This paper presents synthesis methods of Interval Observers (IO) for discrete-time linear switched systems subject to additive unknown-but-bounded process and measurement noises. The novelty of the proposed methods consists in the designing of IO directly in the original state coordinates of the systems. This enables to: (i) mitigating the wrapping effect related to the classical use of similarity transformations; (ii) avoiding the impulsive behavior of the estimation error dynamics, mostly generated by the use of different similarity transformation for each mode of the switched system; (iii) reducing online computational effort. In addition, Bilinear Matrix Inequalities (BMI) and Linear Matrix Inequalities (LMI) conditions are established to check the existence and to compute stabilizing observer gains. The obtained theoretical results are supported by numerical simulations.

In a bounded error context, a secure set-valued state estimator for a class of systems described by a linear discrete-time difference inclusion is introduced in this contribution. The proposed design approach is based on set-valued computation combined with elimination by consistency techniques. More formally, we will show that a fusion between data provided by a set-valued predictor and those generated by a set-valued estimator allows one: (i) To obtain guaranteed state enclosures in the presence of additive and bounded state disturbance and measurement noise; (ii) To be able to detect faulty behaviors of the system and (iii) To be insensitive to a certain class of cyber-attacks. A numerical example is introduced to illustrate the performance of the proposed secure set-valued state estimator.

Hospitals are rightfully considered a field of indoor logistic robotics of high commercial potential. However, today, only a handful of mobile robotic solutions for hospital logistics exist that have failed to trigger widespread acceptance by the market. This is because existing systems require costly infrastructure installation, they do not easily integrate to corporate IT solutions, are not adequately shielded from cybersecurity threats, and as a result, they do not fully automate procedures and traceability of the items they carry. Moreover, existing systems are limited on scope, focusing only on delivery services, and hence do not provide any other type of support to the medical and nursing staff. ENDORSE system will address the aforementioned technical challenges and functional limitations by pursuing four innovation pillars: (i) infrastructure-less multi-robot indoor navigation; (ii) advanced Human-Robot Interaction (HRI) for resolving deadlocks and achieving efficient sharing of space resources in crowded environments; (iii) deployment of the ENDORSE software as a cloud-based service facilitating its integration with corporate software solutions, complying with GDPR data security requirements; (iv) reconfigurable and modular hardware architectures so that diverse modules can be easily swapped. ENDORSE functionality will be demonstrated via the integration of an e-diagnostic support module for vital signs monitoring on a fleet of mobile robots, facilitating connectivity to cloud-based Electronic Health Records (EHR), and validated in an operational hospital environment for realistic assessment.

Indoor localization has gained an increase in interest recently because of the wide range of services it may provide by using data from the Internet of Things. Notwithstanding the large variety of techniques available, indoor localization methods usually show insufficient accuracy and robustness performance because of the noisy nature of the raw data used. In this paper, we investigate ways to work explicitly with range of data, i.e., interval data, instead of point data in the localization algorithms, thus providing a set-theoretic method that needs no probabilistic assumption. We will review state-of-the-art infrastructure-based localization methods that work with interval data. Then, we will show how to extend the existing infrastructure-less localization techniques to allow explicit computation with interval data. The preliminary evaluation of our new method shows that it provides smoother and more consistent localization estimates than state-of-the-art methods.

Home automation is now implemented in many retirement homes in order to improve elderly's autonomy and safety. Smart homes allow to monitor the activities of elderly persons using information coming from different sensors. The ADL (Activities of Daily Living) are used to evaluate the ability of a person to perform on their own a selection of the activities which are essential for independent living in everyday life. The ADL are then used to detect deviations in a person's behaviour. Indoor localization based on the fusion of heterogeneous data from different sensors, is then essential for ADL characterization. For this purpose, a robust data fusion method is presented in this work through a multi-modal analysis to monitor the activities of elderly people (immobility, walking, etc) in a smart home. The paper describes the installation of sensors in a Living Lab and the preliminary experimental results using a set of Pyroelectric Infra Red (PIR) sensors, Radio Frequency Identification (RFID) distance measurement and the outcome of a noise analysis. Within a set-membership framework, our algorithm for robust localization employs a multi-modal data fusion approach dealing with faulty measurements.

In this study, we endeavor to better understand the human motor control system in order to help transposing some of its features onto humanoid robots. The postural coordination task investigated is related to an experimental paradigm that consists in visual target tracking task while balancing. We want to test whether the human biomechanical responses, namely phase / antiphase coordination mode transition, as exhibited during the actual experiments can be modeled by a linearized double inverted pendulum and parallel independent PD feedback control loops. Remarkably, these loops implement joint space control using cartesian task space variables. Furthermore, we want to see how the feedback control gains given by an optimization procedure scale w.r.t frequency or target motion magnitude. A closed-loop synthesis is developed that consists in minimizing a minimum torque criterion under both balance and task constraints. We show that the optimal feedback control gains obtained yield model responses consistent with the literature. In a second part, we implement a gain-scheduling approach where control gains values are predicted via interpolation. Finally, our approach implements a controller capable of achieving the task even when the frequency of the target motion varies over time.

This paper introduces effective numerical methods for the planning and fast replanning of safe motions to ensure the safety, balance, and integrity of humanoid robots over the whole motion duration. Our safe methods do not depend on, nor are connected to, any type of modeling or constraints. To plan safe motions, certain constraints have to be satisfied over a continuous interval of time. Classical methods revert to time-grid discretization, which can be risky for the robot. We introduce a hybrid method to plan safe motions, which combines a classical unsafe method with a verification step that checks constraint violation and computes excess by the usage of interval analysis. When the robot meets unexpected situations, it has to replan a new motion, which is often too time consuming. Hence, we introduce a new method to rapidly replan safe motions, i.e., in less than 2 s CPU time. It computes offline feasible subsets in the vicinity of safe motions and finds online a solution in these subsets without actually recomputing the nonlinear constraints. Our methods are validated by the use the HOAP-3 robot, where the motions are run with no balance controller.

Motion databasing is an important topic in robotics research. Humanoid robots have a large number of degrees of freedom and their motions have to satisfy a set of constraints (balance, maximal joint torque velocity and angle values). Thus motion planning cannot efficiently be done on-line. The computation of optimal motions is performed off-line to create databases that transform the problem of large computation time into a problem of large memory space. Motion planning can be seen as a Semi-Infinite Programming problem (SIP) since it involves a finite number of variables over an infinite set of constraints. Most methods solve the SIP problem by transforming it into a finite programming one using a discretization over a prescribed grid. We show that this approach is risky because it can lead to motions which may violate one or several constraints. Then we introduce our new method for planning safe motions. It uses Interval Analysis techniques in order to achieve a safe discretization of the constraints. We show how to implement this method and use it with state-of-the-art constrained optimization packages. Then, we illustrate its capabilities for planning safe motions dedicated to the HOAP-3 humanoid robot.

This paper models recent data in the field of postural coordination showing the existence of self-organized postural states, and transition between them, underlying suprapostural tracking movements. The proposed closed-loop controller captures the complex postural behaviors observed in humans and can be used to implement efficient and simple balance control principles in humanoids.

Optimal motions are usually used as joint reference trajectories for repetitive or complex motions. In the case of soccer robots, the kicking motion is usually a benchmark motion, computed off-line, without taking into account the current position of the robot or the direction of the goal. Moreover, robots must react quickly to any situation, even if not expected, and cannot spend time to generate a new optimal motion by the classical way. Therefore, we propose a new method for fast motion re-planning based on an off-line computation of a feasible sub-set of the motion parameters, using Interval Analysis.

This paper reports the first results of an ongoing work which aims at providing numerical tools useful to the design of a family of high speed parallel robots. The objective is to find sets of feasible values for the design parameters unlike more usual design procedures relying on optimization techniques. These tools are mainly based on interval analysis and take into account the dynamics of the parallel robots. Moreover, they can deal with bounded uncertainties that affect some physical parameters involved in the dynamics. Following the statement of the design problems considered, a general method is presented. Then, this method is illustrated with the study of a 2-DOF parallel robot.

This paper addresses the problem of restoring standing in paralegia via functional electrical stimulation (FES) and investigates the relashionship between body posture and voluntary upper-body movements. A methodology is presented for upper-body posture estimation in the sagittal plane from force and torque measurements exerted on handles during human standing, in the hypothesis of quasi-static equilibrium. The method consists in setting up constraints related to the geometric equations and the hand-handle interaction. All measured quantities are subject to an uncertainty assumed unknown but bounded. The set membership estimation problem is solved via interval analysis. Guaranteed uncertainty bounds are computed for the estimated postures. The methodology is validated experimentally with spinal cord injured patients with lesions between T5 and T12. Possible applications of the developed methodology are lower limbs function rehabilitation within clinical centers, walk assistance and independent mobility for spinal cord injured patients.

This paper presents the application of an ellipsoidal method for robust dynamic identification of parallel robots. The robot is modelled with classical Lagrange equation which leads to an inverse dynamic model linear with respect to the parameters. Assuming the error additive on input (motor torque), the problem is expressed in a bounded error context. The ellipsoidal method is applied in a factorised form in order to guarantee numerical stability. Experimental results are exhibited for a fully parallel robot with 4 degrees of freedom.

This paper shows how interval analysis can be used to get guaranteed 3D measurements with a stereovision sensor. We propose to describe coordinates of image points by intervals. Thus, we propose a new method based on interval analysis tools to propagate this kind of unknown but bounded uncertainty. Finally, we apply this method to compute a guaranteed model for a projective camera and a guaranteed 3D reconstruction.