Humanoid robots, unmanned rovers, entertainment pets, drones, and so on are great examples of mobile robots. They can be distinguished from other robots by their ability to move autonomously, with enough intelligence to react and make decisions based on the perception they receive from the environment. Mobile robots must have some source of input data, some way of decoding that input, and a way of taking actions (including its own motion) to respond to a changing world. The need to sense and adapt to an unknown environment requires a powerful cognition system. Nowadays, there are mobile robots that can walk, run, jump, and so on like their biological counterparts. Several fields of robotics have arisen, such as wheeled mobile robots, legged robots, flying robots, robot vision, artificial intelligence, and so on, which involve different technological areas such as mechanics, electronics, and computer science. In this article, the world of mobile robots is explored including the new trends. These new trends are led by artificial intelligence, autonomous driving, network communication, cooperative work, nanorobotics, friendly human-robot interfaces, safe human-robot interaction, and emotion expression and perception. Furthermore, these news trends are applied to different fields such as medicine, health care, sports, ergonomics, industry, distribution of goods, and service robotics. These tendencies will keep going their evolution in the coming years.

Highlights Application of fsQCA to analyze the future impact of digital transformation on the automotive industry. The disruptive effect of the introduction of electric vehicles on the market is considered. Government policies regarding the reduction of greenhouse gas emissions in the automotive sector have been taken into account. The methodology has been successfully applied to the automotive industry in Spain. Small and medium-sized enterprises lack digital transformation strategies, which may have a dramatic effect in the long term in Spain.

We study the consistency of sample mean-variance portfolios of arbitrarily high dimension that are based on Bayesian or shrinkage estimation of the input parameters as well as weighted sampling. In an asymptotic setting where the number of assets remains comparable in magnitude to the sample size, we provide a characterization of the estimation risk by providing deterministic equivalents of the portfolio out-of-sample performance in terms of the underlying investment scenario. The previous estimates represent a means of quantifying the amount of risk underestimation and return overestimation of improved portfolio constructions beyond standard ones. Well-known for the latter, if not corrected, these deviations lead to inaccurate and overly optimistic Sharpe-based investment decisions. Our results are based on recent contributions in the field of random matrix theory. Along with the asymptotic analysis, the analytical framework allows us to find bias corrections improving on the achieved out-of-sample performance of typical portfolio constructions. Some numerical simulations validate our theoretical findings.

PurposeThe purpose of this paper is to solve the path‐planning problem of industrial robots in complex environments.Design/methodology/approachA direct method (in each step, the path is been recorded) is presented in which the search of the path is made in the state space of the robotic system, and it makes use of the information generated about the characteristics of the process, introducing graph techniques for branching. The method poses an optimization problem that aims at minimizing the distance travelled by the significant points of the robot.FindingsA new approach to solve the path‐planning problem has been introduced in which the behaviour of three operational parameters (computational time, distance travelled and number of configurations generated) have been analyzed so that the user can choose the most efficient algorithm depending on which parameter he is most interested in.Research limitations/implicationsA new technique has been introduced which yields good results as the examples show.Practical implicationsThe algorithm is able to obtain the solution to the path‐planning problem for any industrial robot working in a complex environment.Originality/valueGives a new tool for solving the path‐planning problem.

In modern asset management, portfolio managers address the multi-account investment decision problem by optimizing each account's portfolio separately based on the trading requirements and portfolio constraints of the individual clients. However, trades associated with the individual accounts are usually pooled together for execution, therefore amplifying the level of the so-called market impact on all accounts. If this aggregate market impact is not considered when each account is individually optimized, the actual market impact can be severely underestimated. Multi-portfolio optimization aims at finding the optimal rebalancing of the multiple accounts by considering their joint effects while adhering to account-specific constraints. In this paper, we first model this phenomenon as a Nash Equilibrium problem (NEP) and thereafter consider a generalized NEP (GNEP) for the case where there are global constraints imposed on all accounts, adopting as a desirable outcome the concept of Nash Equilibrium (NE). For both game problems, we give a complete characterization of the NE, including its existence and uniqueness, and devise various distributed algorithms with provable convergence. Interestingly, the proposed methodology heavily hinges on a number of well-known and important signal processing techniques.

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A new method for subspace identification in array signal processing applications is proposed. The method is based on random matrix theory and provides consistent estimates even when the observation dimension increases without bound at the same rate as the number of observations. This guarantees a good behavior in finite sample size situations, where the number of sensors and the number of samples have the same order of magnitude. Consistency of the algorithm holds in situations where the signal and noise subspaces are asymptotically separable in the sense that, in the asymptotic sample eigenvalue distribution, signal and noise eigenvalues generate different spectral clusters.

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