The Mecanum wheel is one of the practical omni-directional wheel designs in industry, especially for heavy-duty tasks in a confined floor. An issue with Mecanum-wheeled robots is inefficient use of energy. In this study, the robotic motion trajectories are optimized to minimize the energy consumption, where a robotic path is expressed in polynomial functions passing through a given set of via points, and a genetic algorithm is used to find the polynomial’s coefficients being decision variables. To attempt a further reduction in the energy consumption, the via points are also taken as decision variables for the optimization. Both simulations and experiments are conducted, and the results show that the optimized trajectories result in a significant reduction in energy consumption, which can be further lowered when the via points become decision variables. It is also found that the higher the order of the polynomials the larger the reduction in the energy consumption.

We show that a nearly square independent and identically distributed random integral matrix is surjective over the integral lattice with very high probability. This answers a question by Koplewitz [6]. Our result extends to sparse matrices as well as to matrices of dependent entries.

Design fixation refers to blind adherence to a set of ideas, which can limit the output of conceptual design. Engineering designers tend to fixate on features of pre-existing solutions and consequently generate designs with similar features. The objective of this study is to leverage functional magnetic resonance imaging (fMRI) to study the brain activity of engineering designers during conceptual design in order to understand whether/where design fixation can be detected in a person’s brain when solving design problems. Design solutions indicated that fixation effects were detectable at a statistically significant level. fMRI results show increased activation in areas associated with visuospatial processing when comparing ideation activities using an Example solution to No Example solution. Activation was found in the right inferior temporal gyrus, left middle occipital gyrus, and right superior parietal lobule regions. The left lingual and superior frontal gyri were found to be less active in the example condition; these gyri are close in proximity to the prefrontal cortex, associated with creative output. The spatial patterns of activation provide evidence that a shift in mental resources can occur when a designer becomes fixated. For designers, the timing of ideation relative to the timing of benchmarking existing solutions should be considered.

This paper investigates a comparative kinematic analysis between nonredundant and redundant 2-Degree Of Freedom parallel manipulators. The nonredundant manipulator is based on the Five-Bar mechanism, and the redundant one is a 3-RRR planar parallel manipulator. This study is aimed to select the best structure for a haptic application. This latter requires a mechanism with a desired workspace of 10 cm × 10 cm and an admissible force of 5 N in all directions. The analysis criteria are the accuracy of the forward kinematic model and the required actuator torques. Thereby, the geometric parameters of the two structures are optimized in order to satisfy the required workspace such that parallel singularities are overcome. The analysis showed that the nonredundant optimally designed manipulator is more suitable for the haptic application.

Among an array of techniques proposed to speed-up reinforcement learning (RL), learning from human demonstration has a proven record of success. A related technique, called Human-Agent Transfer, and its confidence-based derivatives have been successfully applied to single-agent RL. This article investigates their application to collaborative multi-agent RL problems. We show that a first-cut extension may leave room for improvement in some domains, and propose a new algorithm called coordination confidence (CC). CC analyzes the difference in perspectives between a human demonstrator (global view) and the learning agents (local view) and informs the agents’ action choices when the difference is critical and simply following the human demonstration can lead to miscoordination. We conduct experiments in three domains to investigate the performance of CC in comparison with relevant baselines.

This paper examines the stability of egocentric networks as reported over time using a novel touchscreen-based participant-aided sociogram. Past work has noted the instability of nominated network alters, with a large proportion leaving and reappearing between interview observations. To explain this instability of networks over time, researchers often look to structural embeddedness, namely the notion that alters are connected to other alters within egocentric networks. Recent research has also asked whether the interview situation itself may play a role in conditioning respondents to what might be the appropriate size and shape of a social network, and thereby which alters ought to be nominated or not. We report on change in these networks across three waves and assess whether this change appears to be the result of natural churn in the network or whether changes might be the result of factors in the interview itself, particularly anchoring and motivated underreporting. Our results indicate little change in average network size across waves, particularly for indirect tie nominations. Slight, significant changes were noted between waves one and two particularly among those with the largest networks. Almost no significant differences were observed between waves two and three, either in terms of network size, composition, or density. Data come from three waves of a Chicago-based panel study of young men who have sex with men.

A recurrent finding in personal network research is that individual and social outcomes are influenced not just by the kind of people one knows, but also by how those people are connected to each other. Personal network structure – the way in which one’s personal contacts know and interact with each other – reflects broader trends in social organization and personal communities, and shapes patterns of social capital, support, and isolation. This article proposes a method to identify typologies of structure in large collections of personal networks. The method is applied to six datasets collected in widely different circumstances and using various survey instruments. It is then compared with another recently introduced method to extract typologies of egocentric network structure. Findings show that personal network structure can be effectively summarized using just three measures of cohesive subgroup characteristics. Structural typologies can then be identified by applying standard cluster analysis techniques to the three variables. Both methods considered in the article capture significant variation in network structures, but they also show substantial levels of disagreement and cross-classification. I discuss similarities and differences between the methods, and potential applications of the proposed typologies to substantive research on personal communities, social support, and social capital.

We used in-depth interviews with 101 participants in the East York section of Toronto, Canada to understand how digital media affects social connectivity in general—and networked individualism in particular—for people at different stages of the life course. Although people of all ages intertwined their use of digital media with their face-to-face interactions, younger adults used more types of digital media and have more diversified personal networks. People in different age-groups conserved media, tending to stick with the digital media they learned to use in earlier life stages. Approximately one-third of the participants were Networked Individuals: In each age-group, they were the most actively using digital media to maintain ties and to develop new ones. Another one-third were Socially Bounded, who often actively used digital media but kept their connectivity within a smaller set of social groups. The remaining one-third, who were Socially Limited, were the least likely to use digital media. Younger adults were the most likely to be Networked Individuals, leading us to wonder if the percentage of the population who are Bounded or Limited will decline over time.

Numerous learning tasks can be described as the process of extrapolating patterns from observed data. One of the driving intuitions behind the theory of algorithmic randomness is that randomness amounts to the absence of any effectively detectable patterns: it is thus natural to regard randomness as antithetical to inductive learning. Osherson and Weinstein [11] draw upon the identification of randomness with unlearnability to introduce a learning-theoretic framework (in the spirit of formal learning theory) for modelling algorithmic randomness. They define two success criteria—specifying under what conditions a pattern may be said to have been detected by a computable learning function—and prove that the collections of data sequences on which these criteria cannot be satisfied correspond to the set of weak 1-randoms and the set of weak 2-randoms, respectively. This learning-theoretic approach affords an intuitive perspective on algorithmic randomness, and it invites the question of whether restricting attention to learning-theoretic success criteria comes at an expressivity cost. In other words, is the framework expressive enough to capture most core algorithmic randomness notions and, in particular, Martin-Löf randomness—arguably, the most prominent algorithmic randomness notion in the literature? In this article, we answer the latter question in the affirmative by providing a learning-theoretic characterisation of Martin-Löf randomness. We then show that Schnorr randomness, another central algorithmic randomness notion, also admits a learning-theoretic characterisation in this setting.

Name generators (NGs) and position generators (PGs) have been used to measure resources embedded in personal relationships, namely social support and social capital, respectively. Comparisons of these measures adopted NGs that only elicit a small number of alters (max. 5). In this paper we explore whether the measurement of social capital with NGs eliciting larger personal networks (say 15 to 20 alters) gives more comparable results to the PG in terms of occupational prestige. To address this issue, we designed a personal network questionnaire that combined a multiple name generator (MNG) and a PG and enquired about alter characteristics and alter-alter ties for the two sets of nominations simultaneously, allowing their integrated analysis. The questionnaire was implemented in the software EgoNet to collect data from social/environmental entrepreneurs in Spain (N = 30) and Mexico (N = 30. The analysis shows that the two approaches capture mostly non-overlapping sets of personal network members, suggesting that the PG measured in this case available, but not accessed social capital. Remarkably the NG led to a higher average prestige for this occupational group than the PG, but also a lower heterogeneity in prestige. The consequences of using one or another approach and their interpretations are discussed.

For a rumour spreading protocol, the spread time is defined as the first time everyone learns the rumour. We compare the synchronous push&pull rumour spreading protocol with its asynchronous variant, and show that for any n-vertex graph and any starting vertex, the ratio between their expected spread times is bounded by $O({n^{1/3}}{\log ^{2/3}}n)$. This improves the $O(\sqrt n)$ upper bound of Giakkoupis, Nazari and Woelfel (2016). Our bound is tight up to a factor of O(log n), as illustrated by the string of diamonds graph. We also show that if, for a pair α, β of real numbers, there exist infinitely many graphs for which the two spread times are nα and nβ in expectation, then $0 \le \alpha \le 1$ and $\alpha \le \beta \le {1 \over 3} + {2 \over 3} \alpha $; and we show each such pair α, β is achievable.

We study random composite structures considered up to symmetry that are sampled according to weights on the inner and outer structures. This model may be viewed as an unlabelled version of Gibbs partitions and encompasses multisets of weighted combinatorial objects. We describe a general setting characterized by the formation of a giant component. The collection of small fragments is shown to converge in total variation toward a limit object following a Pólya–Boltzmann distribution.

Though declining since the 1990s, adolescent pregnancy remains common in the United States. Social supports appear to improve outcomes for pregnant teens; however, teen pregnancy introduces social obstacles, such as stigma. This study investigates how currently or previously pregnant teens’ friendship networks differ from nonpregnant girls using data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) and multilevel regression models. To mitigate concerns that background differences contribute to both pregnancy risk and social networks, girls who experience a pregnancy prior to one data collection time point are compared girls who experience their first pregnancy after this time point. This group who become pregnant after the time point is presumably more similar to already pregnant teens than those never experiencing teen pregnancy. When compared to these girls who become pregnant in the future, those who have already experienced a teen pregnancy report similar numbers of friends (out-nominations) and perceived social acceptance, but are predicted to have fewer peers reporting them as friends (in-nominations) and fewer reciprocated friendships. This suggests that pregnant teens may face stigmatization, of which they may be unaware. It further highlights a new comparison group to account for selection in studies of adolescent pregnancy.

In this research, we propose a two-level control strategy for simultaneous gait generation and stable control of planar walking of the Assume The Robot Is A Sphere (ATRIAS) biped robot with unlocked torso, utilizing active spring-loaded inverted pendulum (ASLIP) as reference models. The upper level consists of an energy-regulating control calculated using the ASLIP model, producing reference ground reaction forces (GRFs) for the desired gait. In the lower level controller, PID force controllers for the motors ensure tracking of the reference GRFs for ATRIAS direct dynamics. Meanwhile, ATRIAS torso angle is controlled stably to make it able to follow a point mass template model. Advantages of the proposed control strategy include simplicity and efficiency. Simulation results using ATRIAS’s complete dynamic model show that the proposed two-level controller can reject initial condition disturbances while generating stable and steady walking motion.

This paper tackles the challenge of the necessity of using the sequence of past environment states as the controller’s inputs in a vision-based robot navigation task. In this task, a robot has to follow a given trajectory without falling in pits and missing its balance in uneven terrain, when the only sensory input is the raw image captured by a camera. The robot should distinguish big pits from small holes to decide between avoiding and passing over. In non-Markov processes such as the abovementioned task, the decision is done using past sensory data to ensure admissible performance. Applying images as sensory inputs naturally causes the curse of dimensionality difficulty. On the other hand, using sequences of past images intensifies this difficulty. In this paper, a new framework called recurrent deep learning (RDL) with combination of deep learning (DL) and recurrent neural network is proposed to cope with the above challenge. At first, the proper features are extracted from the raw image using DL. Then, these represented features plus some expert-defined features are used as the inputs of a fully connected recurrent network (as target network) to generate command control of the robot. To evaluate the proposed RDL framework, some experiments are established on WEBOTS and MATLAB co-simulation platform. The simulation results demonstrate the proposed framework outperforms the conventional controller based on DL for the navigation task in the uneven terrains.

The 3 degree-of-freedom Gantry-Tau manipulator with the addition of the spherical wrist mechanism which is called Gantry-Tau-3R is designed as a high-G simulation-based motion platform (SBMP) with the capability of generating the large linear and angular displacement. The combination of both parallel and serial manipulator in newly designed Gantry-Tau-3R mechanism improves the ability of the mechanism to regenerate larger motion signals with higher linear acceleration and angular velocity. The high-frequency signals are reproduced using the parallel part of the mechanism, and sustainable low-frequency accelerations are regenerated via the serial part due to the larger rotational motion capability, which will be used through motion cueing algorithm tilt coordination channel. The proportional integral derivative (PID) and fuzzy incremental controller (FIC) are developed for the proposed mechanism to show the high path tracking performance as a motion platform. FIC reduces the motion tracking error of the newly designed Gantry-Tau-3R and increases the motion fidelity for the users of the proposed SBMP. The proposed method is implemented using Matlab/Simulink software. Finally, the results demonstrate the accurate motion signal generation using linear model predictive motion cues with a fuzzy controller, which is not possible using the common parallel and serial manipulators.

The aerial manipulator is a special and new type of flying robot composed of a rotorcraft unmanned aerial vehicle (UAV) and a/several manipulator/s. It has gained a lot of attention since its initial appearance in 2010. This is mainly because it enables traditional UAVs to conduct versatile manipulating tasks from air, considerably enriching their applications. In this survey, a complete and systematic review of related research on this topic is conducted. First, various types of structure designs of aerial manipulators are listed out. Subsequently, the modeling and control methods are introduced in detail from the perspective of two types of typical application cases: free-flight and motion-restricted operations. Finally, challenges for future research are presented.

Metaphors are powerful tools for design, enabling designers to encapsulate sets of properties and relations as short verbal descriptions. This paper aims to clarify how simple spatial configurations may emerge from concise metaphoric descriptions at the conceptual design phase. To this aim, we propose a framework for a metaphor-based design process. As a basis for the framework, we introduce the concept of “complementary visual potential” – a property which ties the spatial configuration of the objects in the composition with their metaphoric roles. The framework is developed by studying the practice of metaphor-based spatial configuration design in Japanese rock gardens. Accordingly, it is implemented and tested in this context by attempting to generate alternative designs for an existing rock composition in the famous garden of Ryōan-ji. This is followed by a discussion of its possible implications and potential for generalization to other areas of design.