Sensors and interfacing in robotics and manufacturing

Sensing is one of the most essential aspects of any robotic application, be it manufacturing or any automated process. Robotic sensors can be divided into two classes of “internal” and “external”. Examples of the first group are position or velocity, while the second group includes proximity, touch, or vision to name a few. The other important problem in manufacturing is interfacing with the environment within a cell. The object of this tutorial paper is to survey the two issues of “sensing” and “interfacing” in robotics and manufacturing.     

Identification of partial disjunction, parity, and threshold functions

Let F be a class of functions obtained by replacing some inputs of a Boolean function of a fixed type with some constants. The problem considered in this paper, which is called attribute efficient learning, is to identify “efficiently” a Boolean function g out of F by asking for the value of g at chosen inputs, where “efficiency” is measured in terms of the number of essential variables. We study the query complexity of attribute-efficient learning for three function classes that are, respectively, obtained from disjunction, parity, and threshold functions. In many cases, we obtain almost optimal upper and lower bound on the number of queries.     

A view-based neurocomputational system for relational map-making and navigation in visual environments

Artificial navigation systems stand to benefit greatly from learning maps of visual environments, but traditional map-making techniques are inadequate in several respects. This paper describes an adaptive, view-based, relational map-making system for navigating within a 3D environment defined by a spatially distributed set of visual landmarks. Inspired by an analogy to learning aspect graphs of 3D objects, the system comprises two neurocomputational architectures that emulate cognitive mapping in the rat hippocampus. The first architecture performs unsupervised place learning by combining the “What” with the “Where”, namely through conjunctions of landmark identity, pose, and egocentric gaze direction within a local, restricted sensory view of the environment. The second associatively learns action consequences by incorporating the “When”, namely through conjunctions of learned places and coarsely coded robot motions. Together, these networks form a map reminiscent of a partially observable Markov decision process, and consequently provide an ideal neural substrate for prediction, environment recognition, route planning, and exploration. Preliminary results from real-time implementations on a mobile robot called MAVIN (the Mobile Adaptive VIsual Navigator) demonstrate the potential for these capabilities.     

Justification of new technologies through the use of “cause and effect” analysis: Case of tile replacement at Kennedy space center

This paper presents a case of introducing new technology to a single stage in a maintenance operation composed of sequence of stages. The process - Thermal tile replacement - is a low volume, high value operation. A method for data collection at each stage, to estimate the variability in process quality, cost and duration is presented. The method involves: Identifying key product features, accuracy measure for each, rate of product rejection by feature and the associated probability density functions at each stage. The method relates accuracy variability by feature, “effect” to the contributing stage in the process “cause”. Simulation is used to justify the introduction of a new technology and to predict the percentage of product conformity in a “before” and “after” scenarios for the implementation of the new technology. The simulation model enables the quantification of technology impact on the product quality, overall productivity and the associated cost savings.     

An efficient algorithm for large-scale quasi-supervised learning

We present a novel formulation for quasi-supervised learning that extends the learning paradigm to large datasets. Quasi-supervised learning computes the posterior probabilities of overlapping datasets at each sample and labels those that are highly specific to their respective datasets. The proposed formulation partitions the data into sample groups to compute the dataset posterior probabilities in a smaller computational complexity. In experiments on synthetic as well as real datasets, the proposed algorithm attained significant reduction in the computation time for similar recognition performances compared to the original algorithm, effectively generalizing the quasi-supervised learning paradigm to applications characterized by very large datasets.     

Robust adaptive fuzzy tracking control for a class of perturbed strict-feedback nonlinear systems via small-gain approach

This paper presents a novel robust adaptive fuzzy tracking controller (RAFTC) for a wide class of perturbed strict-feedback nonlinear systems with both unknown system and virtual control gain nonlinearities. For unknown system nonlinearities, two types for them are included: one naturally satisfies the “triangularity condition” and may possess a class of unstructured uncertain functions which are not linearly parameterized, while the other is partially known and consists of parametric uncertainties and known “bounding functions”. The Takagi–Sugeno type fuzzy logic systems are used to approximate unknown system nonlinearities and a systematic design procedure is developed for synthesis of RAFTC by combining the backstepping technique and generalized small-gain approach. The algorithm proposed is highlighted by three advantages: (i) the semi-global uniform ultimate bound of RAFTC in the presence of perturbed uncertainties and unknown virtual control gain nonlinearities can be guaranteed, (ii) the adaptive mechanism with minimal learning parameterizations is obtained and (iii) the possible controller singularity problem in some of the existing adaptive control schemes with feedback linearization techniques can be removed. Performance and limitations of proposed method are discussed and illustrated with simulation results.     

Recognizing image “style” and activities in video using local features and naive Bayes

The goal of this paper is to offer a framework for classification of images and video according to their “type”, or “style”––a problem which is hard to define, but easy to illustrate; for example, identifying an artist by the style of his/her painting, or determining the activity in a video sequence. The paper offers a simple classification paradigm based on local properties of spatial or spatio-temporal blocks. The learning and classification are based on the naive Bayes classifier. A few experimental results are presented.     

Learning and motivational couplings promote smarter behaviors of an animat in an unknown world

This paper reports on statistical results of an animat behavior in an unknown environment using a cognitive map. We study the coupling effect between the motivation for eating and the one for drinking. Some smart behaviors are not caused by a sophisticated “intelligent” algorithm, but only through coupling of the motivations. Adding a learning rule on the links of the cognitive map allows to reinforce particular paths, and to forget others. This also leads to reinforce the same “smart” behaviors.     

A learning result for continuous-time recurrent neural networks

The following learning problem is considered, for continuous-time recurrent neural networks having sigmoidal activation functions. Given a “black box” representing an unknown system, measurements of output derivatives are collected, for a set of randomly generated inputs, and a network is used to approximate the observed behavior. It is shown that the number of inputs needed for reliable generalization (the sample complexity of the learning problem) is upper bounded by an expression that grows polynomially with the dimension of the network and logarithmically with the number of output derivatives being matched.     

Law, learning and representation

In machine learning terms, reasoning in legal cases can be compared to a lazy learning approach in which courts defer deciding how to generalize beyond the prior cases until the facts of a new case are observed. The HYPO family of systems implements a “lazy” approach since they defer making arguments how to decide a problem until the programs have positioned a new problem with respect to similar past cases. In a kind of “reflective adjustment”, they fit the new problem into a patchwork of past case decisions, comparing cases in order to reason about the legal significance of the relevant similarities and differences. Empirical evidence from diverse experiments shows that for purposes of teaching legal argumentation and performing legal information retrieval, HYPO-style systems' lazy learning approach and implementation of aspects of reflective adjustment can be very effective.     

Conflict resolution - simulation language

Simulation is used as a tool for aiding in resolving conflicts between negotiating parties. A computer based general simulation language “NEGOTIATE” is partially developed and is used in modeling and simulating situations of conflicts. The user uses commando to simulate scenarios and to specify the issues of conflict/ negotiation, the current status of each and its functional ranking for each opposing party. However “NEGOTIATE” is in its early stage of development , the results achieved so far promise a great potential in resolving many of the situations of conflict.     

Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position

Suggested by the structure of the visual nervous system, a new algorithm is proposed for pattern recognition. This algorithm can be realized with a multilayered network consisting of neuron-like cells. The network, “neocognitron”, is self-organized by unsupervised learning, and acquires the ability to recognize stimulus patterns according to the differences in their shapes: Any patterns which we human beings judge to be alike are also judged to be of the same category by the neocognitron. The neocognitron recognizes stimulus patterns correctly without being affected by shifts in position or even by considerable distortions in shape of the stimulus patterns.     

Digital integrator design incorporating an output scaler

A digital integrator design is proposed which has one or more built in “potentiometers”. It is intended primarily for use as the elementary module of a digital differential analyser (DDA) for process control and the solution of differential equations. By so reducing the number of machine elements, the “patching” problem is considerably eased, especially when electrically programable interconnections are necessary.     

On the feasibility of IEEE 802.11 multi-channel multi-hop mesh networks

Several scientific works have considered the possibility to build Wireless Mesh Networks (WMN) using multi-channel IEEE 802.11 architectures. At the basis of these works is the notion of “non-overlapping” channels, i.e. with a frequency separation equal or greater than 25 MHz. It is now a common assumption that multiple independent transmissions over these channels can coexist without mutual interference even in physical proximity. In this work we demonstrate that this assumption does not hold in general. Through an extensive set of experiments we illustrate the presence of cross-channel interference between “non-overlapping” channels at relay nodes due to the “near-far” effect. We analyze in what manner the MAC layer reacts to such an interference and how this problem extends to higher layers, with detrimental effects on the global throughput. The central problem is that cross-channel interference is not handled adequately by the MAC layer, and in some cases single-channel multi-hop settings perform better than multi-channel. Our results highlight a serious mismatch between some routing and channel assignment schemes proposed recently by the research community, assuming full separation between non-overlapping channels, and what is achievable in practice. More generally, as the presence of cross-channel interference can not be neglected at relay nodes, our findings point to a fundamental problem in building Multi-channel Multi-hop WMN based on IEEE 802.11b/g technology.     

Large-scale simulations of self-organizing neural networks on parallel computers: application to biological modelling

In this contribution we report about a study of a very versatile neural network algorithm known as “Self-organizing Feature Maps” and based on earlier work of Kohonen [1,2]. In its original version, the algorithm addresses a fundamental issue of brain organization, namely how topographically ordered maps of sensory information can be formed by learning.

This algorithm is investigated for a large number of neurons (up to 16 K) and for an input space of dimension d900. To meet the computational demands this algorithm was implemented on two parallel machines, on a self-built Transputer systolic ring and on a Connection Machine CM-2.

We will present below

1. (i) a simulation based on the feature map algorithm modelling part of the synaptic organization in the “hand-region” of the somatosensory cortex,

2. (ii) a study of the influence of the dimension of the input-space on the learning process,

3. (iii) a simulation of the extended algorithm, which explicitly includes lateral interactions, and

4. (iv) a comparison of the transputer-based “coarse-grained” implementation of the model, and the “fine-grained” implementation of the same system on the Connection Machine.

     

Retrieval of movie scenes by semantic matrix and automatic feature weight update

A new semantic-based video scene retrieval method is proposed in this paper. Twelve low-level features extracted from a video clip are represented in a genetic chromosome and target videos that user has in mind are retrieved by the interactive genetic algorithm through the feedback iteration. In this procedure, high-level semantic relevance between retrieved videos is accumulated with so-called semantic relevance matrix and semantic frequency matrix for each iteration, and they are combined with an automatic feature weight update scheme to retrieve more target videos at the next iteration. Experiments over 300 movie scene clips extracted from latest well-known movies, showed an user satisfaction of 0.71 at the fourth iteration for eight queries such as “gloominess”, “happiness”, “quietness”, “action”, “conversation”, “explosion”, “war”, and “car chase”.     

Learning in analog neural network hardware

Hardware implementations of neuroprocessor architectures are currently enjoying commercial availability for the first time ever. This development has been caused in part by the requirement for real-time solutions to time critical neural network applications. Massively parallel asynchronous neuromorphic representations are inherently capable of very high computational speeds when properly cast in the “right stuff”, i.e. electronic or optoelectronic hardware. However, hardware based learning in such systems is still at a primitive stage. In practise, simulations are typically performed in software, and the resulting synaptic weight capturing the input-output transformation subsequently quantized and down-loaded onto the neural hardware. However, because of the numerous discrepancies between the software and hardware, such systems are inherently poor in performance. In this paper we report on chip-in-the-loop learning systems assembled from custom analog “building blocks” hardware.     

What a histogram can really tell the classifier

This paper presents a simple and efficient clustering technique based on the partitioning of the data histogram. The clustering technique was developed in the context of a study of possible unsupervized classification procedures for multispectral earth imagery. The ultimate goal was on-board data compression, the algorithmic production of thematic maps.

The incoming raw spectral data is first reduced to its two principal (Karhunen-Loêve) components, the histogram of which is then partitioned into natural classes on the sole weight of evidence of the global statistics of the imagery. During the course of the study, it became clear that some connection existed between the proposed philosophy and professor Thom's novel theory of “catastrophes”.⁽¹⁾ A simple metric is added to the histogram topology. The metric uses both Shannon's and Fisher's notions of self information. In the domain of definition of the histogram, zones corresponding to the natural classes become separated by a no man's land, an inter-class zone. Under the same formulation, the metric is “Euclidean” on the class zone, “non-Euclidean”, i.e. “Lorentz” on the inter-class zone. This methodology and the underlying philosophy were tested in practice, and encouraging results were obtained.     

Cellular automata synchronization

The first part of this paper deals with the Firing Squad Synchronization problem extended to networks however connected. A new class of configurations called “flower graphs” is presented whose synchronization is useful to reduce synchronization time for any graph. The second part investigates the lower bounds for synchronization time from a theoretical point of view. Results are obtained that are valid for several classes of graphs.