Whole arm manipulation using closed and hybrid planar chains: A kinematic study

This paper studies planar whole arm manipulation of a circular object using closed loop and hybrid manipulators. The manipulation is simple with fewer degrees of actuation than the task space. This is an useful operation if the initial and final positions of the object are on the same surface. Closed loop manipulator is a 4/5 bar mechanism. In hybrid manipulators a open loop manipulator with 3/4 links is attached to the floating link of 4/5 bar mechanism. The mobility analysis is carried out to find the connectivity of the object with reference to frame. The manipulation (forward kinematics) starts from a given configuration of the object and the manipulator. In hybrid manipulators determination of initial configuration involves inverse kinematics of open loop manipulator. The input joint velocities are used to demonstrate the manipulation. Conditions are specified for prehensile manipulation.

Generalization of the collision cone approach for motion safety in 3-D environments

Avoidance of collision between moving objects in a 3-D environment is fundamental to the problem of planning safe trajectories in dynamic environments. This problem appears in several diverse fields including robotics, air vehicles, underwater vehicles and computer animation. Most of the existing literature on collision prediction assumes objects to be modelled as spheres. While the conservative spherical bounding box is valid in many cases, in many other cases, where objects operate in close proximity, a less conservative approach, that allows objects to be modelled using analytic surfaces that closely mimic the shape of the object, is more desirable. In this paper, a collision cone approach (previously developed only for objects moving on a plane) is used to determine collision between objects, moving in 3-D space, whose shapes can be modelled by general quadric surfaces. Exact collision conditions for such quadric surfaces are obtained and used to derive dynamic inversion based avoidance strategies.

Computational algorithms inspired by biological processes and evolution

In recent times computational algorithms inspired by biological processes and evolution are gaining much popularity for solving science and engineering problems. These algorithms are broadly classified into evolutionary computation and swarm intelligence algorithms, which are derived based on the analogy of natural evolution and biological activities. These include genetic algorithms, genetic programming, differential evolution, particle swarm optimization, ant colony optimization, artificial neural networks, etc. The algorithms being random-search techniques, use some heuristics to guide the search towards optimal solution and speed-up the convergence to obtain the global optimal solutions. The bio-inspired methods have several attractive features and advantages compared to conventional optimization solvers. They also facilitate the advantage of simulation and optimization environment simultaneously to solve hard-to-define (in simple expressions), real-world problems. These biologically inspired methods have provided novel ways of problem-solving for practical problems in traffic routing, networking, games, industry, robotics, economics, mechanical, chemical, electrical, civil, water resources and others fields. This article discusses the key features and development of bio-inspired computational algorithms, and their scope for application in science and engineering fields.

Perfect Static Balance of Linkages by Addition of Springs But Not Auxiliary Bodies

A linkage of rigid bodies under gravity loads can be statically counter-balanced by adding compensating gravity loads. Similarly, gravity loads or spring loads can be counterbalanced by adding springs. In the current literature, among the techniques that add springs, some achieve perfect static balance while others achieve only approximate balance. Further, all of them add auxiliary bodies to the linkage in addition to springs. We present a perfect static balancing technique that adds only springs but not auxiliary bodies, in contrast to the existing techniques. This technique can counter-balance both gravity loads and spring loads. The technique requires that every joint that connects two bodies in the linkage be either a revolute joint or a spherical joint. Apart from this, the linkage can have any number of bodies connected in any manner. In order to achieve perfect balance, this technique requires that all the spring loads have the feature of zero-free-length, as is the case with the existing techniques. This requirement is neither impractical nor restrictive since the feature can be practically incorporated into any normal spring either by modifying the spring or by adding another spring in parallel. DOI: 10.1115/1.4006521]

A Compact and Compliant External Pipe-Crawling Robot

The focus of this paper is on the practical aspects of design, prototyping, and testing of a compact, compliant external pipe-crawling robot that can inspect a closely spaced bundle of pipes in hazardous environments and areas that are inaccessible to humans. The robot consists of two radially deployable compliant ring actuators that are attached to each other along the longitudinal axis of the pipe by a bidirectional linear actuator. The robot imitates the motion of an inchworm. The novel aspect of the compliant ring actuator is a spring-steel compliant mechanism that converts circumferential motion to radial motion of its multiple gripping pads. Circumferential motion to ring actuators is provided by two shape memory alloy (SMA) wires that are guided by insulating rollers. The design of the compliant mechanism is derived from a radially deployable mechanism. A unique feature of the design is that the compliant mechanism provides the necessary kinematic function within the limited annular space around the pipe and serves as the bias spring for the SMA wires. The robot has a control circuit that sequentially activates the SMA wires and the linear actuator; it also controls the crawling speed. The robot has been fabricated, tested, and automated. Its crawling speed is about 45 mm/min, and the weight is about 150 g. It fits within an annular space of a radial span of 15 mm to crawl on a pipe of 60-mm outer diameter.

Extended liaison as an interface between product and process model in assembly

This paper describes the use of liaison to better integrate product model and assembly process model so as to enable sharing of design and assembly process information in a common integrated form and reason about them. Liaison can be viewed as a set, usually a pair, of features in proximity with which process information can be associated. A liaison is defined as a set of geometric entities on the parts being assembled and relations between these geometric entities. Liaisons have been defined for riveting, welding, bolt fastening, screw fastening, adhesive bonding (gluing) and blind fastening processes. The liaison captures process specific information through attributes associated with it. The attributes are associated with process details at varying levels of abstraction. A data structure for liaison has been developed to cluster the attributes of the liaison based on the level of abstraction. As information about the liaisons is not explicitly available in either the part model or the assembly model, algorithms have been developed for extracting liaisons from the assembly model. The use of liaison is proposed to enable both the construction of process model as the product model is fleshed out, as well as maintaining integrity of both product and process models as the inevitable changes happen to both design and the manufacturing environment during the product lifecycle. Results from aerospace and automotive domains have been provided to illustrate and validate the use of liaisons. (C) 2014 Elsevier Ltd. All rights reserved.

Single network adaptive critic aided dynamic inversion for optimal regulation and command tracking with online adaptation for enhanced robustness

To combine the advantages of both stability and optimality-based designs, a single network adaptive critic (SNAC) aided nonlinear dynamic inversion approach is presented in this paper. Here, the gains of a dynamic inversion controller are selected in such a way that the resulting controller behaves very close to a pre-synthesized SNAC controller in the output regulation sense. Because SNAC is based on optimal control theory, it makes the dynamic inversion controller operate nearly optimal. More important, it retains the two major benefits of dynamic inversion, namely (i) a closed-form expression of the controller and (ii) easy scalability to command tracking applications without knowing the reference commands a priori. An extended architecture is also presented in this paper that adapts online to system modeling and inversion errors, as well as reduced control effectiveness, thereby leading to enhanced robustness. The strengths of this hybrid method of applying SNAC to optimize an nonlinear dynamic inversion controller is demonstrated by considering a benchmark problem in robotics, that is, a two-link robotic manipulator system. Copyright (C) 2013 John Wiley & Sons, Ltd.

Design and Modeling of an Active Five-Axis Compliant Micromanipulator

This paper presents the design and modeling of an active five-axis compliant micromanipulator whose tip orientation can be independently controlled by large angles about two axes and the tip-position can be controlled in three dimensions. These features enable precise control of the contact point of the tip and the tip-sample interaction forces with three-dimensional nanoscale objects, including those features that are conventionally inaccessible. Control of the tip-motion is realized by means of electromagnetic actuation combined with a novel kinematic and structural design of the micromanipulator, which, in addition, also ensures compatibility with existing high-resolution motion-measurement systems. The design and analysis of the manipulator structure and those of the actuation system are first presented. Quasi-static and dynamic lumped-parameter (LP) models are then derived for the five-axis compliant micromanipulator. Finite element (FE) analysis is employed to validate these models, which are subsequently used to study the effects of tip orientation on the mechanical characteristics of the five-axis micromanipulator. Finally, a prototype of the designed five-axis manipulator is fabricated by means of focused ion-beam milling (FIB).

Action Recognition from Motion Capture Data using Meta-cognitive RBF Network Classifier

Action recognition plays an important role in various applications, including smart homes and personal assistive robotics. In this paper, we propose an algorithm for recognizing human actions using motion capture action data. Motion capture data provides accurate three dimensional positions of joints which constitute the human skeleton. We model the movement of the skeletal joints temporally in order to classify the action. The skeleton in each frame of an action sequence is represented as a 129 dimensional vector, of which each component is a 31) angle made by each joint with a fixed point on the skeleton. Finally, the video is represented as a histogram over a codebook obtained from all action sequences. Along with this, the temporal variance of the skeletal joints is used as additional feature. The actions are classified using Meta-Cognitive Radial Basis Function Network (McRBFN) and its Projection Based Learning (PBL) algorithm. We achieve over 97% recognition accuracy on the widely used Berkeley Multimodal Human Action Database (MHAD).

Electrically driven intracellular and extracellular nanomanipulators evoke neurogenic/cardiomyogenic differentiation in human mesenchymal stem cells

Nanomechanical intervention through electroactuation is an effective strategy to guide stem cell differentiation for tissue engineering and regenerative medicine. In the present study, we elucidate that physical forces exerted by electroactuated gold nanoparticles (GNPs) have a strong influence in regulating the lineage commitment of human mesenchymal stem cells (hMSCs). A novel platform that combines intracellular and extracellular GNPs as nano-manipulators was designed to trigger neurogenic/cardiomyogenic differentiation in hMSCs, in electric field stimulated culture condition. In order to mimic the native microenvironment of nerve and cardiac tissues, hMSCs were treated with physiologically relevant direct current electric field (DC EF) or pulsed electric field (PEF) stimuli, respectively. When exposed to regular intermittent cycles of DC EF stimuli, majority of the GNP actuated hMSCs acquired longer filopodial extensions with multiple branch-points possessing neural-like architecture. Such morphological changes were consistent with higher mRNA expression level for neural-specific markers. On the other hand, PEF elicited cardiomyogenic differentiation, which is commensurate with the tubelike morphological alterations along with the upregulation of cardiac specific markers. The observed effect was significantly promoted even by intracellular actuation and was found to be substrate independent. Further, we have substantiated the participation of oxidative signaling, G0/G1 cell cycle arrest and intracellular calcium Ca2+] elevation as the key upstream regulators dictating GNP assisted hMSC differentiation. Thus, by adopting dual stimulation protocols, we could successfully divert the DC EF exposed cells to differentiate predominantly into neural-like cells and PEF treated cells into cardiomyogenic-like cells, via nanoactuation of GNPs. Such a novel multifaceted approach can be exploited to combat tissue loss following brain injury or heart failure. (C) 2015 Elsevier Ltd. All rights reserved.

Kernelization complexity of possible winner and coalitional manipulation problems in voting

In the POSSIBLE WINNER problem in computational social choice theory, we are given a set of partial preferences and the question is whether a distinguished candidate could be made winner by extending the partial preferences to linear preferences. Previous work has provided, for many common voting rules, fixed parameter tractable algorithms for the POSSIBLE WINNER problem, with number of candidates as the parameter. However, the corresponding kernelization question is still open and in fact, has been mentioned as a key research challenge 10]. In this paper, we settle this open question for many common voting rules. We show that the POSSIBLE WINNER problem for maximin, Copeland, Bucklin, ranked pairs, and a class of scoring rules that includes the Borda voting rule does not admit a polynomial kernel with the number of candidates as the parameter. We show however that the COALITIONAL MANIPULATION problem which is an important special case of the POSSIBLE WINNER problem does admit a polynomial kernel for maximin, Copeland, ranked pairs, and a class of scoring rules that includes the Borda voting rule, when the number of manipulators is polynomial in the number of candidates. A significant conclusion of our work is that the POSSIBLE WINNER problem is harder than the COALITIONAL MANIPULATION problem since the COALITIONAL MANIPULATION problem admits a polynomial kernel whereas the POSSIBLE WINNER problem does not admit a polynomial kernel. (C) 2015 Elsevier B.V. All rights reserved.

Bayesian Online Changepoint Detection

Changepoints are abrupt variations in the generative parameters of a data sequence. Online detection of changepoints is useful in modelling and prediction of time series in application areas such as finance, biometrics, and robotics. While frequentist methods have yielded online filtering and prediction techniques, most Bayesian papers have focused on the retrospective segmentation problem. Here we examine the case where the model parameters before and after the changepoint are independent and we derive an online algorithm for exact inference of the most recent changepoint. We compute the probability distribution of the length of the current ``run,'' or time since the last changepoint, using a simple message-passing algorithm. Our implementation is highly modular so that the algorithm may be applied to a variety of types of data. We illustrate this modularity by demonstrating the algorithm on three different real-world data sets.

Vision-based global localization using a visual vocabular

This paper presents a novel coarse-to-fine global localization approach that is inspired by object recognition and text retrieval techniques. Harris-Laplace interest points characterized by SIFT descriptors are used as natural land-marks. These descriptors are indexed into two databases: an inverted index and a location database. The inverted index is built based on a visual vocabulary learned from the feature descriptors. In the location database, each location is directly represented by a set of scale invariant descriptors. The localization process consists of two stages: coarse localization and fine localization. Coarse localization from the inverted index is fast but not accurate enough; whereas localization from the location database using voting algorithm is relatively slow but more accurate. The combination of coarse and fine stages makes fast and reliable localization possible. In addition, if necessary, the localization result can be verified by epipolar geometry between the representative view in database and the view to be localized. Experimental results show that our approach is efficient and reliable. ©2005 IEEE.

Image-based localization and pose recovery using scale invariant features

In this paper, we propose a vision based mobile robot localization strategy. Local scale-invariant features are used as natural landmarks in unstructured and unmodified environment. The local characteristics of the features we use prove to be robust to occlusion and outliers. In addition, the invariance of the features to viewpoint change makes them suitable landmarks for mobile robot localization. Scale-invariant features detected in the first exploration are indexed into a location database. Indexing and voting allow efficient recognition of global localization. The localization result is verified by epipolar geometry between the representative view in database and the view to be localized, thus the probability of false localization will be decreased. The localization system can recover the pose of the camera mounted on the robot by essential matrix decomposition. Then the position of the robot can be computed easily. Both calibrated and un-calibrated cases are discussed and relative position estimation based on calibrated camera turns out to be the better choice. Experimental results show that our approach is effective and reliable in the case of illumination changes, similarity transformations and extraneous features. © 2004 IEEE.

Component-based approach to the holonic control of a robot assembly cell

The aim of this paper is to describe the implementation of a new approach for the introduction of so called 'holonic manufacturing' principles into existing production control systems. Such an approach is intended to improve the reconfigurability of the control system to cope with the increasing requirements of production change. A conceptual architecture is described and implemented in a robot assembly cell to demonstrate that this approach can lead to a manufacturing control system which can adapt relatively simply to long-term change. A design methodology and migration strategy for achieving these solutions using conventional hardware is proposed to develop execution level of manufacturing control systems.