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The purpose is to create an algorithm that optimizes the trajectories that an autonomous vehicle must follow to reduce its energy consumption and reduce the emission of greenhouse gases.

An algorithm is presented that respects the dynamic constraints of the robot, including the characteristics of power delivery by the motor, the behaviour of the tires and the basic inertial parameters. Using quadratic sequential programming with distributed and non-monotonous search direction (Quadratic Programming Algorithm with Distributed and Non-Monotone Line Search), an optimization algorithm proposed and developed by Professor K. Schittkowski is implemented.

Relations between important operating variables have been obtained, such as the evolution of the autonomous vehicle’s velocity, the driving torque supplied by the engine and the forces acting on the tires. In a subsequent analysis, the aim is to analyse the relationship between trajectory made and energy consumed and calculate the reduction of greenhouse gas emissions. Also this method has been checked against another different methodology commented on in the references.

The main limitation comes from the modelling that has been done. As greater is the mechanical systems analysed, more simplifying hypotheses should be introduced to solve the corresponding equations with the current computers. However, the solutions are obtained and they can be used qualitatively to draw conclusions.

One main objective is to obtain guidelines to reduce greenhouse gas emissions by reducing energy consumption in the realization of autonomous vehicles’ trajectories. The first step to achieve that is to obtain a good model of the autonomous vehicle that takes into account not only its kinematics but also its dynamic properties, and to propose an optimization process that allows to minimize the energy consumed. In this paper, important relationships between work variables have been obtained.

The idea is to be friendly with nature and the environment. This algorithm can help by reducing an instance of greenhouse gases.

Originality comes from the fact that we not only look for the autonomous vehicle’s modelling, the simulation of its motion and the analysis of its working parameters, but also try to obtain from its working those guidelines that are useful to reduce the energy consumed and the contamination capability of these autonomous vehicles or car-like robots.

The purpose of this paper is to solve the path‐planning problem of industrial robots in complex environments.

A 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.

A 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.

A new technique has been introduced which yields good results as the examples show.

The algorithm is able to obtain the solution to the path‐planning problem for any industrial robot working in a complex environment.

Gives a new tool for solving the path‐planning problem.

The purpose of this paper is to solve the trajectory planning problem of industrial robots in a complex environment.

A simultaneous algorithm was presented in which the trajectory was generated gradually as the robot moves. It takes into account the presence of obstacles (to avoid collisions) and differential constraints related to the dynamics of the robotic system. The method poses an optimization problem that aims at minimizing the time to perform the trajectory when several interpolation functions are used.

A new approach to solving the trajectory planning problem in which the behaviour of four operational parameters (execution time, computational time, distance travelled and number of configurations) have been analyzed when changing the interpolation functions, therefore enabling the user to choose the most efficient algorithm depending on which parameter the user is most interested in. From the examples solved the interpolation function that yields the best results has been found.

This new technique is very time consuming due to the great number of mathematical calculations that have to be made. However, it yields a solution.

The algorithm is able to obtain the solution to the trajectory planning problem for any industrial robot. Also, even mobile obstacles in the workspace could be incorporated at the same time as the robot is moving and creating the path and the time history of motion.

It gives a new tool for solving the trajectory planning problem and describes the best interpolation function.

The purpose of this paper is to compare the quality and efficiency of five methods for solving the path planning problem of industrial robots in complex environments.

In total, five methods are presented for solving the path planning problem and certain working parameters have been monitored using each method. These working parameters are the distance travelled by the robot and the computational time needed to find a solution. A comparison of results has been analyzed.

After this study, it could be easy to know which of the proposed methods is most suitable for application in each case, depending on the parameter the user wants to optimize. The findings have been summarized in the conclusion section.

The five techniques which have been developed yield good results in general.

The algorithms introduced are able to solve the path planning problem for any industrial robot working with obstacles.

The path planning algorithms help robots perform their tasks in a more efficient way because the path followed has been optimized and therefore they help human beings work together with the robots in order to obtain the best results from them.

The paper shows which algorithm offers the best results, depending on the example the user has to solve and the parameter to be optimized.

The purpose of this paper is to analyze the impact of the torque, power, jerk and energy consumed constraints on the generation of minimum time collision‐free trajectories for industrial robots in a complex environment.

An algorithm is presented in which the trajectory is generated under real working constraints (specifically torque, power, jerk and energy consumed). It also takes into account the presence of obstacles (to avoid collisions) and the dynamics of the robotic system. The method solves an optimization problem to find the minimum time trajectory to perform the tasks the robot should do.

Important conclusions have been reached when solving the trajectory planning problem related to the value of the torque, power, jerk and energy consumed and the relationship between them, therefore enabling the user to choose the most efficient way of working depending on which parameter he is most interested in optimizing. From the examples solved the authors have found the relationship between the maximum and minimum values of the parameters studied.

This new approach tries to model the real behaviour of the actuators in order to be able to upgrade the trajectory quality, so a lot of work has to be done in this field.

The algorithm solves the trajectory planning problem for any industrial robot and the real characteristics of the actuators are taken into account, which is essential to improve the performance of it.

This new tool enables the performance of the robot to be improved by combining adequately the values of the mentioned parameters (torque, power, jerk and consumed energy).

The purpose of this paper is to present the development and validation of a methodology which allows modeling and solving the inverse and direct dynamic problem in real time in robot manipulators.

The robot dynamic equation is based on the Gibbs‐Appell equation of motion, yielding a well‐structured set of equations that can be computed in real time. This paper deals with the implementation and calculation of the inverse and direct dynamic problem in robots, with an application to the real‐time control of a PUMA 560 industrial robot provided with an open control architecture based on an industrial personal computer.

The experimental results show the validity of the dynamic model and that the proposed resolution method for the dynamic problem in real time is suitable for control purposes.

The accuracy of the applied friction model determines the accuracy of the identified overall model and consequently of the control. This is especially obvious in the case of the PUMA 560 robot, in which the presence of friction is remarkable in some of their joints. Hence, future work should focus on identifying a more precise friction model. The robot model could also be extended by incorporating rotor dynamics and could be applied for different robot configurations as parallel robots.

Gibbs‐Appell equations are used in order to develop the robotic manipulator dynamic model, instead of more usual dynamics formulations, due to several advantages that these exhibit. The obtained non‐physical identified parameters are adapted in order to enable their use in a control algorithm.

The purpose of this paper is to present and interpret the “Enterprise Politics Model (EPM)” developed by Professor Antonio Valero, Founder and first Dean of IESE Business School, University of Navarra, Spain.

Drawing from a careful reading of Valero’s writings in their original context and some developments of these by his followers, this paper systematically presents and discusses the key ideas of Valero’s model for management and corporate governance.

The main features of Valero’s philosophy of the firm and of senior management can be summarized in four points: the firm as a community of persons; the firm as an intermediate social institution serving the common good of society; the different nature of political and technical practice, which leads senior management to exercise practical reason – not only science or technique, and at the same time a kind of political art, or wisdom; and the role and responsibility of entrepreneurs and top management. Valero emphasizes the political nature of management and, from a practical perspective, suggests a global analysis based on four big areas of governance: business activity, managing structure, institutional configuration, and professional community. He makes his model applicable by developing “political procedures”.

Valero’s “EPM” is an original humanistic approach to management and corporate governance, with implications to business education. Valero’s contributions were based on his business and teaching experience and in a deep humanistic background, but adopted an intuitive outlook and need further conceptual development, actualization to contemporary business context and empirical research on the relationship between this model and performance.

Valero’s “EPM” is a practically oriented humanistic approach to management and corporate governance which can be a realistic alternative to conventional, and often criticized, approaches to management and corporate governance. In fact, it has already been successfully applied in several companies.

In a context of growing discontent toward capitalism and the role of business in society, the “EPM” – discussed in this paper – shows how business might be run and organized to be socially responsible, contributing to the common good and respecting individual rights and flourishing.

The paper discusses, systematizes and interprets an innovative way of understanding management and corporate governance.

Mayan towns in the Guatemalan highlands hold periodic markets on specific days of the week. A market is attended by local townspeople, by peasants residing in the town’s hinterland, and by vendors bringing wares from other towns. This study aims to determine the effects of physical, environmental, and cultural differences on the number of vendors that are sent from one Guatemalan town to a periodic market in another.

To understand how these markets are integrated, a gravity model is developed, examining the flow of vendors from 85 towns of residence to 15 market towns. In this model, the flow of vendors from one town to another is a function not only of physical distance, but of ecological complementarities, of linguistic differences, of road access, and of demographic endowments.

Results show that traveling vendors in these periodic markets do indeed integrate Guatemala both ethnically and ecologically, serving as a place in which different ethnic groups meet and bring in products that cannot be produced locally. Results also suggest that participation in markets is part of a diversified set of activities used by rural peasants to support their households.