Modelling and solving the multi-day container drayage problem

This paper deals with a general Multi-Day Container Drayage Problem (MDCDP) that consists in assigning trucks to container transportation orders during several days. To this aim, a Mixed Integer Linear Programming problem is formulated: the model describes real problems taking into account the orders to be planned for several days, the types of the containers and the rest periods of drivers. In order to address real scenarios, a heuristic algorithm based on the rolling horizon approach is proposed. Some randomly generated MDCDP instances validate the heuristic algorithm and a case study of real dimensions shows the effectiveness of the proposed solution technique

Improved humanoid vocalization acquisition from a human tutor

This paper describes an approach which allow a humanoid robot to automatically acquire vocalization capability by learning from a human tutor. The proposed algorithm can, at the same time, synthesize speech utterances from unrestricted text and generate facial movements of the humanoid head synchronized with the generated speech. The algorithm uses fuzzy articulatory rules, derived from the International Phonetic Alphabet (IPA) to allow simpler adaptation to different languages, and genetic optimization of the membership degrees. Experimental results show a good subjective acceptance of the acquired vocalization in terms of quality, naturalness and synchronization. Although the algorithm has been implemented on a virtual talking face, it could eventually be used also in mechanical vocalization systems

TOWARDS ARTICULATORY CONTROL OF TALKING HEADS IN HUMANOID ROBOTICS USING A GENETIC-FUZZY IMITATION LEARNING ALGORITHM

In human heads there is a strong structural linkage between the vocal tract and facial behavior during speech. For a robotic talking head to have human-like behavior, this linkage should be emulated. One way to do that is to estimate the articulatory features from a given utterance and to use them to control a talking head. In this paper, we describe an algorithm to estimate the articulatory features from a spoken sentence using a novel computational model of human vocalization. Our model uses a set of fuzzy rules and genetic optimization. That is, the places of articulation are considered as fuzzy sets whose degrees of membership are the values of the articulatory features. The fuzzy rules represent the relationships between places of articulation and speech acoustic parameters, and the genetic algorithm estimates the degrees of membership of the places of articulation according to an optimization criteria and it performs imitation learning. We verify our model by performing audio-visual subjective tests of animated talking heads showing that the algorithm is able to produce correct results. In particular, subjective listening tests of artificially generated sentences from the articulatory description resulted in an average phonetic accuracy slightly under 80%. Through the analysis of large amounts of natural speech, the algorithm can be used to learn the places of articulation of all phonemes of a given speaker. The estimated places of articulation are then used to control talking heads in humanoid robotics

Omni-directional non-visual perception for human interactions with service robots

The development of suitable tools for human-robot interaction is very important in the service robotics field. To this aim, in this paper we present a system to allows mobile robots to interact with human beings using non-visual perception. Our approach allows a human being to monitor the behaviors of a group of robots by means of non-visual perception using the acoustic channel. Thus, an acoustic localization is used as the basis for the non-visual interaction, while the non-visual perception is used also for multi-robot coordination. In this context, humans can easily understand the robots\u2019 messages by just listening to them. Some points related to this work are worth remarking here: first acoustic localization is used as the basis for the non-visual interaction. Second, non-visual perception has been also used for multi-robot navigation. Third, human-robot interaction is restricted to non-visual monitoring. The location of acoustic sources is detected using a circular microphone array installed on each robot and a neural network. The localization information is used for avoiding collision during the robots movements. However, the localization is perturbed by uncertainties; for this reason, fuzzy rules are used for finding collision-free path for the mobile robots

Algorithms for acoustic localization based on microphone array in service robotics

This paper deals with the development of acoustic source localization algorithms for service robots working in real conditions. One of the main utilizations of these algorithms in a mobile robot is that the robot can localize a human operator and eventually interact with him/herself by means of verbal commands. The location of a speaking operator is detected with a microphone array based algorithm; localization information is passed to a navigation module which sets up a navigation mission using knowledge of the environment map. In fact, the system we have developed aims at integrating acoustic, odometric and collision sensors with the mobile robot control architecture. Good performance with real acoustic data have been obtained using neural network approach with spectral subtraction and a noise robust voice activity detector. The experiments show that the average absolute localization error is about 40 cm at 0 dB and about 10 cm at 10 dB of SNR for the named localization. Experimental results describing mobile robot performance in a talker following task are reported

Spatial map building using fast texture analysis of rotating sonar sensor data for mobile robots

This paper presents a novel, fast algorithm for accurate detection of the shape of targets around a mobile robot using a single rotating sonar element. The rotating sonar yields an image built up by the reflections of an ultrasonic beam directed at different scan angles. The image is then interpreted with an image-understanding approach based on texture analysis. Several important tasks are performed in this way, such as noise removal, echo correction and restoration. All these processes are obtained by estimating and restoring the degree of texture continuity. Texture analysis, in fact, allows us to look at the image on a large scale thus giving the possibility to infer the overall behavior of the reflection process. The algorithm has been integrated in a mobile robot. However, the algorithm is not suitable for working during the mobile robot movement, rather it can be used during the period when the robot stays in a fixed position

A genetic-fuzzy algorithm for the articulatory imitation of facial movements during vocalization of a humanoid robot

In human heads there is a strong structural linkage between vocal tract and facial behavior during speech. For a robotic talking head to have a human-like behavior, this linkage should be emulated. One way to do that is to compute an estimate of the articulatory features which produce a given utterance and then to transform them into facial animation. We present a computational model of human vocalization which is aimed at describing the articulatory mechanisms which produce spoken phonemes. It uses a set of fuzzy rules and genetic optimization. The former represents the relationships between places of articulations and speech acoustic parameters, while the latter estimates the degrees of membership of the places of articulation. That is, the places of articulation are considered as fuzzy sets whose degrees of membership are the articulatory features. The trajectories of articulatory parameters can be used to control a graphical or mechanical talking head. We verify the model presented here by generating and listening to artificial sentences. Subjective listening tests of artificially generated sentences from the articulatory description resulted in an average phonetic accuracy of about 79 %. Through the analysis of a large amount of natural speech, the algorithm can be used to learn the places of articulation of all phonemes of a given speaker