: Palermo, Francesca. (2020). "Implementing Tactile and Proximity Sensing for Crack Detection " 2020 IEEE International Conference on Robotics and Automation (ICRA)
Published in Frontiers Robotics and AI, 2020
This paper demonstrates an updated implementation of the previous ICRA paper. A custom-designed integrated tactile and proximity sensor has been implemented. The proposed method implements machine learning techniques (handcrafted features, and state of the art classification algorithms). A real-time classification method has been implemented for online classification of explored surfaces.
Recommended citation: Palermo, Francesca, et al. (2020). "Automatic Fracture Characterization Using Tactile and Proximity Optical Sensing " Frontiers in Robotics and AI 7 (2020)
Published in 25th International Conference on Pattern Recognition (ICPR2020), 2020
We propose a multi-modal method to predict three key indicators of the filling mass: filling type, filling level, and container capacity. These indicators are then combined to estimate the filling mass of a container. Our method obtained Top-1 overall performance among all submissions to CORSMAL 2020 Challenge on both public and private subsets while showing no evidence of overfitting.
Recommended citation: Iashin Vladimir, et al. (2020). "Top-1 CORSMAL Challenge 2020 Submission: Filling Mass Estimation Using Multi-modal Observations of Human-robot Handovers " 25th International Conference on Pattern Recognition (ICPR2020)
Published in Submitted, 2021
We present results for an innovative approach involving vision and tactile sensing to detect and characterise surface cracks. The proposed algorithm localises surface cracks in a remote environment through videos/photos taken by an on-board robot camera, which is then followed by automatic tactile inspection of the surfaces. Faster R-CNN deep learning-based object detection is used for identifying the location of potential cracks. Random forest classifier is used for tactile identification of the cracks to confirm their presences. Offline and online experiments to compare vision only and combined vision and tactile based crack detection are demonstrated.