The highly percipient nature of human mind in avoiding sensory overload is a crucial factor which gives human vision an advantage over machine vision, the latter has otherwise powerful computational resources at its disposal given today's technology. This stresses the need to focus on methods which extract a concise representation of the environment inorder to approach a complex problem such as visual mapping. This article is an attempt of creating a mapping system, which proposes an architecture that combines task-specific and saliency driven approaches. The proposed method is implemented on a warehouse robot. The proposed solution provide a priority framework which enables an industrial robot to build a concise visual representation of the environment. The method is evaluated on data collected by a RGBD sensor mounted on a fork-lift robot and shows promise for addressing visual mapping problems in industrial environments.

The use of the velocity potential of an incompressible fluid is an important and elegant tool for obstacle avoidance of mobile robots. Obstacles are modeled as cylindrical objects - combinations of cylinders can also form super obstacles. Possible trajectories of a vehicle are given by a set of streamlines around the obstacle computed by the velocity potential. Because of the number of streamlines and of data points involved therein, models of sets of streamlines for different sizes of obstacles are created first using dataset models and finally fuzzy models of streamlines. Once an obstacle appears in the sensor cone of the robot the set of streamlines is computed from which that streamline is selected that guarantees a smooth transition from/to the planned trajectory. Collisions with other robots are avoided by a combination of velocity potential and force potential and/or the change of streamlines during operation (lane hopping).

Obstacle avoidance is an important issue for off-line path planning and on-line reaction to unforeseen appearance of obstacles during motion of a non-holonomic mobile robot along apredefined trajectory. Possible trajectories for obstacle avoidance are modeled by the velocity potential using a uniform flow plus a doublet representing a cylindrical obstacle. In the case of an appearance of an obstacle in the sensor cone of the robot a set of streamlines is computed from which a streamline is selected that guarantees a smooth transition from/to the planned trajectory. To avoid collisions with other robots a combination of velocity potential and force potential and/or the change of streamlines during operation (lane hopping) are discussed.

This paper addresses the robust fuzzy control problem for discrete-time nonlinear systems in the presence of sampling time uncertainties. The case of the discrete T-S fuzzy system with sampling-time uncertainty is considered and a robust controller design method is proposed. The sufficient conditions and the design procedure are formulated in the form of linear matrix inequalities (LMI). The effectiveness of the proposed controller design methodology is demonstrated of a visual-servoing control problem.

In this paper, we address the design of an attitude controller that achieves stable, and robust aggressive maneuverability for an unmanned helicopter. The controller proposed is in the form of a fuzzy gain-scheduler, and is used for stable and robust altitude, roll, pitch, and yaw control. The controller is obtained from a realistic nonlinear multiple-input-multiple-output model of a real unmanned helicopter platform, the APID-MK3. The results of this work are illustrated by extensive simulation, showing that the objective of aggressive, and robust maneuverability has been achieved.

We focus on the analysis and design of two different sliding mode observers for dynamic Takagi-Sugeno (TS) fuzzy systems. A nonlinear system of this class is composed of multiple affine local linear models that are smoothly interpolated by weighting functions resulting from a fuzzy partitioning of the state space of a given nonlinear system subject to observation. The Takagi-Sugeno fuzzy system is then an accurate approximation of the original nonlinear system. Our approach to the analysis and design of observers for Takagi-Sugeno fuzzy systems is based on extending sliding mode observer schemes to the case of interpolated multiple local affine linear models. Thus, our main contribution is nonlinear observer analysis and design methods that can effectively deal with model/plant mismatches. Furthermore, we consider the difficult case when the weighting functions in the Takagi-Sugeno fuzzy system depend on the estimated state.

We consider the analysis and design of three different types of nonlinear observers for dynamic Takagi-Sugeno fuzzy systems. Our approach is based on extending existing nonlinear observer schemes, namely Thau-Luenberger and sliding mode observers, to the case of interpolated multiple local affine linear models. Then linear matrix inequality based techniques are used for observer analysis and design.

An intelligent physical agent must incorporate motor and perceptual processes to interface with the physical world, and abstract cognitive processes to reason about the world and the options available. One crucial aspect of incorporating cognitive processes into a physically embedded reasoning system is the integration between the symbols used by the reasoning processes to denote physical objects, and the perceptual data corresponding to these objects. We treat this integration aspect by proposing a fuzzy computational theory of anchoring. Anchoring is the process of creating and maintaining the correspondence between symbols and percepts that refer to the same physical objects. Modeling this process using fuzzy set-theoretic notions enables dealing with perceptual data that can be affected by uncertainty/imprecision and imprecise/vague linguistic descriptions of objects.

The work reported in the paper is aimed at achieving aggressive manoeuvrability for an unmanned helicopter APID MK-III by Scandicraft AB in Sweden. The manoeuvrability problem is treated at the level of attitude (pitch, roll, yaw) and the aim is to achieve stabilization of the attitude angles within much larger ranges than currently available. We present a fuzzy gain scheduling control approach based on two different types of Iinearization of the original nonlinear APID MK-III model. The performance of the fuzzy gain scheduled controllers is evaluated in simulation and shows that they are effective means for achieving the desired robust manoeuvrability.

We consider the possibility of generalizing the notion of a fuzzy If-Then rule to take into account its context dependent nature. We interpret fuzzy rules as modeling a forward directed causal relationship between the antecedent and the conclusion, which applies in most contexts, but on occasion breaks down in exceptional contexts. The default nature of the rule is modeled by augmenting the original If-Then rule with an exception part. We then consider the proper semantic correlate to such an addition and propose a ternary relation which satisfies a number of intuitive constraints described in terms of a number of inference rules. In the rest of the paper, we consider implementational issues arising from the unless extension and propose the use of reason maintenance systems, in particular TMS's, where a fuzzy If-Then-Unless rule is encoded into a dependency net. We verify that the net satisfies the constraints stated in the inference schemes and conclude with a discussion concerning the integration of qualitative IN-OUT labelings of the TMS with quantitative degree of membership labelings for the variables in question.

The objective of the present article is twofold: first, to provide ways for eliciting consistent a priori and conditional probabilities for a set of events representing pieces of evidence and hypotheses in the context of a rule based expert system. Then an algorithm is proposed which uses the least possible number of a prior and conditional probabilities as its input and which computes the lower and upper bounds for higher order conditional and joint probabilities, so that these be consistent with the input probabilities provided. In the case, when inconsistent lower and upper bounds are obtained, it is suggested how the latter can be turned into consistent ones, by changing the values of only these input probabilities which are directly represented in the higher order probability under consideration. Secondly, a number of typical cases with respect to the problems of aggregation and propagation of uncertainty in expert systems is considered. It is shown how these can be treated by using higher order joint probabilities. For this purpose no global assumptions for independence of evidence and for mutual exclu-siveness of hypotheses are required, since the presence of independent and/or dependent pieces of evidence, as well as the presence of mutually exclusive hypotheses, is explicitly encoded in the input probabilities and thus, such a presence is automatically detected by the algorithm when computing higher order joint probabilities.

The intended purpose of the present article is two-fold: first, introducing an interval-like representation of uncertainty that is an adequate summary of the following two items of information: a report on how strongly the validity of a proposition is supported by a body of evidence and a report on how strongly the validity of its negation is supported. A representation of this type is called a beliefinterval and is introduced as a subinterval of a certain verbal scale consisting of nine linguistic estimates expressing the amount of support provided for the validity of a proposition and/or its negation; each linguistic estimate is represented as a fuzzy number in the interval [0,1]. A belief-interval is bounded from below by an estimate indicating the so-called degree of support and from above by an estimate indicating the so-called degree of plauibility. The latter is defined as the difference between a fuzzy number representing the maximal degree of support that might be provided for a proposition in general and a fuzzy number expressing the degree of support provided for the validity of the negation of the proposition under consideration. The so-introduced degrees of support and plausibility of a proposition are subjective measurements provided by the expert on the basis of some negative and/or positive evidence available to him. Thus, these two notions do not have the same measure-based origins as do the set-theoretic measures of support and plausibility proposed by G. Shafer, neither do they coincide with the possibility and necessity measures proposed by L. Zadeh. The main difference is that in our case the degree of plausibility might be, in cases of contradictory beliefs, less than its corresponding degree of support. Three types of belief-intervals are identified on the basis of the different amounts of support that might be provided for the validity of a proposition and/or its negation, namely balanced, unbalanced, and contradictory belief-intervals. The second objective of this article is to propose a calculus for the belief-intervals by extending the usual logical connectives and, or, negation, and implies. Thus, conjunctive and disjunctive operators are introduced using the Dubois' parametrized family of T-norms and their dual T-conorms. The parameter Q characterizing the latter is being interpreted as a measure of the strength of these connectives and further interpretation of the notion of strength is done in the cases of independent and dependent evidence. This leads to the introduction of specific conjunctive and disjunctive operators to be used separately in each of the latter two cases. A negation operator is proposed with the main purpose of determining the belief-interval to be assigned to the negation of a particular proposition, given the belief-interval of the proposition alone. A so-called aggregation operator is introduced with the purpose of aggregating multiple belief-intervals assigned to one and the same proposition into a total belief-interval for this particular proposition. Detachment operators are proposed for determining the belief-interval of a conclusion given the belief-interval of the premise and the one that represents the amount of belief commited to the validity of the inference rule itself. Two different detachment operators are constructed for use in cases when: (1) the presence of the negation of the premise suggests the presence of the negation of the conclusion, and (2) when the presence of the negation of the premise does not tell anything at all with respect to the validity of the conclusion to be drawn.