Voting as Validation in Robot Programming

Utete, Simukai W.; Barshan, Billur; Ayrulu, Birsel

doi:10.1177/02783649922066277

Cited by 20 publications

(15 citation statements)

References 23 publications

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“…In Utete, Barshan and Ayrulu (1999), based on this algorithm, sensors assign probability masses to plane, corner and acute corner target types using Dempster±Shafer evidential reasoning. Combining the opinions of 15 sensing nodes using Dempster's rule of combination improves the correct decision percentage to 87%.…”

Section: Discussionmentioning

confidence: 99%

Neural networks for improved target differentiation and localization with sonar

Ayrulu

Barshan

2001

Neural Networks

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This study investigates the processing of sonar signals using neural networks for robust differentiation of commonly encountered features in indoor robot environments. Differentiation of such features is of interest for intelligent systems in a variety of applications. Different representations of amplitude and time-of-¯ight measurement patterns acquired from a real sonar system are processed. In most cases, best results are obtained with the low-frequency component of the discrete wavelet transform of these patterns. Modular and non-modular neural network structures trained with the back-propagation and generating±shrinking algorithms are used to incorporate learning in the identi®ca-tion of parameter relations for target primitives. Networks trained with the generating±shrinking algorithm demonstrate better generalization and interpolation capability and faster convergence rate. Neural networks can differentiate more targets employing only a single sensor node, with a higher correct differentiation percentage (99%) than achieved with previously reported methods (61±90%) employing multiple sensor nodes. A sensor node is a pair of transducers with ®xed separation, that can rotate and scan the target to collect data. Had the number of sensing nodes been reduced in the other methods, their performance would have been even worse. The success of the neural network approach shows that the sonar signals do contain suf®cient information to differentiate all target types, but the previously reported methods are unable to resolve this identifying information. This work can ®nd application in areas where recognition of patterns hidden in sonar signals is required. Some examples are system control based on acoustic signal detection and identi®cation, map building, navigation, obstacle avoidance, and target-tracking applications for mobile robots and other intelligent systems. q

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Section: Discussionmentioning

confidence: 99%

Neural networks for improved target differentiation and localization with sonar

Ayrulu

Barshan

2001

Neural Networks

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“…Another example due to Utete et al [202], uses voting techniques for landmark classification from range data.…”

Section: Low-level Processing Of Laser Datamentioning

confidence: 99%

Environment Learning for Indoor Mobile Robots

Andrade‐Cetto¹

2006

Springer Tracts in Advanced Robotics

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This thesis focuses on the various aspects of autonomous environment learning for indoor service robots. Particularly, on landmark extraction from sensor data, autonomous map building, and robot localization.To univocally identify landmarks from sensor data, we study several landmark representations, and the mathematical foundation necessary to extract the features that build them from images and laser range data. The features extracted from just one sensor may not suffice in the invariant characterization of landmarks and objects, pushing for the combination of information from multiple sources. We present a new algorithm that fuses complementary information from two low level vision modules into coherent object models that can be tracked and learned in a mobile robotics context. Illumination conditions and occlusions are the most prominent artifacts that hinder data association in computer vision. By using photogrammetric and geometric constraints we restrict the search for landmark matches in successive images, and by locking our interest in one or a set of landmarks in the scene, we track those landmarks along successive frames, reducing considerably the data association problem. We concentrate on those tools from the geometry of multiple views that are relevant to the computation of initial landmark location estimates for coarse motion recovery; a desirable characteristic when odometry is not available or is highly unreliable.Once landmarks are accurately extracted and identified, the second part of the problem is to use these observations for the localization of the robot, as well as the refinement of the landmark location estimates. We consider robot motion and sensor observations as stochastic processes, and treat the problem from an estimation theoretic point of view, dealing with noise by using probabilistic methods.The main drawback we encounter is that current estimation techniques have been devised for static environments, and that they lack robustness in more realistic situations. To aid in those situations in which landmark observations might not be consistent in time, we propose a new set of temporal landmark quality functions, and show how iii by incorporating these functions in the data association tests, the overall estimationtheoretic approach to map building and localization is improved. The basic idea consists on using the history of data association mismatches for the computation of the likelihood of future data association, together with the spatial compatibility tests already available.Special attention is paid in that the removal of spurious landmarks from the map does not violate the basic convergence properties of the localization and map building algorithms already described in the literature; namely, asymptotic convergence and full correlation.The thesis also gives an in depth analysis of the fully correlated model to localization and map building from a control systems theory point of view. Considering the fact that the Kalman filter is nothing else but an optimal observer,...

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“…The ÿrst is Dempster-Shafer evidential reasoning which is well-suited for dealing with imprecise evidence and uncertainty in a more rational way than other tools [23][24][25]. The second technique is majority voting which provides fast and robust fusion in certain problems [26,27]. Despite the fast and robust fusion capability of majority voting, it involves certain consistency problems that limit its usage.…”

Section: Introductionmentioning

confidence: 99%

Reliability measure assignment to sonar for robust target differentiation

Ayrulu

Barshan

2002

Pattern Recognition

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This article addresses the use of evidential reasoning and majority voting in multi-sensor decision making for target di erentiation using sonar sensors. Classiÿcation of target primitives which constitute the basic building blocks of typical surfaces in uncluttered robot environments has been considered. Multiple sonar sensors placed at geographically di erent sensing sites make decisions about the target type based on their measurement patterns. Their decisions are combined to reach a group decision through Dempster-Shafer evidential reasoning and majority voting. The sensing nodes view the targets at di erent ranges and angles so that they have di erent degrees of reliability. Proper accounting for these di erent reliabilities has the potential to improve decision making compared to simple uniform treatment of the sensors. Consistency problems arising in majority voting are addressed with a view to achieving high classiÿcation performance. This is done by introducing preference ordering among the possible target types and assigning reliability measures (which essentially serve as weights) to each decision-making node based on the target range and azimuth estimates it makes and the belief values it assigns to possible target types. The results bring substantial improvement over evidential reasoning and simple majority voting by reducing the target misclassiÿcation rate. ?

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Voting as Validation in Robot Programming

Abstract: This paper investigates the use of voting as a conflict-resolution technique for data analysis in robot programming. Voting represents an information-abstraction technique. It is argued that in some cases

Cited by 20 publications

References 23 publications

Neural networks for improved target differentiation and localization with sonar

Neural networks for improved target differentiation and localization with sonar

Environment Learning for Indoor Mobile Robots

Reliability measure assignment to sonar for robust target differentiation

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