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Dept. of Physics, Systems Engineering and Signal Theory, University of Alicante, San Vicente del Raspeig, Alicante, Spain, Computer Science Research Institute, University of Alicante, San Vicente del Raspeig, Alicante and Spain
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5 Papers

Touch Detection with Low-cost Visual-based Sensor

Topics: Robot Sensing Systems; Intelligent Robots; Learning and Adaptive Systems

Authors: Julio Castaño-Amoros , Pablo Gil and Santiago Puente

Affiliation: AUROVA Lab, Computer Science Research Institute, University of Alicante, Alicante 03690, Spain

Digital Object Identifier: 10.5220/0010699800003061

Proceedings of ROBOVIS 2021, ISBN: 978-989-758-537-1

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Robotic manipulation continues being an unsolved problem. It involves many complex aspects, for example, perception tactile of different objects and materials, grasping control to plan the robotic hand pose, etc. Most of previous works on this topic used expensive sensors. This fact makes difficult the application in the industry. In this work, we propose a grip detection system using a low-cost visual-based tactile sensor known as DIGIT, mounted on a ROBOTIQ gripper 2F-140. We proved that a Deep Convolutional Network is able to detect contact or no contact. Capturing almost 12000 images with contact and no contact from different objects, we achieve 99% accuracy with never seen samples, in the best scenario. As a result, this system will allow us to implement a grasping controller for the gripper.

3DCNN Performance in Hand Gesture Recognition Applied to Robot Arm Interaction

Topics: Robotics; Motion Tracking and Action Recognition; Image and Video Analysis and Understanding

Authors: J. A. Castro-Vargas 1 , B. S. Zapata-Impata 1 , P. Gil 2 , J. A. Garcia-Rodriguez 3 and F. Torres 2

Affiliations: 1 Dept. of Physics, Systems Engineering and Signal Theory, University of Alicante, San Vicente del Raspeig, Alicante and Spain , 2 Dept. of Physics, Systems Engineering and Signal Theory, University of Alicante, San Vicente del Raspeig, Alicante, Spain, Computer Science Research Institute, University of Alicante, San Vicente del Raspeig, Alicante and Spain , 3 Dept. of Computer Technology, University of Alicante, San Vicente del Raspeig, Alicante, Spain, Computer Science Research Institute, University of Alicante, San Vicente del Raspeig, Alicante and Spain

Digital Object Identifier: 10.5220/0007570208020806

Proceedings of ICPRAM 2019, ISBN: 978-989-758-351-3

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In the past, methods for hand sign recognition have been successfully tested in Human Robot Interaction (HRI) using traditional methodologies based on static image features and machine learning. However, the recognition of gestures in video sequences is a problem still open, because current detection methods achieve low scores when the background is undefined or in unstructured scenarios. Deep learning techniques are being applied to approach a solution for this problem in recent years. In this paper, we present a study in which we analyse the performance of a 3DCNN architecture for hand gesture recognition in an unstructured scenario. The system yields a score of 73% in both accuracy and F1. The aim of the work is the implementation of a system for commanding robots with gestures recorded by video in real scenarios.
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Robotics

Software Engineering
Pattern Recognition

Candidate Oil Spill Detection in SLAR Data

Topics: Neural Networks; Video Analysis; Classification; Signal Processing; Object Recognition; Learning in Process Automation

Authors: Sergiu-Ovidiu Oprea , Pablo Gil , Damian Mira and Beatriz Alacid

Affiliation: University of Alicante, Spain

Digital Object Identifier: 10.5220/0006187103720377

Proceedings of ICPRAM 2017, ISBN: 978-989-758-222-6

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Intentional oil pollution damages marine ecosystems. Therefore, society and governments require maritime surveillance for early oil spill detection. The fast response in the detection process helps to identify the offenders in the vast majority of cases. Nowadays, it is a human operator whom is trained for carrying out oil spill detection. Operators usually use image processing techniques and data analysis from optical, thermal or radar acquired from aerial vehicles or spatial satellites. The current trend is to automate the oil spill detection process so that this can filter candidate oil spill from an aircraft as a decision support system for human operators. In this work, a robust and automated system for candidate oil spill based on Recurrent Neural Network (RNN) is presented. The aim is to provide a faster identification of the candidate oil spills from SLAR scanned sequences. So far, the majority of the research works about oil spill detection are focused on the classification between real oil spills and look-alikes, and they use SAR or optical images but not SLAR. Furthermore, the overall decision is usually taken by an operator in the research works of state-of-art, mainly due to the wide variety of types of look-alikes which cause false positives in the detection process when traditional NN are used. This work provides a RRN-based approach for candidate oil spill detection using SLAR data in contrast with the traditional Multilayer Perceptron Neural Network (MPNN). The system is tested with temporary data acquired from a SLAR sensor mounted on an aircraft. It achieves a success rate in detecting of 97%.

Oil Spill Detection using Segmentation based Approaches

Topics: Feature Selection and Extraction; Signal Processing; Image Understanding; Object Recognition

Authors: D. Mira , P. Gil , B. Alacid and F. Torres

Affiliation: University of Alicante, Spain

Digital Object Identifier: 10.5220/0006191504420447

Proceedings of ICPRAM 2017, ISBN: 978-989-758-222-6

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This paper presents a description and comparison of two segmentation methods for the oil spill detection in the sea surface. SLAR sensors acquire video sequences from which snapshots are extracted for the detection of oil spills. Both approaches are segmentation based on graph techniques and J-image respectively. Finally, the aim of applying both approaches to SLAR snapshots, as shown, is to detect the largest part of the oil slick and minimize the false detection of the spill.

Using Geometry to Detect Grasping Points on 3D Unknown Point Cloud

Topics: Vision, Recognition and Reconstruction; Autonomous Agents; Mobile Robots and Autonomous Systems; Cognitive Robotics

Authors: Brayan S. Zapata-Impata , Carlos M. Mateo , Pablo Gil and Jorge Pomares

Affiliation: University of Alicante, Spain

Digital Object Identifier: 10.5220/0006470701540161

Proceedings of ICINCO 2017, ISBN: 978-989-758-264-6

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In this paper, we focus on the task of computing a pair of points for grasping unknown objects, given a single point cloud scene with a partial view of them. The main goal is to estimate the best pair of 3D-located points so that a gripper can perform a stable grasp over the objects in the scene with no prior knowledge of their shape. We propose a geometrical approach to find those contact points by placing them near a perpendicular cutting plane to the object’s main axis and through its centroid. During the experimentation we have found that this solution is fast enough and gives sufficiently stable grasps for being used on a real service robot.

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