Analysis of methods for reducing line segments in maps: Towards a general approach

International audienceSegment-based maps are emerging as an efficient way to represent the environments in which mobile robots operate. When compared to grid-based maps, maps composed of line segments usually need less space to be stored. However, very little effort has been devoted to methods that allow to reduce the size of segment-based maps by removing redundant line segments that represent the same object in the environment. This problem is usually addressed with rather ad hoc methods that are embedded in mapping systems. In this paper, we put forward the problem of reducing the size of segment-based maps by presenting a survey of the existing methods and by experimentally evaluating some of them. Our results can be used to set out some guidelines for the development of a general approach to reducing redundant line segments in maps

A Cooperative Negotiation Protocol for Physiological Model Combination

The global model of a complex phenomenon can emerge from the cooperative negotiation of agents embedding local partial models of the phenomenon. We adopted this approach to model complex physiological phenomena, such as those related to the metabolism of glucose-insulin and to the determination of the heart rate (pacing). In this paper we formally describe and analyze the properties of a cooperative negotiation protocol we developed to allow the agents to produce a global coherent model of a physiological phenomenon. We concentrate on the study of the conditions under which an agreement is guaranteed to be reached. We also show details of an application concerning the pacing problem

HMMs for Anomaly Detection in Autonomous Robots

Detection of anomalies and faults is a key element for long-term robot autonomy, because, together with subsequent diagnosis and recovery, allows to reach the required levels of robustness and persistency. In this paper, we propose an approach for detecting anomalous behaviors in autonomous robots starting from data collected during their routine operations. The main idea is to model the nominal (expected) behavior of a robot system using Hidden Markov Models (HMMs) and to evaluate how far the observed behavior is from the nominal one using variants of the Hellinger distance adopted for our purposes. We present a method for online anomaly detection that computes the Hellinger distance between the probability distribution of observations made in a sliding window and the corresponding nominal emission probability distri- bution. We also present a method for onine anomaly detection that computes a variant of the Hellinger distance between two HMMs representing nominal and observed behaviors. The use of the Hellinger distance positively impacts on both detection performance and interpretability of detected anomalies, as shown by results of experiments performed in two real-world application domains, namely, water monitoring with aquatic drones and socially assistive robots for elders living at home. In particular, our approach improves by 6% the area under the ROC curve of standard online anomaly detection methods. The capabilities of our online method to discriminate anomalous behaviors in real-world applications are statistically proved

RoCKIn Benchmarking and Scoring System

The main innovation brought forth by the European Project RoCKIn is the definition, implementation and application to an actual robot competition of the novel paradigm of benchmarking through competitions. By doing so, RoCKIn set in motion an evolutionary process to transform robot competitions from successful showcases with limited scientific impact into benchmarking tools for the consistent and objective evaluation of the performance of autonomous robot systems. Our work began by revisiting, in the light of the features and limitations of a competition setting, the very foundations of the scientific method; then we built on these by designing a novel type of competitions where the concepts of benchmark and objective performance metrics are the key points; finally, we arrived to the implementation of such concepts in the form of a real-world robot competition. This chapter describes the above process, explaining how each of its several aspects (theoretical, technical, procedural) has been tackled by RoCKIn. Special attention will be devoted to the problems of defining performance metrics and of capturing the ground truth needed to reliably assess robot perceptions and actions