Navigation without localisation: reliable teach and repeat based on the convergence theorem

We present a novel concept for teach-and-repeat visual navigation. The proposed concept is based on a mathematical model, which indicates that in teach-and-repeat navigation scenarios, mobile robots do not need to perform explicit localisation. Rather than that, a mobile robot which repeats a previously taught path can simply `replay' the learned velocities, while using its camera information only to correct its heading relative to the intended path. To support our claim, we establish a position error model of a robot, which traverses a taught path by only correcting its heading. Then, we outline a mathematical proof which shows that this position error does not diverge over time. Based on the insights from the model, we present a simple monocular teach-and-repeat navigation method. The method is computationally efficient, it does not require camera calibration, and it can learn and autonomously traverse arbitrarily-shaped paths. In a series of experiments, we demonstrate that the method can reliably guide mobile robots in realistic indoor and outdoor conditions, and can cope with imperfect odometry, landmark deficiency, illumination variations and naturally-occurring environment changes. Furthermore, we provide the navigation system and the datasets gathered at http://www.github.com/gestom/stroll_bearnav.Comment: The paper will be presented at IROS 2018 in Madri

A short curriculum of the robotics and technology of computer lab

Our research Lab is directed by Prof. Anton Civit. It is an interdisciplinary group of 23 researchers that carry out their teaching and researching labor at the Escuela Politécnica Superior (Higher Polytechnic School) and the Escuela de Ingeniería Informática (Computer Engineering School). The main research fields are: a) Industrial and mobile Robotics, b) Neuro-inspired processing using electronic spikes, c) Embedded and real-time systems, d) Parallel and massive processing computer architecture, d) Information Technologies for rehabilitation, handicapped and elder people, e) Web accessibility and usability In this paper, the Lab history is presented and its main publications and research projects over the last few years are summarized.Nuestro grupo de investigación está liderado por el profesor Civit. Somos un grupo multidisciplinar de 23 investigadores que realizan su labor docente e investigadora en la Escuela Politécnica Superior y en Escuela de Ingeniería Informática. Las principales líneas de investigaciones son: a) Robótica industrial y móvil. b) Procesamiento neuro-inspirado basado en pulsos electrónicos. c) Sistemas empotrados y de tiempo real. d) Arquitecturas paralelas y de procesamiento masivo. e) Tecnología de la información aplicada a la discapacidad, rehabilitación y a las personas mayores. f) Usabilidad y accesibilidad Web. En este artículo se reseña la historia del grupo y se resumen las principales publicaciones y proyectos que ha conseguido en los últimos años

Dynamic Compensation Framework to Improve the Autonomy of Industrial Robots

It is challenging to realize the autonomy of industrial robots under external and internal uncertainties. A majority of industrial robots are supposed to be programmed by teaching-playback method, which is not able to handle with uncertain working conditions. Although many studies have been conducted to improve the autonomy of industrial robots by utilizing external sensors with model-based approaches as well as adaptive approaches, it is still difficult to obtain good performance. In this chapter, we present a dynamic compensation framework based on a coarse-to-fine strategy to improve the autonomy of industrial robots while at the same time keeping good accuracy under many uncertainties. The proposed framework for industrial robot is designed along with a general intelligence architecture that is aiming to address the big issues such as smart manufacturing, industrial 4.0

On the role of gestures in human-robot interaction

This thesis investigates the gestural interaction problem and in particular the usage of gestures for human-robot interaction. The lack of a clear definition of the problem statement and a common terminology resulted in a fragmented field of research where building upon prior work is rare. The scope of the research presented in this thesis, therefore, consists in laying the foundation to help the community to build a more homogeneous research field. The main contributions of this thesis are twofold: (i) a taxonomy to define gestures; and (ii) an ingegneristic definition of the gestural interaction problem. The contributions resulted is a schema to represent the existing literature in a more organic way, helping future researchers to identify existing technologies and applications, also thanks to an extensive literature review. Furthermore, the defined problem has been studied in two of its specialization: (i) direct control and (ii) teaching of a robotic manipulator, which leads to the development of technological solutions for gesture sensing, detection and classification, which can possibly be applied to other contexts

Learning Task Priorities from Demonstrations

Bimanual operations in humanoids offer the possibility to carry out more than one manipulation task at the same time, which in turn introduces the problem of task prioritization. We address this problem from a learning from demonstration perspective, by extending the Task-Parameterized Gaussian Mixture Model (TP-GMM) to Jacobian and null space structures. The proposed approach is tested on bimanual skills but can be applied in any scenario where the prioritization between potentially conflicting tasks needs to be learned. We evaluate the proposed framework in: two different tasks with humanoids requiring the learning of priorities and a loco-manipulation scenario, showing that the approach can be exploited to learn the prioritization of multiple tasks in parallel.Comment: Accepted for publication at the IEEE Transactions on Robotic

Control strategy for a flexible analytical chemistry robotics system

This thesis is the result of work carried out during more than two years on a Teaching Company Scheme. Liaison took place between Rhone-Poulenc Agriculture Limited (the industrial partner), hereafter referred to as RPAL or the company, and Middlesex University (the academic partner). The aim of the Scheme was to realise the design, development, commissioning, testing and validation of an intelligent robotic system for sample analysis of trace pesticides and metabolites in order to enable quicker product development. Due to the complexity of the project and the range of technical expertise and skills needed for its implementation, three associates participated in the Programme. I joined as the second associate. With my degree in Industrial Engineering, I have been in overall charge of developing the computational aspects of the system, from control overview to implementation and validation. Two distinct types of studies will be carried out with the robot based system: • Routine extraction of pesticide from soil or plant material, which is compound as well as analyst dependant. • Method development studies, to optimise those routine extraction processes. Traditional strategies of control were not applicable for such system because we were dealing with the automation of a non repetitive process involving non-deterministic operations (evaporation, filtration, etc.). The resulting control system should provide a high degree of flexibility to allow workcell reconfiguration without involving any reprogramming. Modularity is also a must if expansion and upgrading to new technologies and equipment is to involve relatively little cost and effort. In addition, all generated data has to be stored and reported following Good Laboratory Practice (GLP) standards. As the system is both large and flexible in operation, it has proven a real challenge to develop. Software had to be written that can - among its many tasks - allow unrestricted analyst choice, optimise system performance, detect, prioritise and act upon error signals, dynamically schedule robot and instrument operation in real time, trace samples as they pass through the system and generate results as reports stored in databases. The system is now virtually complete, and is presently undergoing the last stages of the validation. Due to the success of this scheme, further cooperative ventures are being planned between Rhone-Poulenc and Middlesex University in both the UK and France