Control and state estimation procedures need to be robust against imprecisely known parameters, uncertainty in initial conditions, and external disturbances. Interval methods and other set-based techniques form the basis for the implementation of powerful approaches that can be used to identify parameters of dynamic system models in the presence of the aforementioned types of uncertainty. Moreover, they are applicable to a verified feasibility and stability analysis of controllers and state estimators. In addition to these approaches which are typically used offline for analysis of system models designed with classical floating point procedures, interval and set-based methods have also been developed in recent years, which allow to directly solve the associated design tasks and to implement reliable techniques that are applicable online, i.e., during system operation. The latter approaches include set-based model predictive control, online parameter adaptation techniques for nonlinear variable-structure and backstepping controllers, interval observers, and fault diagnosis techniques. This paper provides an overview of the methodological background and reviews numerous practical applications for which interval and other set-valued approaches have been employed successfully.

Proving that the state of a controlled nonlinear system always stays inside a time moving bubble (or capture tube) amounts to proving the inconsistency of a set of nonlinear inequalities in the time-state space. In practice however, even with a good intuition, it is difficult for a human to find such a capture tube except for simple examples. In 2014, Jaulin et al. established properties that support a new interval approach for validating a quasi capture tube, i.e. a candidate tube (with a simple form) from which the mobile system can escape, but into which it enters again before a given time. A quasi capture tube is easy to find in practice for a controlled system. Merging the trajectories originated from the candidate tube yields the smallest capture tube enclosing it. This paper proposes an interval constraint programming solver dedicated to the quasi capture tube validation. The problem is viewed as a differential CSP where the functional variables correspond to the state variables of the system and the constraints define system trajectories that escape from the candidate tube "for ever". The solver performs a branch and contract procedure for computing the trajectories that escape from the candidate tube. If no solution is found, the quasi capture tube is validated and, as a side effect, a corrected smallest capture tube enclosing the quasi one is computed. The approach is experimentally validated on several examples having 2 to 5 degrees of freedom.

This paper proposes a new concept of thick gradual sets (TGSs), which is based on the notions of thick sets (TSs) and gradual sets (GSs). A TS is an uncertain set, which is represented by a pair of crisp sets (CSs). These CSs represent the upper and lower bounds of the TS. Therefore, a TS can be considered as an interval of CSs. A GS is a CS, which is parameterized by a degree of pertinence and aims to increase the specificity of CSs. Furthermore, a TGS is an interval of GSs, i.e., a pair of lower and upper GSs. In situations when the constraint of monotonicity (consistency) is guaranteed, a GS becomes a type-1 fuzzy set (T1FS) and a TGS can be regarded as a thick fuzzy set (TFS). Moreover, a TFS, which is composed of lower and upper T1FS bounds, can be interpreted as a type-2 fuzzy set (T2FS). According to the TGS representation, this new approach offers an original concept for interpreting, manipulating, and computing some uncertain quantities that cannot be represented by GSs, T1FSs, and/or T2FSs. The potential applications of the TGS concept has been validated using application examples in the frameworks of solving fuzzy systems of equations and uncertain fuzzy regression and through a real-world application where the trajectory of an underwater robot is uncertain and cannot be precisely known because of disturbances induced by the environment. The proposed approach makes it possible to compute the uncertain zone explored by the underwater robot.

For a mobile robot to operate in its environment it is crucial to determine its position with respect to an external reference frame using noisy sensor readings. A scenario in which the robot is moved to another position during its operation without being told, known as the kidnapped robot problem, complicates global localisation. In addition to that, sensor malfunction and external influences of the environment can cause unexpected errors, called outliers, that negatively affect the localisation process. This paper proposes a method based on the fusion of a particle filter with bounded-error localisation, which is able to deal with outliers in the measurement data. The application of our algorithm to solve the kidnapped robot problem using simulated data shows an improvement over conventional probabilistic filtering methods.

This article aims at proposing the concept of thick fuzzy sets (TFSs). A TFS is based on the joint use of thick sets (TSs) and α-cuts concepts. A TFS is represented by a family of nested TSs. A TS is an uncertain set, which is represented by a pair of crisp sets (CSs). These CSs characterize the upper and lower bounds of the TS. Therefore, a TS can be regarded as an interval of CSs. In this framework, as a type-1 fuzzy set (T1FS) is regarded as a family of nested CSs, a TFS can be represented by a family of nested TSs. Furthermore, according to the vertical dimension α, a TFS can be regarded as an interval with T1FS bounds. The potentialities of the TFS concept have been validated using application examples where a real-world application for modeling the zone explored by an underwater robot is given.

This paper deals with the localization problem of a robot in an environment made of indistinguishable landmarks, and assuming the initial position of the vehicle is unknown. This scenario is typically encountered in underwater applications for which landmarks such as rocks all look alike. Furthermore, the position of the robot may be lost during a diving phase, which obliges us to consider unknown initial position. We propose a deterministic approach to solve simultaneously the problems of data association and state estimation, without combinatorial explosion. The efficiency of the method is shown on an actual experiment involving an underwater robot and sonar data.

In this paper, we propose a set-inversion approach to validate the controller of a nonlinear system that should satisfy some state constraints. We introduce the notion of follow set which corresponds to the set of all output vectors such that the desired dynamics can be followed without violating the state-constraints. This follow set can then be used to choose feasible trajectories that a mobile robot will be able to follow. An illustrative example with a robot towing a trailer is presented. This example is motivated by the safe control of a boat towing a marine magnetic sensor to find wrecks.

This paper addresses the problem of platooning control for a fleet of heterogeneous sailboats. The platooning maintains a constant time headway (CTH) between sailboats following a circular path, a complex problem for sailboats due to the influence of wind direction. First, the desired acceleration based on the CTH and the sailboat velocity needed to converge to the platooning is defined. Second, a control of sailboat orientation to manage the sailboat acceleration is proposed. The proposed platooning strategy adapts to the specific characteristics of sailboats, which are different from other motorized marine vehicles. Two tack strategies can be used for the method: the first is to regulate the sailboat velocity; the second is to go front of the wind while staying in a short corridor. The desired acceleration for fulfilling the platooning has been derived and validated. The simulation results demonstrate the effectiveness of the proposed approach in comparison with an optimal receding horizon control algorithm.

This article depicts an algorithm which matches the output of a Lidar with an initial terrain model to estimate the absolute pose of a robot. Initial models do not perfectly fit the reality and the acquired data set can contain an unknown, and potentially large, proportion of outliers. We present an interval based algorithm that copes with such conditions, by matching the Lidar data with the terrain model in a robust manner. Experimental validations using different terrain model are reported to illustrate the performance of the method.

Probabilistic approaches are extensively used to solve high-dimensionality problems in many different fields. The particle filter is a prominent approach in the field of Robotics, due to its adaptability to non-linear models with multi-modal distributions. Nonetheless, its result is strongly dependent on the quality and the number of samples required to cover the space of possible solutions. In contrast, interval analysis deals with high-dimensionality problems by reducing the space enclosing the actual solution. Notwithstanding, it cannot precise where in the resulting subspace the actual solution is. We devised a strategy that combines the best of both worlds. Our approach is illustrated by solving the global localization problem for underwater robots.

This paper proposes an interval-based method for the validation of reliable and robust navigation rules for mobile robots. The main idea is to show that for all feasible perturbations, there exists a safe subset of the state space such that the system cannot escape. The methodology is illustrated on the line-following problem of a sailboat and then validated on an actual experiment where an actual sailboat robot, which is named Vaimos, sails autonomously from Brest to Douarnenez (i.e., more than 100 km).

Measurements are never absolutely accurate, the measurement result x̃ is, in general, different from the actual (unknown) values x of the corresponding quantity. In many practical problems, we only know upper bounds Δ on the measurement errors equation. In such situations, once we know the measurement result, the only conclusion that we can make about the actual value x is that this value belongs to the interval [x̃ - Δ, x̃ + Δ]. There exist many efficient algorithms for processing such interval data. However, these algorithms usually assume that all the measurement results are valid. In reality, due to factors such as sensor malfunction, some measurement results may be way off (outliers), for which the difference between x̃ and x is much larger than the upper bound Δ on the measurement error. In this paper, we overview the algorithmic problems related to processing interval sensor data in the presence of outliers. Our case study - for which we develop and analyze these algorithms - is localization of underwater robots, a problem in which a significant number of measurement results are outliers.

This paper proposes a new set-membership approach to solve range-only simultaneous localization and mapping (SLAM) problems in the case where the map is described by an arbitrary occupancy set (i.e., we do not assume that the map is composed of segments, punctual marks, etc.). The principle is to transform the SLAM problem into a hybrid constraint satisfaction problem (CSP), where the variables can either be real numbers, vectors, trajectories, or subsets of \BBRn. An extension of existing constraint propagation methods is then proposed to solve hybrid CSPs involving set-valued variables. A simulated test case is then proposed to show the feasibility of the approach.

Uncertainties are commonly present in optimization systems, and when they are considered in the design stage, the problem usually is called a robust optimization problem. Robust optimization problems can be treated as noisy optimization problems, as worst case minimization problems, or by considering the mean and standard deviation values of the objective and constraint functions. The worst case scenario is preferred when the effects of the uncertainties on the nominal solution are critical to the application under consideration. Based on this worst case scenario, we developed the [I]RMOEA (Interval Robust Multi-Objective Evolutionary Algorithm), a hybrid method that combines interval analysis techniques to deal with the uncertainties in a deterministic way and a multi-objective evolutionary algorithm. We introduce [I]RMOEA and illustrate it on three robust test functions based on the ZDT problems. The results show that [I]RMOEA is an adequate way of tackling robust optimization problems with evolutionary techniques taking advantage of the interval analysis framework.

This paper proposes a set membership method based on interval analysis to solve the simultaneous localization and map building (SLAM) problem. The principle of the approach is to cast the SLAM problem into a constraint satisfaction problem for which interval propagation algorithms are particularly powerful. The resulting propagation method is illustrated on the localization and map building of an actual underwater robot.

We present a new approach for computing with fuzzy sets based on interval analysis techniques. Our proposed method is capable of managing multi-dimensional continuous membership functions of arbitrary form, such as, piecewise affine functions or non-linear expressions without any restrictions regarding convexity. We present a formal representation of fuzzy sets that allows to easily cast fuzzy problems into the set inversion framework. The SIVIA algorithm is presented as a convenient solution to solve this problem via interval analysis. It characterizes the resulting fuzzy set in an approximate (with the desired precision) but guaranteed way. Different combination operators were implemented using our method. We show that each operator is implemented following the same procedure and thus that, potentially, any fuzzy problem could be represented as a set inversion problem. Our approach is illustrated by examples at the end of the paper, where a discussion over the obtained results takes place.

This paper introduces a new formalism to deal with constrained dynamic systems. It shows that, when the constraints are linear, the controller can take advantage of linear programming algorithms (such as the simplex) to guarantee that the constraints will be satisfied. To illustrate the contribution of the approach, the control problem of a wheeled stair-climbing robot is considered. Masses supported by the robot have to be moved in order to avoid any sliding of the wheels. For such control problems where strong nonlinearities occur, conventional control methods fail to provide any reliable controller.

This paper deals with feedback controller synthesis for timed event graphs (TEG) in dioids where the number of initial tokens and time delays are only known to belong to intervals. The synthesis presented here is mainly based on dioid, interval analysis and residuation theory. A dioid of intervals is introduced and used to establish a linear model of uncertain TEG. It is shown how to apply the residuation theory over this new algebraic structure. Then, a robust feedback controller synthesis is proposed for these systems.

In this paper, initial localization problems are solved by using set-membership estimation. The method can be used with any robot and any kind of sensor(s), provided that a computable model of the environment/sensor interaction is available. With a pedagogical aim in mind, it is detailed in the case of the localization of a vehicle from range measurements in a polygonal environment. Salient properties of the method are as follows. First, it does not need any explicit management of matching hypotheses. Second, it is able to deal with ambiguous situations where several radically different vehicle configurations are consistent with the measurements. Third, it can be made robust to outliers. Fourth, it can deal with nonlinear observation models without any approximation. Fifth, the result is guaranteed in the sense that no configuration consistent with the data and the hypotheses can be missed.

When reliable prior bounds on the acceptable errors between the data and corresponding model outputs are available, bounded-error estimation techniques make it possible to characterize the set of all acceptable parameter vectors in a guaranteed way, even when the model is nonlinear and the number of data points small. However, when the data may contain outliers, i.e., data points for which these bounds should be violated, this set may turn out to be empty, or at least unrealistically small. The outlier minimal number estimator (OMNE) has been designed to deal with such a situation, by minimizing the number of data points considered as outliers. OMNE has been shown in previous papers to be remarkably robust, even to a majority of outliers. Up to now, it was implemented by random scanning, so its results could not be guaranteed. In this paper, a new algorithm based on set inversion via interval analysis provides a guaranteed OMNE, which is applied to the initial localization of an actual robot in a partially known two-dimensional (2-D) environment. The difficult problems of associating range data to landmarks of the environment and of detecting potential outliers are solved as byproducts of the procedure.