Open Access

Robots generate large amounts of data which need to be stored in a meaningful way such that they can be used and interpreted later. Such data can be written into log files, but these files lack the querying features and scaling capabilities of modern databases - especially when dealing with multi-robot systems, where the trade-off between availability and consistency has to be resolved. However, there is a plethora of existing databases, each with its own set of features, but none designed with robotic use cases in mind. This work presents three main contributions: (a) structures for benchmarking scenarios with a focus on networked multi-robot architectures, (b) an extensible workbench for benchmarking databases for different scenarios that makes use of Docker containers and (c) a comparison of existing databases given a set of multi-robot use cases to showcase the usage of the framework. The comparison gives indications for choosing an appropriate database.

Open ended robotic discovery aims at enabling robots to autonomously design and execute sophisticated experiments for gaining conceptual insight about real world. Such experiments are planned activities rather than innate motor commands and thus each single experiment results in a multivariate time series. In such a scenario, reducing the number of features in order to allow a symbolic learner to build a correct conceptual model of underlying phenomena is a fundamental task. Only few feature selection approaches deal with finding relevant features in multivariate time series, which is just what the robot receives through its sensors. In this paper, we present results of applicability of a range of feature selection and time series analysis approaches on a novel real world scenario for autonomous robotic discovery. We found that even sophisticated representations and state of the art techniques, which perform very well on other benchmarks, do not show significant results in context of open ended discovery.

In Artificial Intelligence, numerous learning paradigms have been developed over the past decades. In most cases of embodied and situated agents, the learning goal for the artificial agent is to „map“ or classify the environment and the objects therein [1, 2], in order to improve navigation or the execution of some other domain-specific task. Dynamic environments and changing tasks still pose a major challenge for robotic learning in real-world domains. In order to intelligently adapt its task strategies, the agent needs cognitive abilities to more deeply understand its environment and the effects of its actions. In order to approach this challenge within an open-ended learning loop, the XPERO project (http://www.xpero.org) explores the paradigm of Learning by Experimentation to increase the robot's conceptual world knowledge autonomously. In this setting, tasks which are selected by an actionselection mechanism are interrupted by a learning loop in those cases where the robot identifies learning as necessary for solving a task or for explaining observations. It is important to note that our approach targets unsupervised learning, since there is no oracle available to the agent, nor does it have access to a reward function providing direct feedback on the quality of its learned model, as e.g. in reinforcement learning approaches. In the following sections we present our framework for integrating autonomous robotic experimentation into such a learning loop. In section 1 we explain the different modules for stimulation and design of experiments and their interaction. In section 2 we describe our implementation of these modules and how we applied them to a real world scenario to gather target-oriented data for learning conceptual knowledge. There we also indicate how the goaloriented data generation enables machine learning algorithms to revise the failed prediction model.

Robot deployment in realistic dynamic environments is a challenging problem despite the fact that robots can be quite skilled at a large number of isolated tasks. One reason for this is that robots are rarely equipped with powerful introspection capabilities, which means that they cannot always deal with failures in a reasonable manner; in addition, manual diagnosis is often a tedious task that requires technicians to have a considerable set of robotics skills.

To achieve autonomous mobile robot navigation, one of fundamental requirements is localization technique. It is difficult for any mobile robot to perform assigned task or sequence of tasks autonomously without accurate position knowledge relative to its environment. There are many existing approaches to accomplish localization in mobile robots but some of the existing mobile robot localization techniques can not be used in large indoor environments because of installation and environmental requirements. Many of existing indoor localization solutions costs significantly more as compared to the solution evaluated in this paper. In this paper a novel passive landmark based indoor position estimation technique is investigated for its robustness, accuracy, repeatability and reliability in large public environment. Use of passive artificial landmarks minimize the disturbance to the user, make the installation process easy, scalable and reduces the total system cost. A comprehensive testing arena is developed at Computer Science Department, University of Applied Sciences Bonn-Rhein-Sieg. This technique has been tested here under different environmental conditions i.e different lighting conditions, different sensor angles, different orientations and different ceiling heights. These tests are designed to subject this technique to possible unexpected and expected conditions that could arise in any large indoor public environment e.g supermarkets, museums etc. This makes our testing and experimental evaluation highly valuable before integrating this technique with mobile service robots at a large scale. This paper experimentally evaluates reliability, robustness and accuracy of this technique over other techniques.

The positive influence of physical activity for people at all life stages is well known. Exercising has a proven therapeutic effect on the cardiovascular system and can counteract the increase of cardiovascular diseases in our aging society. An easy and good measure of the cardiovascular feedback is the heart rate. Being able to model and predict the response of a subject’s heart rate on work load input allows the development of more advanced smart devices and analytic tools. These tools can monitor and control the subject’s activity and thus avoid overstrain which would eliminate the positive effect on the cardiovascular system. Current heart rate models were developed for a specific scenario and evaluated on unique data sets only. Additionally, most of these models were tested in indoor environments, e.g. on treadmills and bicycle ergometers. However, many people prefer to do sports in outdoors environments and use their smart phone to record their training data. In this paper, we present an evaluation of existing heart rate models and compare their prediction performance for indoor as well as for outdoor running exercises. For this purpose, we investigate analytical models as well as machine learning approaches in two training sets: one indoor exercise set recorded on a treadmill and one outdoor exercise set recorded by a smart phone.

With the increasing average age of the population in many developed countries, afflictions like cardiovascular diseases have also increased. Exercising has a proven therapeutic effect on the cardiovascular system and can counteract this development. To avoid overstrain, determining an optimal training dose is crucial. In previous research, heart rate has been shown to be a good measure for cardiovascular behavior. Hence, prediction of the heart rate from work load information is an essential part in models used for training control. Most heart-rate-based models are described in the context of specific scenarios, and have been evaluated on unique datasets only. In this paper, we conduct a joint evaluation of existing approaches to model the cardiovascular system under a certain strain, and compare their predictive performance. For this purpose, we investigated some analytical models as well as some machine learning approaches in two scenarios: prediction over a certain time horizon into the future, and estimation of the relation between work load and heart rate over a whole training session.

Currently, a multitude of studies, whitepapers and roadmaps claim to predict the future of robotics and automation. Unfortunately, all of them are to some extent limited in their scope, by design and/or are biased by some particular interests. In most cases this is not even by intention, but the fact, that the euRobotics Multiannual Roadmap and Strategic Research Agenda only focus on the European robotics community limits it to a certain field of view. The same applies for the U.S. Robotics Roadmap and other documents. In this paper, we suggest the methodology for a Delphi-Study – a guided, unbiased and holistic approach based on neutral interviews with internationally well renowned experts. The necessity for a Delphi-Study has already been laid out by the IEEE RAS Industries Activities Board in the IEEE RAM Magazine [6].