Navigation testing for continuous integration in robotics

Robots working in real-world applications need to be robust and reliable. However, ensuring robust software in an academic development environment with dozens of developers poses a significant challenge. This work presents a testing framework, successfully employed in a large-scale integrated robotics project, based on continuous integration and the fork-and-pull model of software development, implementing automated system regression testing for robot navigation. It presents a framework suitable for both regression testing and also providing processes for parameter optimisation and benchmarking

Plataforma​ ​Robótica​ Para​ Tareas​ de​ Reconstrucción​ Tridimensional​ de​​ Entornos Exteriores

Este artículo presenta los resultados obtenidos en el diseño e implementación de una plataforma robótica todoterreno para la investigación y el desarrollo de aplicaciones de robótica de servicios en entornos exteriores, con especial énfasis en las tareas de reconstrucción tridimensional del entorno. En el documento se describe la estructura mecánica del robot, su arquitectura hardware-software y de comunicaciones y los sistemas perceptivos embarcados. Finalmente, como aportación adicional se presenta un algoritmo diseñado específicamente para llevar a cabo la reconstrucción tridimensional automática y eficiente del entorno, que opera sin necesidad de información previa sobre el mismo. Los resultados avalan la funcionalidad tanto de la plataforma robótica en sí, como de los algoritmos de adquisición y alineación de la información tridimensional, así como de selección automática de las mejores​ ​ posiciones​ ​ de​ ​ escaneo

Spectral analysis for long-term robotic mapping

This paper presents a new approach to mobile robot mapping in long-term scenarios. So far, the environment models used in mobile robotics have been tailored to capture static scenes and dealt with the environment changes by means of ‘memory decay’. While these models keep up with slowly changing environments, their utilization in dynamic, real world environments is difficult. The representation proposed in this paper models the environment’s spatio-temporal dynamics by its frequency spectrum. The spectral representation of the time domain allows to identify, analyse and remember regularly occurring environment processes in a computationally efficient way. Knowledge of the periodicity of the different environment processes constitutes the model predictive capabilities, which are especially useful for long-term mobile robotics scenarios. In the experiments presented, the proposed approach is applied to data collected by a mobile robot patrolling an indoor environment over a period of one week. Three scenarios are investigated, including intruder detection and 4D mapping. The results indicate that the proposed method allows to represent arbitrary timescales with constant (and low) memory requirements, achieving compression rates up to 106 . Moreover, the representation allows for prediction of future environment’s state with ∼ 90% precision

A 3D simulation environment with real dynamics: a tool for benchmarking mobile robot performance in long-term deployments

This paper describes a method to compare and evaluate mobile robot algorithms for long-term deployment in changing environments. Typically, the long-term performance of state estimation algorithms for mobile robots is evaluated using pre-recorded sensory datasets. However such datasets are not suitable for evaluating decision-making and control algorithms where the behaviour of the robot will be different in every trial. Simulation allows to overcome this issue and while it ensures repeatability of experiments, the development of 3D simulations for an extended period of time is a costly exercise. In our approach long-term datasets comprising high-level tracks of dynamic entities such as people and furniture are recorded by ambient sensors placed in a real environment. The high-level tracks are then used to parameterise a 3D simulation containing its own geometric models of the dynamic entities and the background scene. This simulation, which is based on actual human activities, can then be used to benchmark and validate algorithms for long-term operation of mobile robots

Getting to Know Your Robot Customers: Automated Analysis of User Identity and Demographics for Robots in the Wild

Long-term studies with autonomous robots “in the wild” (deployed in real-world human-inhabited environments) are among the most laborious and resource-intensive endeavours in human-robot interaction. Even if a robot system itself is robust and well-working, the analysis of the vast amounts of user data one aims to collect and analyze poses a significant challenge. This letter proposes an automated processing pipeline, using state-of-the-art computer vision technology to estimate demographic factors from users’ faces and reidentify them to establish usage patterns. It overcomes the problem of explicitly recruiting participants and having them fill questionnaires about their demographic background and allows one to study completely unsolicited and nonprimed interactions over long periods of time. This letter offers a comprehensive assessment of the performance of the automated analysis with data from 68 days of continuous deployment of a robot in a care home and also presents a set of findings obtained through the analysis, underpinning the viability of the approach. Inde

FreMEn: Frequency map enhancement for long-term mobile robot autonomy in changing environments

We present a new approach to long-term mobile robot mapping in dynamic indoor environments. Unlike traditional world models that are tailored to represent static scenes, our approach explicitly models environmental dynamics. We assume that some of the hidden processes that influence the dynamic environment states are periodic and model the uncertainty of the estimated state variables by their frequency spectra. The spectral model can represent arbitrary timescales of environment dynamics with low memory requirements. Transformation of the spectral model to the time domain allows for the prediction of the future environment states, which improves the robot's long-term performance in dynamic environments. Experiments performed over time periods of months to years demonstrate that the approach can efficiently represent large numbers of observations and reliably predict future environment states. The experiments indicate that the model's predictive capabilities improve mobile robot localisation and navigation in changing environments

Algorithm for efficient 3D reconstruction of outdoor environments using mobile robots

In this paper, an algorithm for the reconstruction of an outdoor environment using a mobile robot is presented. The focus of this algorithm is making the mapping process efficient by capturing the greatest amount of information on every scan, ensuring at the same time that the overall quality of the resulting 3D model of the environment complies with the specified standards. With respect to existing approaches, the proposed approach is an innovation since there are very few information based methods for outdoor reconstruction that use resulting model quality and trajectory cost estimation as criteria for view planning

Frequency map enhancement: introducing dynamics into static environment models

We present applications of the Frequency Map Enhancement (FreMEn), which improves the performance of mobile robots in long-term scenarios by introducing the notion of dynamics into their (originally static) environment models. Rather than using a fixed probability value, the method models the uncertainty of the elementary environment states by their frequency spectra. This allows to integrate sparse and irregular observations obtained during long-term deployments of mobile robots into memory-efficient spatio-temporal models that reflect mid- and long-term pseudo-periodic environment variations. The frequency-enhanced spatio-temporal models allow to predict the future environment states, which improves the efficiency of mobile robot operation in changing environments. In a series of experiments performed over periods of weeks to years, we demonstrate that the proposed approach improves mobile robot localization, path and task planning, activity recognition and allows for life-long spatio-temporal exploration

Lifelong exploration of dynamic environments

We propose a novel spatio-temporal mobile-robot exploration method for dynamic, human-populated environments. In contrast to other exploration methods that model the environment as being static, our spatio-temporal exploration method creates and maintains a world model that not only represents the environment's structure, but also its dynamics over time. Consideration of the world dynamics adds an extra, temporal dimension to the explored space and makes the exploration task a never-ending data-gathering process to keep the robot's environment model up-to-date. Thus, the crucial question is not only where, but also when to observe the explored environment. We address the problem by application of information-theoretic exploration to world representations that model the environment states' uncertainties as probabilistic functions of time. The predictive ability of the spatio-temporal model allows the exploration method to decide not only where, but also when to make environment observations. To verify the proposed approach, an evaluation of several exploration strategies and spatio-temporal models was carried out using real-world data gathered over several months. The evaluation indicates that through understanding of the environment dynamics, the proposed spatio-temporal exploration method could predict which locations were going to change at a specific time and use this knowledge to guide the robot. Such an ability is crucial for long-term deployment of mobile robots in human-populated spaces that change over time