Showing papers by "Joaquin Carrasco published in 2019"

Intuitive Bare-Hand Teleoperation of a Robotic Manipulator Using Virtual Reality and Leap Motion

Abstract: Despite various existing works on intuitive human-robot interaction (HRI) for teleoperation of robotic manipulators, to the best of our knowledge, the following research question has not been investigated yet: Can we have a teleoperated robotic manipulator that simply copies a human operator’s bare hand posture and gesture in a real-time manner without having any hand-held devices? This paper presents a novel teleoperation system that attempts to address this question. Firstly, we detail how to set up the system practically by using a Universal Robots UR5, a Robotiq 3-finger gripper, and a Leap Motion based on Unity and ROS, and describe specifically what information is communicated between each other. Furthermore, we provide the details of the ROS nodes developed for controlling the robotic arm and gripper, given the information of a human’s bare hands sensed by the Leap Motion. Then, we demonstrate our system executing a simple pick-and-place task, and discuss possible benefits and costs of this HRI concept.

Elbow Detection in Pipes for Autonomous Navigation of Inspection Robots

Abstract: Nuclear decommissioning is a global challenge with high costs associated with it due to the hazardous environments created by radioactive materials. Most nuclear decommissioning sites contain significant amounts of pipework, the majority of which is uncharacterised with regards radioactive contamination. If there is any uncertainty as to the contamination status of a pipe, it must be treated as contaminated waste, which can lead to very high disposal costs. To overcome this challenge, an in-pipe autonomous robot for characterisation is being developed. One of the most significant mechatronic challenges with the development of such a robot is the detection of elbows in the unknown pipe networks to allow the robotic system to autonomously navigate around them. This paper presents a novel method of predicting the direction and radius of the corner using whisker-like sensors. Experiments have shown that the proposed system has a mean error of 4.69∘ in the direction estimation.

Conditions for the equivalence between IQC and graph separation stability results

Abstract: This paper provides a link between time-domain and frequency-domain stability results in the literature. Specifically, we focus on the comparison between stability results for a feedback in...

IQC analysis of reset control systems with time-varying delay

Abstract: The study of input–output stability of reset control systems with time-varying delay is addressed in this work. The time-varying function that defines the delay is assumed to be bounded on ...

Input-Output Stability of Barrier-Based Model Predictive Control.

Abstract: Conditions for input-output stability of barrier-based model predictive control of linear systems with linear and convex nonlinear (hard or soft) constraints are established through the construction of integral quadratic constraints (IQCs). The IQCs can be used to establish sufficient conditions for global closed-loop stability. In particular conditions for robust stability can be obtained in the presence of unstructured model uncertainty. IQCs with both static and dynamic multipliers are developed and appropriate convex searches for the multipliers are presented. The effectiveness of the robust stability analysis is demonstrated with an illustrative numerical example.

A Navigational System for Quadcopter Remote Inspection of Offshore Substations

Abstract: Effective and safe maintenance of offshore infrastructure is hampered by its remote location. Robotic inspection can provide a retrofit solution, improving safety for human personnel by removing them from a potentially hazardous environment, and also reduce operational costs. There are three primary challenges for navigation around an offshore substation: low visibility, high electromagnetic fields and the absence of Global Positioning System (GPS) signals. This paper details a navigational system that enables Unmanned Aerial Vehicles (UAVs) to operate within a dark and GPS-denied environment. Keywords–Robotics In Hazardous Fields; Aerial Robotics; SLAM; Sensor-based Control

Omnipotent Virtual Giant for Remote Human-Swarm Interaction

Abstract: This paper proposes an intuitive human-swarm interaction framework inspired by our childhood memory in which we interacted with living ants by changing their positions and environments as if we were omnipotent relative to the ants. In virtual reality, analogously, we can be a super-powered virtual giant who can supervise a swarm of mobile robots in a vast and remote environment by flying over or resizing the world and coordinate them by picking and placing a robot or creating virtual walls. This work implements this idea by using Virtual Reality along with Leap Motion, which is then validated by proof-of-concept experiments using real and virtual mobile robots in mixed reality. We conduct a usability analysis to quantify the effectiveness of the overall system as well as the individual interfaces proposed in this work. The results revealed that the proposed method is intuitive and feasible for interaction with swarm robots, but may require appropriate training for the new end-user interface device.

Improving thermal substation inspections utilising machine learning

Abstract: Periodic thermal imaging inspection of air-insulated substations can lead to false negatives due to the heating effects of solar radiation and cooling effects of wind and precipitation. This work aims to characterize the effects of wind on thermal images of thermally loaded equipment, allowing thermal response forecasts to be made. Data is collected in two load patterns from an indoors experiment, comprising a current loop of two overhead- line conductors energized by a high-current DC power supply. Wind is emulated by an industrial fan. Infrared images, environmental data (ambient temperature, humidity, pressure, wind speed and direction) and electrical load data are all captured periodically. A further dataset from an in-service substation is used. Models are created using vector autoregressive and long short-term memory recurrent neural network models in order to further develop the methods presented by Bortoni et al. The results display a clear improvement over those found in the literature, highlighting the utility of modern data-processing techniques. These results present an opportunity to extract meaningful information for long term thermal condition monitoring of power substations.