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This paper studies a day-ahead scheduling problem on power generation of thermal power plants and charge/discharge of battery energy storage systems, where the confidence intervals of the prediction of photovoltaic (PV) and demand power are available. This problem is reduced to an interval optimization problem, where parameters having any values in certain intervals are included. We have developed a method for efficiently solving this kind of problems using tools from the interval analysis. However, the class of the problem that we have studied there is limited in the sense that the output capacity constraints of thermal power plants are not taken into account. This paper proposes a new method for efficiently solving a more practical scheduling problem with the capacity constraints of thermal power plants added. To use our method, we need to analyze a matrix corresponding to a Jacobian of a solution to an optimization with respect to parameters on demand deviation, which is too complex to analyze in general. The key to overcome this difficulty is that we introduce a virtual thermal power plant whose output capacity constraint is not imposed. By using this idea, we can transform the above matrix into an appropriate matrix that is analyzed as easily as possible. The efficiency of the proposed method is shown by numerical simulations on the power system in the Tokyo area.

International audience; The problem of observer design is addressed for a class of triangular nonlinear systems with not-necessarily small delay and sampled output measurements. One more difficulty is that the system state matrix is dependent on the un-delayed output signal which is not accessible to measurement, making existing observers inapplicable. A new chain observer, composed of m elementary observers in series, is designed to compensate for output sampling and arbitrary large delays. The larger the time-delay the larger the number m. Each elementary observer includes an output predictor that is conceived to compensate for the effects of output sampling and a fractional delay. The predictors are defined by first-order ordinary differential equations (ODEs) much simpler than those of existing predictors which involve both output and state predictors. Using a small gain type analysis, sufficient conditions for the observer to be exponentially convergent are established in terms of the minimal number m of elementary observers and the maximum sampling interval.

This version corrects some typos in the published version.; International audience; Regional stability analysis of linear systems with multi-rate samplers and actuator saturation is studied. A hybrid controller is used to perform a fusion of measurements sampled at different times. In between sampling events, the controller behaves as a copy of the plant. When a new measurement is available, the controller state undergoes a jump. The resulting system is analyzed in a hybrid system framework. Sufficient conditions in the form of matrix inequalities are given to determine estimates of the basin of attraction of the closed-loop system. Finally, the effectiveness of the proposed methodology is shown in an example.

International audience; The Internet of Things (IoT) refers to the domain of physical/logical objects connected to the Internet that can be accessible anywhere and anytime, giving the access policies. Web objects and services can be seen as distributed and cooperative agents that need to collaborate in order to reach advanced functionalities and also to optimize the overall quality offered to the end users. However, the number of the services that can be supplied through these devices on the Internet will increase more and more. In this context, performing the composition and selection of the best services according to quality-of-service (QoS) criteria become a complex task, especially when dealing with global service selection to satisfy global constraints of users in decentralized environments. For this purpose, we propose a distributed and optimal QoS selection approach based on the multiagent paradigm and the distributed constraint optimization problem (DCOP) formalism. Hence, we revisited a DCOP technique for developing an efficient algorithm: the DPOP4QoS implemented under a real-time Web protocol for IoT communication, the extensible messaging and presence protocol. The proposed algorithm takes into account the specificities of the service composition context and the satisfaction of the global user constraints. This paper also presents a set of experiments conducted under realistic distributed environment to evaluate the performance of the proposed algorithm.

International audience; We propose and evaluate a three-dimensional (3D) face recognition approach that applies the speeded up robust feature (SURF) algorithm to the depth representation of shape index map, under real-world conditions, using only a single gallery sample for each subject. First, the 3D scans are preprocessed, then SURF is applied on the shape index map to find interest points and their descriptors. Each 3D face scan is represented by keypoints descriptors, and a large dictionary is built from all the gallery descriptors. At the recognition step, descriptors of a probe face scan are sparsely represented by the dictionary. A multitask sparse representation classification is used to determine the identity of each probe face. The feasibility of the approach that uses the SURF algorithm on the shape index map for face identification/authentication is checked through an experimental investigation conducted on Bosphorus, University of Milano Bicocca, and CASIA 3D datasets. It achieves an overall rank one recognition rate of 97.75%, 80.85%, and 95.12%, respectively, on these datasets.

International audience; In the present paper, a method based on a new concept called power fuzzy soft set is proposed for multi-observer decision making problems under uncertain information. The new method applies a weighted conjunctive operator to aggregate these sets into a reliable resultant power fuzzy soft set from the input data set. To decide among the alternatives, a new ranking algorithm is introduced. The effectiveness and feasibility of this method are demonstrated by comparing it to algorithms based on the maximum score in decision making.

International audience; In recent years, a plethora of different studies for design of traditional ensemble classifiers has been proposed in order to improve final recognition accuracy. However, among the ensemble classifiers, combination methods are focused on building independent classifiers of the same or different algorithms using majority voting methods. In this paper, we present a new fusion scheme for ensemble classifiers based on a new concept called Generalized Fuzzy Soft Set (GFSS), which we apply in activity classification. Essentially, we apply a weighted aggregate operator to the output of each classifier in order to fuse the GFSS into a more reliable classifier. The proposed fusion method is based on a new ranking algorithm to classify activities. We show that the proposed method produces more accurate results than the best single classifier and its effectiveness is demonstrated by comparing it with single classifier in terms of activity recognition accuracy.

International audience; In order to alleviate the negative effect of curse of dimensionality, band selection is a crucial step for hyperspectral image (HSI) processing. In this letter, we propose a novel unsupervised band selection approach to reduce the dimensionality for hyperspectral imagery. In order to obtain the most representative bands, the correlation matrix computed from the original HSI is used to describe the correlation characteristics among bands, while the block-diagonal structure is measured to segment all bands into a series of subspace. After applying the spectral clustering algorithm, the optimal combination of band is finally selected. To verify the effectiveness and superiority of the proposed band selection method, experiments have been conducted on three widely used real-world hyperspectral data. The results have shown that the proposed method outperforms other methods in HSI classification application.

In this paper, an active impedance control strategy for a knee-joint orthosis is proposed to assist individuals suffering from lower-limb muscular weaknesses during the swing phase of walking activities. The goal of the proposed strategy is to decrease the human effort required for ensuring a successful knee joint movement during walking without sacrificing the wearer's control priority. In this study, a gait-phase based desired knee-joint admittance model is designed by analyzing the kinematic and kinetic characteristics of the wearer's shank-foot segment during walking. Moreover, the mechanical impedance of the human/orthosis system is adapted to the desired one using an active impedance compensation. The control approach was implemented using a knee joint orthosis and tested with four healthy subjects. The EMG signals of the short head of the biceps femoris and the vastus medialis are used as metrics to evaluate the effectiveness of the proposed strategy. The experimental results show that the human effort can be significantly decreased when the wearers are assisted using the proposed approach.

International audience; The emergence of high-dimensional data requires the design of new optimization methods. Indeed, conventional optimization methods require improvements, hybridization, or parameter tuning in order to operate in spaces of high dimensions. In this paper, we present a new adaptive variant of a pattern search algorithm to solve global optimization problems exhibiting such a character. The proposed method has no parameters visible to the user and the default settings, determined by almost no a priori experimentation, are highly robust on the tested datasets. The algorithm is evaluated and compared with 11 state-of-the-art methods on 20 benchmark functions of 1000 dimensions from the CEC’2010 competition. The results show that this approach obtains good performances compared to the other methods tested.

International audience; This article proposes a robust PID adaptive controller for nonlinear systems with one or more degrees of freedom (DoF). The adaptive controller aims at minimizing the errors in trajectory tracking without requiring a prior modeling of the targeted nonlinear system. Furthermore, the proposed controller requires only the inputs and outputs of the system. And it is based on modified particle swarm optimization algorithm whose goal is to find the best PID parameters that optimize the execution of desired task by minimizing an objective function. The adaptation by the controller addresses two critical problems: The first problem is the instability of the control signal provided by the convergence phase of the classical PSO algorithm. This behavior adversely affects the lifetime of any actuator and, therefore, is undesirable. The second problem is the stagnation of the classical PSO algorithm after convergence at the immediately found optimal solution. The proposed adaptive PID controller is initially tested in simulation on a dynamical model of a robot manipulator evolving in the vertical plan. Which is followed by experimental tests performed on an actuated joint orthosis worn by human subjects having different morphologies. A comparative study with two other algorithms has been also conducted. Based on the obtained results, we conclude the efficiency of the proposed approach.

International audience; This paper presents a knowledge-based engineering framework for the design, deployment and running of context-aware monitoring agents that are dedicated to ambient assisted living applications. A new modeling approach of agent knowledge, that combines the advantages of both ontologies and object oriented modeling and programming, is proposed. In this approach, the agents’ logic is implemented using a micro-ontology and production rules based on the closed world assumption, called smart rules. These rules are managed using a standard reasoning system embedded in the agent core. Unlike semantic web approaches, the proposed approach rely on the closed world and unique name assumptions. These features are required for monitoring purposes in ambient intelligence and robotics domains. We present a practical work, where monitoring agents are instantiated in the user environment and their reasoning rules operate to handle the detection and confirmation of abnormal and emergency situations with respect to user’s context. These rules allow the agents to trigger appropriate actions with help of companion robot.

International audience; no abstract

An efficient covering of the search space is an important issue when dealing with metaheuristics. Sensitivity analysis methods aim at evaluating the influence of each variable of a problem on a mod...

International audience; This paper deals with the control of an active ankle foot orthosis (AAFO) to assist the gait of paretic patients. The AAFO system is driven by both, the residual human torque delivered by the muscles spanning the ankle joint and the AAFO's actuator's torque. A projection-based model reference adaptive control is proposed to assist dorsiflexion and plantar-flexion of the ankle joint during daily living walking activities. Unlike most classical model-based controllers, the proposed one does not require any prior estimation of the system's (foot-AAFO) parameters. The ankle reference trajectory was extracted from healthy subjects gait activities in a clinical environment. The input-to-state stability of the foot-AAFO system with respect to a bounded human muscular torque is proved in closed-loop based on a Lyapunov analysis. Preliminary experimental results with a healthy subject walking on a treadmill, show satisfactory results in terms of tracking performance and ankle assistance throughout the gait cycle.

International audience; This paper presents a bounded control strategy of a knee-joint exoskeleton that ensures knee flexion/extension movements during the assistance and rehabilitation processes. The control strategy is easy to implement, since it does not require any prior knowledge of the human effort. The proposed control law is based on nested saturations with the main advantage of explicitly avoiding the actuator's saturation, guaranteeing and enhancing the wearer's security. Lyapunov-based analysis is stated to prove: 1) the asymptotic stability of the shank-foot-exoskeleton in the absence of a human active effort; 2) the trajectories ultimate boundedness in the presence of parameter uncertainties; and 3) the input-to-state stability with respect to a bounded human active torque. Preliminary real-time experiments show satisfactory results in terms of assistance-as-needed and passive/resistive rehabilitation performances following desired trajectories expressing knee flexion/extension movements.

Accepted; International audience; The present study proposes a new approach for the assessment of the human balance control. This approach is based on the decomposition of the center of pressure displacement using empirical mode decomposition (EMD) that provides an effective time-frequency analysis of non-stationary signals. Twenty-eight healthy subjects performed quiet standing in four conditions—feet apart/together with respect to eyes open/closed—while recording the stabilometric signals in the anteroposterior (AP) and mediolateral (ML) directions. The EMD method decomposes each stabilometric signal into several subsignals called intrinsic mode functions (IMFs). Stabilogram-diffusion analysis technique is applied to generate the diffusion curve of each IMF signal. Each diffusion curve is modeled as a second-order system and provides representative features, such as the gain parameter. Analysis of the gain parameter shows the major effect of visual input and feet conditions on the strategy to control/stabilize the balance. Significant differences were found between young and elderly, and between women and men. In addition, the impact of feet position seems to be higher in ML direction than in AP direction.

International audience; Considering the evolution of neuroimaging in the medical field, some new emerging biometric modalities become interesting and promising candidates to recognize persons. These modalities are considered as a part of “Hidden Biometrics” which consists in using clinical measurements and medical imaging for recognition purpose. The main motivation in using hidden biometrics is the fact that system attacks may be extremely difficult to consider. This specificity highly contributes in increasing the robustness in terms of person verification and identification. In this paper, we deal with a novel non-invasive approach to recognize persons by extracting a brain signature, called “brainprint”. In particular, we explored the brain cortical regions of volumetric brain MRI (Magnetic Resonance Imaging) images, acquired from 220 healthy subjects. For each subject, four 3D cortical surfaces are considered, then transformed into 2D cortical folds maps. From the resulting textures, brainprints are constructed by extracting features using Wavelet Gabor Transform. These brainprints are considered in this work as a discriminative signature of the brain. In terms of performance evaluation, we show that an EER = 2.90 ± 0.47 is reached for verification mode. On the other hand, when dealing with identification, the proposed approach allows a recognition rate of 99.6%.

International audience; Facial ageing modelling has been an active research topic in the field of anthropology. Considering the fact that ageing is a non-uniform and a non-linear process for different face types (e.g. origins, gender etc.), dealing with a reliable face-ageing model may considerably help investigators working in some specific fields such as forensics. Unlike numerous studies dealing with forward or predictive face models, in this study, the authors propose a backward model aiming at estimating childhood face images using their corresponding adult face appearance as an input. For the proposed approach, face contour and different components are modified non-linearly, based on an estimated geometrical model. On the other hand, the face texture is estimated by mapping a reference face texture to the estimated geometrical model. This approach will show that it will be possible to `digitally' rejuvenate an adult person's face down to it being 3-4 years old. For evaluation purposes, a database has been created from 112 subjects. Results have been evaluated using both objective (face recognition system) and subjective (human perception) criteria. The most promising and interesting results will be highlighted further ahead.

International audience; In this work we contribute to development of a “Human-like Visual-Attention-based Artificial Vision” system for boosting firefighters’ awareness about the hostile environment in which they are supposed to move along. Taking advantage from artificial visual-attention, the investigated system’s conduct may be adapted to firefighter’s way of gazing by acquiring some kind of human-like artificial visual neatness supporting firefighters in interventional conditions’ evaluation or in their appraisal of the rescue conditions of people in distress dying out within the disaster. We achieve such a challenging goal by combining a statistically-founded bio-inspired saliency detection model with a Machine-Learning-based human-eye-fixation model. Hybridization of the two above-mentioned models leads to a system able to tune its parameters in order to fit human-like gazing of the inspected environment. It opens appealing perspectives in computer-aided firefighters’ assistance boosting their awareness about the hostile environment in which they are supposed to evolve. Using as well various available wildland fires images’ databases as an implementation of the investigated concept on a 6-wheeled mobile robot equipped with communication facilities, we provide experimental results showing the plausibility as well as the efficiency of the proposed system.

Biogeography-based optimization (BBO) is an evolutionary algorithm which is inspired by the migration of species between habitats. Almost 10 years have passed since the first BBO paper was published in 2008. BBO has successfully solved optimization problems in many different domains and has reached a relatively mature state. Considering the significant and expanding research on BBO and its applications, we find that the time is right to provide a 10-year anniversary review of the published literature, and also to point out some important avenues of future research. The purpose of this paper is to summarize and organize the literature related to the past 10 years of BBO research. Beginning with a foundation of basic BBO, we review the family of BBO algorithms and discuss BBO modifications, hybridizations, applications in science and engineering, and mathematical theory. Finally, the paper presents some interesting open problems and future research directions for BBO.

International audience; In the case of dynamic motion such as jumping, an important fact in sEMG (surface Electromyogram) signal based control on exoskeletons, myoelectric prostheses, and rehabilitation gait is that multichannel sEMG signals contain mass data and vary greatly with time, which makes it difficult to generate compliant gait. Inspired by the fact that muscle synergies leading to dimensionality reduction may simplify motor control and learning, this paper proposes a new approach to generate flexible gait based on muscle synergies extracted from sEMG signal. Two questions were discussed and solved, the first one concerning whether the same set of muscle synergies can explain the different phases of hopping movement with various velocities. The second one is about how to generate self-adapted gait with muscle synergies while alleviating model sensitivity to sEMG transient changes. From the experimental results, the proposed method shows good performance both in accuracy and in robustness for producing velocity-adapted vertical jumping gait. The method discussed in this paper provides a valuable reference for the sEMG-based control of bionic robot leg to generate human-like dynamic gait.

In the recent years, there has been a growing interest in wireless sensor networks (WSN). Network's lifetime depends on energy efficiency and load balancing where connectivity is a very important factor. However, such connectivity can be lost due to the failure of some sensor nodes which creates disruptions to the network operations, lead to a reconfiguration of the network by generating energy losses, or in another case, the network mission fails. Energy conservation is a very important problem in WSN. In this paper, we propose a new solution for the connectivity problem when failure nodes are considered. The replacement of failed nodes is done in two phases: the first one is the search of redundant nodes using the clusterheads; the second phase is a restoration of connectivity. Performance evaluation of the proposed replacement approach shows that the results are globally satisfactory.

International audience; Model-driven engineering (MDE) and search-based software engineering (SBSE) are both relevant approaches to software engineering. MDE aims to raise the level of abstraction in order to cope with the complexity of software systems, while SBSE involves the application of metaheuristic search techniques to complex software engineering problems, reformulating engineering tasks as optimization problems. The purpose of this paper is to survey the relatively recent research activity lying at the interface between these two fields, an area that has come to be known as search-based model-driven engineering. We begin with an introduction to MDE, the concepts of models, of metamodels and of model transformations. We also give a brief introduction to SBSE and metaheuristics. Then, we survey the current research work centered around the combination of search-based techniques and MDE. The literature survey is accompanied by the presentation of references for further details.

International audience; As an important movement of the daily living activities, sit-to-stand (STS) movement is usually a difficult task facing elderly and dependent people. To provide appropriate power assistance for the sit-to-stand movement, a novel intention-based Active Impedance Control (AIC) strategy applied on a lower limb exoskeleton is proposed in this paper. The AIC is able to adapt the mechanical impedance of the human-exoskeleton system towards a desired one using the exoskeleton's power assistance. In the AIC structure, a human joint torque observer is designed to estimate the human joint torques using joint angles information instead of electromyography (EMG) or force/torque sensors; a time-varying desired impedance model is proposed according the wearer's lower limb motion ability. Simulations were implemented to illustrate the characteristics and performances of the proposed approach. Experiments with a healthy subject were carried out to evaluate the effectiveness of the proposed method. The experiments show satisfactory results in terms of appropriate power assist based on the wearer's motion intention.

International audience; An important challenge to be addressed in the domain of Internet of Things (IoT) is the development of efficient services selection algorithms for an optimal management of both energy and Quality of Service (QoS) in the context of IoT services composition. This issue becomes crucial in the case of large-scale IoT environments composed of thousands of distributed entities. In this paper, an energy-centered and QoS-aware services selection algorithm (EQSA) is proposed for IoT services composition. The proposed selection approach consists of preselecting the services offering the QoS level required for user's satisfaction using a lexicographic optimization strategy and QoS constraints relaxation technique. In order to reduce the energy consumption of a composite service without affecting the user's satisfaction, the most suitable services among the preselected ones are then selected using the concept of relative dominance of services in the sense of Pareto. The relative dominance of a candidate service depends on its energy profile and QoS attributes, and user's preferences. The proposed algorithm has been evaluated through several simulation scenarios. The obtained results show clearly the good performances of the EQSA algorithm in terms of selection time, energy efficiency, composition lifetime, and optimality and its added value in comparison with algorithms dealing separately with QoS and energy consumption.

International audience; Neurologic injuries, such as stroke, spinal cord injuries, and weaknesses of skeletal muscles with elderly people, may considerably limit the ability of this population to achieve the main daily living activities. Recently, there has been an increasing interest in the development of wearable devices, the so-called exoskeletons, to assist elderly as well as patients with limb pathologies, for movement assistance and rehabilitation. In this paper, we review and discuss the state of the art of the lower limb exoskeletons that are mainly used for physical movement assistance and rehabilitation. An overview of the commonly used actuation systems is presented. According to different case studies, a classification and comparison between different types of actuators is conducted, such as hydraulic actuators, electrical motors, series elastic actuators, and artificial pneumatic muscles. Additionally, the mainly used control strategies in lower limb exoskeletons are classified and reviewed, based on three types of human-robot interfaces: the signals collected from the human body, the interaction forces between the exoskeleton and the wearer, and the signals collected from exoskeletons. Furthermore, the performances of several typical lower limb exoskeletons are discussed, and some assessment methods and performance criteria are reviewed. Finally, a discussion of the major advances that have been made, some research directions, and future challenges are presented.

International audience; Wearable robotic systems have been a mechanism which clearly drives the motive of bringing back paraplegics back on their feet as well as executing difficult task beyond human ability. The purpose of this research study is to design and investigate the efficacy of rehabilitative walking in patients with lower limb disorders using oscillators which may commonly be referred to as central pattern generators (CPGs). In order to achieve this, a rhythmic trajectory is designed using Van der Pol oscillators. This rhythmic trajectory commensurates with the movement pattern of the hips and knees for a normal walking gait of humans. The dynamical model of a five-link biped exoskeletal device having four actuated joints is computed with regard to the wearer using Lagrangian principles in the sagittal plane. A feedback linearization control technique is therefore utilized for tracking the rhythmic trajectory to achieve a proper following of the human walking gait. Matlab/Simulink is used to validate this proposed strategy in the presence of uncertainties with a view to implementing it practically in the laboratory with human in the loop. Results show that humans with the aid of the exoskeleton device will possess the ability to track this rhythmic trajectory representing the hip and knee joint movements. The controller proved robust enough against disturbance.

The present paper deals with the control of a knee joint orthosis intended to be used for rehabilitation and assistive purposes. A model, integrating human shank and orthosis, is presented. To reduce the influence of the uncertainties in muscular torque modeling on the system control, a nonlinear observer is proposed to estimate the muscular torque developed by the wearer. Additionally, a robust terminal sliding mode control approach combined with the nonlinear observer is presented. To illustrate the effectiveness of the proposed control method, a comparison with two control methods, basic sliding mode and sliding mode with nonlinear observer, are also given. The asymptotic stability of the presented approaches and observer convergence are proved by means of a Lyapunov analysis. Furthermore, the proof of advantage of the robust terminal sliding mode control method with the nonlinear observer (improving the tracking precision and reducing the required time for eliminating external disturbances) is proposed as well. The experiment results show that the robust terminal sliding mode control approach combined with the nonlinear observer has a significant advantage with respect to the position tracking and robustness regarding the modeling identification errors and external disturbances. This paper deals with the control of a knee joint exoskeleton for rehabilitation purposes.An observer is proposed to estimate the muscular torque developed by the wearer.A robust terminal sliding mode control method combined with the observer is presented.Asymptotic stability is proved by means of a Lyapunov analysis.Experiment tests were conducted with three subjects.

International audience; no abstract

International audience; This paper presents a control approach of a lower limb exoskeleton by modulating the original impedance of the wearer’ swinging leg to reduce the muscular efforts. The proposed method ensures compensation of the damping and gravity effects of the wearer’s leg to desired levels offering though the possibility to adapt the lower limb wearer’s impedance as a function of the gait phase evolution. The performance of the proposed approach is evaluated by carrying out experiments with two healthy subjects. The EMG activities of the extensor muscles spanning the knee-joint are used as assesment criteria. The results show that the muscular activities required to perform the same flexion/extension movements are effectively reduced by providing lower desired joint damping and stiffness with exoskeleton’s assistance.

The analysis and monitoring of the human daily living activities plays an important role for rehabilitation goals, fall prevention rehabilitation and general health-care treatments. Among these activities, walking is the most important daily motion. Studying the evolution of the gait cycle through the analysis of the human center of force is beneficial to predict any abnormal walking pattern. The analysis is based on the use of pressure-based mapping system that collects pressure and force measurement during the gait cycle. This paper deals mainly with the detection of the main characteristics of the gait phases. To this end, a segmentation of the center of force of the human body measure through the in-shoe pressure mapping system is performed to identify the gait phases. The proposed segmentation approach consists in modeling each segment by a regression model and using logistic functions to model the transitions between segments. This flexible modeling through the logistic functions has the advantage of detecting abrupt and smooth transitions between segments. This paper deals with the detection of the main characteristics of the gait phases.A segmentation of the human body COF is done using the in-shoe pressure mapping system.Identification of the gait phases is done using a regression model.The proposed method has the advantage of detecting abrupt and smooth transitions.The proposed method is implemented and verified by experiment tests.

This paper proposes a novel mechanism for orthotic devices, which has an active joint actuator with a biarticular elastic mechanism that connects two joints simultaneously. The biarticular elasticity in the proposed mechanism is adopted to utilize the energy transfer function of the biarticular muscles so that (1) the torque from the active joint actuator is efficiently transmitted, and (2) the forces required to propel the upper body forward are assisted in a more efficient and sophisticated way. In recent years, many researchers have discovered that the biarticular elasticity is a key mechanism of humans' efficient movement and implemented the biarticular principle into various types of robots. However, the application of the biarticular mechanism has been hindered by the coupling and redundancy which the mechanism induces. This paper provides theoretic fundamentals for analyzing the kinematics and dynamics of the biarticular elasticity to help engineers design assistive mechanisms and controllers using the biarticular elasticity. The effectiveness of the proposed methods are verified by simulation studies in this paper. This paper proposes a novel mechanism for orthotic devices.A biarticular elastic mechanism with an active joint actuator is utilized.The proposed mechanism transmits torques from the active joint actuator efficiently.Theoretic fundamentals for analyzing the proposed mechanism are provided.Virtual hook control is proposed to control the proposed mechanism.

International audience; Challenges for the next generation of Brain Computer Interfaces (BCI) are to mitigate the common sources of variability (electronic, electrical, biological) and to develop online and adaptive systems following the evolution of the subject's brain waves. Studying electroencephalographic (EEG) signals from their associated covariance matrices allows the construction of a representation which is invariant to extrinsic perturbations. As covariance matrices should be estimated, this paper first presents a thorough study of all estimators conducted on real EEG recording. Working in Euclidean space with covariance matrices is known to be error-prone, one might take advantage of algorithmic advances in Riemannian geometry and matrix manifold to implement methods for Symmetric Positive-Definite (SPD) matrices. Nonetheless, existing classification algorithms in Riemannian spaces are designed for offline analysis. We propose a novel algorithm for online and asynchronous processing of brain signals, borrowing principles from semi-unsupervised approaches and following a dynamic stopping scheme to provide a prediction as soon as possible. The assessment is conducted on real EEG recording: this is the first study on Steady-State Visually Evoked Potential (SSVEP) experimentations to exploit online classification based on Rie-mannian geometry. The proposed online algorithm is evaluated and compared with state-of-the-art SSVEP methods, which are based on Canonical Correlation Analysis (CCA). It is shown to improve both the classification accuracy and the information transfer rate in the online and asynchronous setup.

International audience; The Internet of Things is the natural continuity of the Ambient Intelligence where smart and ambient environments are built mainly by integrating a large number of interconnected smart objects (sensors, actuators, Smartphone, appliances, etc.) with heterogeneous capabilities abstracted as software services. These services can be composed on the fly and provided, all the time and everywhere, to assist users in their daily activities. A key issue in user-centered services composition is to intelligently and effectively discover and select the most relevant services that best match the users' requirements and closely meet the specified quality-of-service level. Monitoring seamlessly the provided services and enhancing their quality, is still a challenging issue due mainly to the dynamicity and uncertainty characterizing ambient environments. In this paper, we propose a new service-oriented, user-centered and event-aware Framework capable of performing services monitoring to handle automatically events that may occur in ambient environments. This monitoring is based on a dynamic services discovery and selection process to enhance self-adaptation to unpredicted changes, and ensure services continuity with best quality. The overall proposed Framework has been implemented and validated through a scenario dedicated to daily activity recognition in an Ambient-Assisted Living environment. In addition, the obtained performances from extensive tests show clearly the efficiency and feasibility of the proposed approach in the case of a large-scale environment.

The Ambient Intelligence (AmI) technologies have the potential to create intelligent environments with new generation of assistive services, enhanced with ubiquitous robots. These environments have the ability to be anticipatory, responsive and intelligent providers of assistive services anytime and anywhere. These services can assist frail persons effectively in their daily tasks. One of the main challenging research problems in assistive robotics is to endow ubiquitous robots with ability to pro-actively taking on some tasks to help humans in performing complex activities, by participating with them just as other humans do, in normal societies or organizations.In this paper, we propose a collective intelligence framework based on narrative reasoning and natural language processing. In the proposed approach, we propose a hybrid model that bridges together the Narrative Knowledge Representation Language (NKRL), from natural language processing field, and the HARMS (Humans, software Agents, Robots, Machines and Sensors) model, from multi-agent systems engineering field. This model is able to (i) drive the dialogues between humans, robots and smart devices, (ii) understand a complex situation, and (iii) trigger reactive actions, in the ubiquitous environment, according to given contexts.Two scenarios dedicated to the assistance of a frail person in a smart home equipped with a companion robot and smart objects are implemented and discussed for validation purposes of the proposed framework. Overview of the ubiquitous robotics and ambient intelligence.Hybrid model that bridges together the NKRL and the HARMS model.Context Aware modeling based on the upper ontologies of NKRL.Semantic Reasoning based Collective Intelligence in ubiquitous robotics.

International audience; Action languages have emerged as an important field of knowledge representation for reasoning about change and causality in dynamic domains. This paper presents Cerbere, a production system designed to perform online causal, temporal and epistemic reasoning based on the Event Calculus. The framework implements the declarative semantics of the underlying logic theories in a forward-chaining rule-based reasoning system, coupling the high expressiveness of its formalisms with the efficiency of rule-based systems. To illustrate its applicability, we present both the modeling of benchmark problems in the field, as well as its utilization in the challenging domain of smart spaces. A hybrid framework that combines logic-based with probabilistic reasoning has been developed, that aims to accommodate activity recognition and monitoring tasks in smart spaces.

In this paper, autonomous control problem of a class of bionic continuum robots named “Compact Bionic Handling Arm” (CBHA) is addressed. These robots can reproduce biological behaviors of trunks, tentacles, or snakes. The modeling problem associated with continuum robots includes nonlinearities, structured and unstructured uncertainties, and the hyperredundancy. In addition to these problems, the CBHA comprises the hysteresis behavior of its actuators and a memory phenomenon related to its structure made of polyamide materials. These undesirable effects make it difficult to design a control system based on quantitative models of the CBHA. Thus, two subcontrollers are proposed in this paper. One, encapsulated in the other, and both implemented in real time allow controlling of the CBHA's end-effector position. The first subcontroller controls the CBHA's kinematics based on a distal supervised learning scheme. The second subcontroller controls the CBHA's kinetics based on an adaptive neural control. These subcontrollers allow a better assessment of the stability of the control architecture while ensuring the convergence of Cartesian errors. The obtained experimental results using a CBHA robot show an accurate tracking of the CBHA's end-effector position.

International audience; In recent years, much attention has been directed to building robots with hybrid and multi-modal locomotion in air, water and on the ground. This rise in interest is due to the enormous requirements of multi-domain earthquake rescue, pollution monitoring, natural species discovery and other applications in which multi-modal locomotion can offer unprecedented advantages to robot mobility. Swimming, crawling, rolling, walking, running, jumping and flying are quite common in the animal kingdom, and such locomotion occurs in very different physical environments. Interestingly, an animal may perform two or more modes of locomotion. For instance, turtles and salamanders can swim underwater and walk on land, while swans can swim and fly effectively. Their often remarkable abilities could inspire innovative designs to improve the way man-made systems operate in and interact with challenging outdoor environments consisting of multiple media. Modes of locomotion in nature can be decomposed broadly into three categories: terrestrial, aerial and aquatic. There are situations in which the distinction between categories is vague, such as movement on the surface of water or in underground tunneling. However, generally, the types of locomotion used by animals can be categorized into one of these three areas. The primary venue of locomotion for many animals tends to be mono-modal; the majority of their task-space demands only a singular modality, with minimal need of alternative morphologies for transportation. The fundamental reason for multi-modal animal locomotion is for survival. The need for these multiple modes can arise from different requirements related to survival, including fast escape, fast pursuit, searching for food, breeding, nesting, conserving energy and migration. The paper presents a brief survey on the recent application works of hybrid and multi-modal locomotion.

International audience; Fault diagnosis is a critical task for operators in the context of e-TOM (enhanced Telecom Operations Map) assurance process. Its purpose is to reduce network maintenance costs and to improve availability, reliability and performance of network services. Although necessary, this operation is complex and requires significant involvement of human expertise. The study of the fundamental properties of fault diagnosis shows that the diagnosis process complexity needs to be addressed using more intelligent and efficient approaches. In this paper, we present a hybrid approach that combines Bayesian networks and case-based reasoning in order to overcome the usual limits of fault diagnosis techniques and to reduce human intervention in this process. The proposed mechanism allows the identification of the root cause with a finer precision and a higher reliability. At the same time, it helps to reduce computation time while taking into account the network dynamicity. Furthermore, a study case is presented to show the feasibility and performance of the proposed approach based on a real-world use case: a virtual private network topology.

International audience; Recognition of the wearer's motion intention plays an important role in the study of power-assist robots. In this paper, an intention-guided control strategy is proposed and applied to an upper-limb power-assist exoskeleton. Meanwhile, a human-robot interface comprised of force-sensing resistors (FSRs) is designed to estimate the motion intention of the wearer's upper limb in real time. Moreover, a new concept called the “intentional reaching direction (IRD)” is proposed to quantitatively describe this intention. Both the state model and the observation model of IRD are obtained by studying the upper limb behavior modes and analyzing the relationship between the measured force signals and the motion intention. Based on these two models, the IRD can be inferred online using an adapted filtering technique. Guided by the inferred IRD, an admittance control strategy is deployed to control the motions of three DC motors placed at the corresponding joints of the robotic arm. The effectiveness of the proposed approaches is finally confirmed by experiments on a 3 degree-of-freedom (DOF) upper-limb robotic exoskeleton.

International audience; The Web of Objects is a new emerging concept that represents a new way of organizing physical or immaterial objects—or things—delivering a high level of services that can be discovered, composed, executed, and monitored. Building an ambient assisted living (AAL) ecosystem, according to this vision, is of great interest since it consists of multiple heterogeneous objects that need to be semantically and automatically interoperable. In this chapter, a new semantic framework for monitoring the AAL heterogeneous ecosystems is presented. This framework deals with context awareness and closed-world assumption (CWA) semantic reasoning in designing management rules and ontologies. It preserves, on one hand, the structural expressiveness provided by ontologies, through the compatibility with the W3C recommendations and, on the other hand, it takes advantage of the full benefit of the production rule reasoning. It is capable of dynamically detecting real-world objects and capturing their contexts, in order to identify the specific changes that are happening in the environment, to infer the current situation and to react accordingly. Context management is designed by combining the micro-concept ontology language into a set rules and queries using the SEMbySEM business rule language. These rules allow us to express high-level reasoning over context events, sent from the façade, while the queries allow us to generate high-level monitoring views, concerning manageable objects status and context, as well as their measures and produced activities. Conversely, the monitoring rules can trigger control actions that are translated, by the façade, into concrete operations on the targeted manageable object. In order to validate the proposed framework, a scenario dedicated to the safety of elderly people in smart homes is proposed and implemented hereby.

In this paper, an omni-directional walking-aid robot is developed to assist the elderly in the daily living movements. A motion control strategy of walking-aid robot based on the observation of the human status through wearable sensors is proposed. During normal walking, the robot is controlled using a conventional admittance control scheme. When the tendency of a fall is detected, the robot will immediately react to prevent the user from falling down. The distance between the human Center of Pressure (COP) and the midpoint of the human feet is assumed to be a significant feature to detecting the fall events. When the user is in a quasi-static state or walking slowly, the COP can be approximated by the projection of Center of Gravity (COG) of the user's body. A simple and low-cost wearable sensor system is proposed to measure online the COG of the user. A limitation of the proposed wearable sensor system is that the Head-Arms-Torso (HAT) of the user is assumed to be always in upright position, which may generate measurement error. From comparison experiments with a reference optical system it is found that the measurement error is acceptable especially at the early stage of fall event. Dubois possibility theory is applied to describe the membership function of "normal walking" state. A threshold based fall detection approach is obtained from online evaluation of the walking status. Finally, experiments demonstrate the validity of the proposed strategy. An omni-directional walking-aid robot is developed to assist and support the elderly.A wearable sensor system was designed to estimate online the human posture.A fall detection method by wearable sensor is obtained based on possibility theory.Normally the robot uses admittance control while it is braked when fall detected.Experiments were conducted to test the wearable sensor and walking-aid robot.

International audience; The IEEE Robotics and Automation Society (RAS) Technical Committee (TC) on Biorobotics was formed with the goal of providing a forum and dissemination mechanism for the interaction between biological and artificial (autonomous or semi-autonomous) systems and to present biology as a learning tool for novel engineering paradigms. In 2007, in an effort to define the scope of the technical field of the TC, the TC on Biorobotics was created to focus on various research fields that involve the understanding and implementation of complex living organisms by virtue of mechatronic systems. One such field is biologically inspired robotics, which is characterized by a multidisciplinary approach that aims to strengthen the collaboration between roboticists and biologists. To this end, two main approaches are adopted for research in Biorobotics: 1) the application of biological concepts/methodologies to improve the current capabilities of robots, for example, extending the robot’s flexibility and robustness by adopting design principles of biological systems and 2) the application of advanced robotic technology to improve the current techniques/methodologies adopted by biologists. The principles and techniques of hybrid and multimodal locomotion recently have been emphasized and explored by researchers for better performance of bioinspired movements. This paper highlights the recent activities conducted by the TC on Biorobotics.

International audience; The aim of the present work is to estimate the vertical forces of heavy vehicle and identify the unknown dynamic parameters using sliding mode observer approach. This observation needs a good knowledge of dynamic parameters such as damping coefficient, spring stiffness, etc. In this paper, suspension stiffness and unsprung masses have been identified. Experimental results carried out on an instrumented tractor have been presented in order to show the quality of the state observation, parameters identification and force estimation. These estimation results are then compared to the measured one coming from the sensors installed in the tractor. Many scenarios have been tested. In this paper, the results coming from zigzag test have been shown and commented.

International audience; In this study, a new active impedance control method of a knee joint orthosis is proposed by using a nonlinear observer to estimate the human joint torque. The proposed method is an alternative to the sensor-based impedance controller in which the human joint torque is estimated by using the electromyography (EMG) sensor, force/torque sensors, etc. The use of the nonlinear observer can efficiently overcome the shortcomings of the EMG and force/torque sensors, such as complexity of use, encumbrance, modeling, sensitivity to noise, high cost, etc. Additionally, a main goal of the proposed active impedance control is to decrease the impedance of the human-orthosis system to a desired level that the wearer can easily achieve. To achieve this goal, a virtual desired impedance model is designed to estimate human intention movements; a nonlinear disturbance observer based sliding mode controller is proposed to track the human estimated movements. Two experiments were carried out to verify the performance of the proposed nonlinear observer and sliding mode controller, as well as the effectiveness of the active impedance assistance strategy. The results show that the proposed method can effectively decrease the impedance of the human-orthosis system according to the desired impedance model. At the same time, the required human muscle torque for ensuring the knee joint flexion/extension movement can be also significantly decreased with the active impedance assistance.

Graphical abstractDisplay Omitted HighlightsHuman safety is ensured for the wearer of exoskeleton.Adaptive controller without prior knowledge.Stability proved theoretically using Lyapunov approach.No chattering phenomena in the input control.External disturbances are rejected and the wearer efforts are taken into account. This paper deals with the rehabilitation purposes using an active orthosis driven by an adaptive neural controller based on a radial basis function neural network (RBFNN). Two essential conditions are required in our study: ensuring the wearer safety and the good trajectory tracking. We consider for our experiments the same movements often recommended by the doctor during therapy sessions. In this context, it is possible to add some trivial prior knowledge as the dynamic model structure and all dynamical identified parts. The unknown or the uncertainty part of the inertia term of the knee-shank-orthosis system is identified online using an adaptive term. All other uncertainties or unknown dynamics are identified online by the RBFNN. The Lyapunov approach has been used to derive adaptation laws of the neural parameters and the inertia term. These adaptation laws ensure the stability of the system composed of the exoskeleton and its wearer. The wearer can be completely inactive or applying either a resistive or an assistive effort. Experimental results have been conducted on a real exoskeleton that is used for rehabilitation reasons. Based on these results we conclude with the effectiveness of the proposed approach.

International audience; The present chapter deals with the development of two rehabilitation approaches of the lower limb by means of the knee joint exoskeleton EICOSI. The model of the exoskeleton is presented as well as a modified Hill-type muscular model of the wearer’s lower limb. The parameters of the shank-foot-exoskeleton and the muscles are identified based on a least square optimization algorithm. Two control techniques are proposed: the first one ensures the passive rehabilitation of the lower limb by means of bounded control laws and the second ensures the assistance-as-need of the wearer based on the human intention estimated by means of EMG electrodes fixed at some particular muscles. Experimental tests are performed on healthy subjects and show the efficiency of the proposed strategies.

International audience; In this paper, a novel trajectory planning approach is proposed for redundant manipulators in the case of several obstacles. The trajectory is discretized and at each step, we search for a new position of the end effector in the Cartesian space to reach the final position. Because of the redundancy, this position can be achieved by an infinity of configurations in the joint space. Thus, we use this property to find the best configuration that allows to avoid obstacles and singularities of the robot. The proposed method is based on a bilevel optimization formulation of the problem and bi-genetic algorithm to solve it. In order to avoid obstacles, we also proposed to manage constraints of the problem dynamically. This technique adapts the number of constraints in the formulation of the problem with the position of the obstacles. Simulation results showed the effectiveness of the proposed method.

International audience; In this chapter we discuss the necessity to move beyond built-in monotonic semantic web based reasoning-architectures for endowing ubiquitous robots with cognitive capabilities, which are strongly required in ambient assistive living, towards new architectures that combine different reasoning mechanisms to achieve better context awareness and adaptability in dynamic environments. We also present practical reasoning approaches that we have developed during the last decade for ambient intelligence and robotics applications. Finally, we discuss future directions that should be investigated to implement high-level cognitive capabilities that can be supported by cloud computing platforms as reasoning backend for robots and connected devices in smart spaces. These will enhance the human-environment interaction using robots, emergency prevention, management and rescue.