Industrial robot efficient trajectory generation without collision through the evolution of the optimal trajectory

[EN] An efficient algorithm is presented to obtain trajectories for industrial robots working in industrial environments. The procedure starts with the obtaining of an optimal time trajectory neglecting the presence of obstacles. When obstacles are considered, the initial trajectory (obtained by neglecting obstacles) will not be feasible and will have to evolve so that it can become a solution. In this paper, the way that it evolves until a new feasible collision-free trajectory is obtained considering the possible obstacles is described. This is a direct algorithm that works in a discrete space of trajectories, approaching the global solution as the discretization is refined. The solutions obtained are efficient trajectories near to the minimum time one and they meet the physical limitations of the robot (the maximum values of torque, power and jerk are considered for each actuator), avoid collisions, and take into account the constraint of energy consumed. Examples already published and new examples in real industrial environments have been solved to verify the working of the algorithm.This paper has been made possible thanks to support from the Spanish Ministry of Education, Culture and Sports through the Project for Research and Technological Development with ref. DPI2013-44227-R.Rubio Montoya, FJ.; Llopis Albert, C.; Valero Chuliá, FJ.; Suñer Martinez, JL. (2016). Industrial robot efficient trajectory generation without collision through the evolution of the optimal trajectory. Robotics and Autonomous Systems. 86:106-112. https://doi.org/10.1016/j.robot.2016.09.008S1061128

Motion planning with dynamics awareness for long reach manipulation in aerial robotic systems with two arms

Human activities in maintenance of industrial plants pose elevated risks as well as significant costs due to the required shutdowns of the facility. An aerial robotic system with two arms for long reach manipulation in cluttered environments is presented to alleviate these constraints. The system consists of a multirotor with a long bar extension that incorporates a lightweight dual arm in the tip. This configuration allows aerial manipulation tasks even in hard-to-reach places. The objective of this work is the development of planning strategies to move the aerial robotic system with two arms for long reach manipulation in a safe and efficient way for both navigation and manipulation tasks. The motion planning problem is addressed considering jointly the aerial platform and the dual arm in order to achieve wider operating conditions. Since there exists a strong dynamical coupling between the multirotor and the dual arm, safety in obstacle avoidance will be assured by introducing dynamics awareness in the operation of the planner. On the other hand, the limited maneuverability of the system emphasizes the importance of energy and time efficiency in the generated trajectories. Accordingly, an adapted version of the optimal Rapidly-exploring Random Tree algorithm has been employed to guarantee their optimality. The resulting motion planning strategy has been evaluated through simulation in two realistic industrial scenarios, a riveting application and a chimney repairing task. To this end, the dynamics of the aerial robotic system with two arms for long reach manipulation has been properly modeled, and a distributed control scheme has been derived to complete the test bed. The satisfactory results of the simulations are presented as a first validation of the proposed approach.Unión Europea H2020-644271Ministerio de Ciencia, Innovación y Universidades DPI2014-59383-C2-1-

Efficient trajectory of a car-like mobile robot

This article is (c) Emerald Group Publishing and permission has been granted for this version to appear here https://riunet.upv.es/. Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Group Publishing Limited.[EN] Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Practical implications 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. Social implications The idea is to be friendly with nature and the environment. This algorithm can help by reducing an instance of greenhouse gases. Originality/value 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.Valero Chuliá, FJ.; Rubio Montoya, FJ.; Besa Gonzálvez, AJ.; Llopis Albert, C. (2019). Efficient trajectory of a car-like mobile robot. Industrial Robot An International Journal. 46(2):211-222. https://doi.org/10.1108/IR-10-2018-0214S211222462Ghita, N., & Kloetzer, M. (2012). Trajectory planning for a car-like robot by environment abstraction. Robotics and Autonomous Systems, 60(4), 609-619. doi:10.1016/j.robot.2011.12.004Katrakazas, C., Quddus, M., Chen, W.-H., & Deka, L. (2015). Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions. Transportation Research Part C: Emerging Technologies, 60, 416-442. doi:10.1016/j.trc.2015.09.011Li, B., & Shao, Z. (2015). Simultaneous dynamic optimization: A trajectory planning method for nonholonomic car-like robots. Advances in Engineering Software, 87, 30-42. doi:10.1016/j.advengsoft.2015.04.011Rubio, F., Llopis-Albert, C., Valero, F., & Suñer, J. L. (2016). Industrial robot efficient trajectory generation without collision through the evolution of the optimal trajectory. Robotics and Autonomous Systems, 86, 106-112. doi:10.1016/j.robot.2016.09.008Rubio, F., Valero, F., Lluís Sunyer, J., & Garrido, A. (2010). The simultaneous algorithm and the best interpolation function for trajectory planning. Industrial Robot: An International Journal, 37(5), 441-451. doi:10.1108/01439911011063263Sariff, N., & Buniyamin, N. (2006). An Overview of Autonomous Mobile Robot Path Planning Algorithms. 2006 4th Student Conference on Research and Development. doi:10.1109/scored.2006.4339335Renny Simba, K., Uchiyama, N., & Sano, S. (2016). Real-time smooth trajectory generation for nonholonomic mobile robots using Bézier curves. Robotics and Computer-Integrated Manufacturing, 41, 31-42. doi:10.1016/j.rcim.2016.02.002Tokekar, P., Karnad, N., & Isler, V. (2014). Energy-optimal trajectory planning for car-like robots. Autonomous Robots, 37(3), 279-300. doi:10.1007/s10514-014-9390-

Coordination of several robots based on temporal synchronization

© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/This paper proposes an approach to deal with the problem of coordinating multi-robot systems, in which each robot executes individually planned tasks in a shared workspace. The approach is a decoupled method that can coordinate the participating robots in on-line mode. The coordination is achieved through the adjustment of the time evolution of each robot along its original planned geometric path according to the movements of the other robots to assure a collision-free execution of their respective tasks. To assess the proposed approach different tests were performed in graphical simulations and real experiments.Postprint (published version

Optimal time trajectories for industrial robots with torque, power, jerk and energy consumed constraints

This article is (c) Emerald Group Publishing and permission has been granted for this version to appear here https://riunet.upv.es/. Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Group Publishing Limited.[EN] Purpose - 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. Design/methodology/approach - 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. Findings - 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. Research limitations/implications - 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. Practical implications - 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. Originality/value - 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).This paper has been made possible thanks to support from the Spanish Ministry of Science and Innovation, through the Project for Research and Technological Development, ref. DPI2010-20 814-C02-01.Rubio Montoya, FJ.; Valero Chuliá, FJ.; Suñer Martinez, JL.; Cuadrado Iglesias, JI. (2012). Optimal time trajectories for industrial robots with torque, power, jerk and energy consumed constraints. Industrial Robot: An International Journal. 39(1):92-100. doi:10.1108/01439911211192538]S9210039

Methodology to evaluate transversal competences in the master's degree in industrial engineering based on a system of rubrics and indicators

[EN] This paper presents a methodology to evaluate transversal competences in the context of the subject “Design and application of industrial equipment” in the Master's Degree in Industrial Engineering at Universitat Politècnica de València (Spain). The competency-based education implies several activities, such as a project-based learning that must be eventually defended in public by students in groups. Evidence of learning is collected based on a well-defined system of rubrics and indicators, which are known in advance by students. We have observed that the use of such techniques improves the students learning on the contents of the subject, allows to acquire the transversal competences related to the analysis and problem solving, and enhances the ability to understand concepts intuitively. Moreover, results clearly show a positive influence on the use of such tools for improving the professional and ethical commitment to the issues raised.Llopis-Albert, C.; Rubio, F. (2021). Methodology to evaluate transversal competences in the master's degree in industrial engineering based on a system of rubrics and indicators. Multidisciplinary Journal for Education, Social and Technological Sciences. 8(1):30-44. https://doi.org/10.4995/muse.2021.15244OJS304481Eberle, B. (1996). Scamper: Games for Imagination Development. Prufrock Press Inc. ISBN 978-1-882664-24-5.Llopis-Albert, C., Rubio, F., Valero, F. (2015). Improving productivity using a multi-objective optimization of robotic trajectory planning. Journal of Business Research, 68 (7), 1429-1431. https://doi.org/10.1016/j.jbusres.2015.01.027Llopis-Albert, C., Rubio, F., Valero, F. (2018). Optimization approaches for robot trajectory planning. Multidisciplinary Journal for Education, Social and Technological Sciences, 5(1), 1-16. https://doi.org/10.4995/muse.2018.9867Llopis-Albert, C., Rubio, F., Valero, F. (2019). Fuzzy-set qualitative comparative analysis applied to the design of a network flow of automated guided vehicles for improving business productivity. Journal of Business Research, 101, 737-742. https://doi.org/10.1016/j.jbusres.2018.12.076Llopis-Albert, C., Rubio, F., Valero, F., Liao, H., Zeng, S. (2019a). Stochastic inverse finite element modeling for characterization of heterogeneous material properties. Materials Research Express, 6(11), 115806. https://doi.org/10.1088/2053-1591/ab4c72Llopis-Albert, C., Valero, F., Mata, V., Pulloquinga, J.L., Zamora-Ortiz, P., Escarabajal, R.J. (2020). Optimal Reconfiguration of a Parallel Robot for Forward Singularities Avoidance in Rehabilitation Therapies. A Comparison via Different Optimization Methods. Sustainability, 12(14), 5803. https://doi.org/10.3390/su12145803Llopis-Albert, C., Valero, F., Mata, V., Zamora-Ortiz, P., Escarabajal, R.J., Pulloquinga, J.L. (2020a). Optimal Reconfiguration of a Limited Parallel Robot for Forward Singularities Avoidance. Multidisciplinary Journal for Education, Social and Technological Sciences, 7(1), 113-127. https://doi.org/10.4995/muse.2020.13352Rubio, F., Llopis-Albert, C., Valero, F., Suñer, J.L. (2015). Assembly Line Productivity Assessment by Comparing Optimization-Simulation Algorithms of Trajectory Planning for Industrial Robots. Mathematical Problems in Engineering, 10 pages. Article ID 931048. https://doi.org/10.1155/2015/931048Rubio, F., Llopis-Albert, C., Valero, F., & Suñer, J. L. (2016). Industrial robot efficient trajectory generation without collision through the evolution of the optimal trajectory. Robotics and Autonomous Systems, 86, 106-112. https://doi.org/10.1016/j.robot.2016.09.008Rubio, F., Llopis-Albert, C. (2019). Viability of using wind turbines for electricity generation in electric vehicles. Multidisciplinary Journal for Education, Social and Technological Sciences, 6(1), 115-126. https://doi.org/10.4995/muse.2019.11743Rubio, F., Valero, F., & Llopis-Albert, C. (2019a). A review of mobile robots: Concepts, methods, theoretical framework, and applications. International Journal of Advanced Robotic Systems, 16(2), 172988141983959. https://doi.org/10.1177/1729881419839596Rubio, F., Llopis-Albert, C., Valero, F., Besa, A.J. (2020). Sustainability and optimization in the automotive sector for adaptation to government vehicle pollutant emission regulations. Journal of Business Research 112, 561-566. https://doi.org/10.1016/j.jbusres.2019.10.050UPV, 2020. Proyecto institucional competencias transversales. Universitat Politècnica de València (UPV). Valencia. Spain. https://www.upv.es/entidades/ICE/info/Proyecto_Institucional_CT.pdfValero, F., Rubio, F., Llopis-Albert, C., Cuadrado, J.I. (2017). Influence of the Friction Coefficient on the Trajectory Performance for a Car-Like Robot. Mathematical Problems in Engineering, 9 pages. Article ID 4562647. https://doi.org/10.1155/2017/4562647Valero, F., Rubio, F., Llopis-Albert, C. (2019). Assessment of the Effect of Energy Consumption on Trajectory Improvement for a Car-like Robot. Robotica, 37(11), 1998-2009. https://doi.org/10.1017/S0263574719000407Valero, F., Rubio, F., Besa, A.J. (2019a). Efficient trajectory of a car-like mobile robot. Industrial Robot: the international journal of robotics research and application, 46(2), 211-222. https://doi.org/10.1108/IR-10-2018-021

Trajectory planning for industrial robot using genetic algorithms

En las últimas décadas, debido la importancia de sus aplicaciones, se han propuesto muchas investigaciones sobre la planificación de caminos y trayectorias para los manipuladores, algunos de los ámbitos en los que pueden encontrarse ejemplos de aplicación son; la robótica industrial, sistemas autónomos, creación de prototipos virtuales y diseño de fármacos asistido por ordenador. Por otro lado, los algoritmos evolutivos se han aplicado en muchos campos, lo que motiva el interés del autor por investigar sobre su aplicación a la planificación de caminos y trayectorias en robots industriales. En este trabajo se ha llevado a cabo una búsqueda exhaustiva de la literatura existente relacionada con la tesis, que ha servido para crear una completa base de datos utilizada para realizar un examen detallado de la evolución histórica desde sus orígenes al estado actual de la técnica y las últimas tendencias. Esta tesis presenta una nueva metodología que utiliza algoritmos genéticos para desarrollar y evaluar técnicas para la planificación de caminos y trayectorias. El conocimiento de problemas específicos y el conocimiento heurístico se incorporan a la codificación, la evaluación y los operadores genéticos del algoritmo. Esta metodología introduce nuevos enfoques con el objetivo de resolver el problema de la planificación de caminos y la planificación de trayectorias para sistemas robóticos industriales que operan en entornos 3D con obstáculos estáticos, y que ha llevado a la creación de dos algoritmos (de alguna manera similares, con algunas variaciones), que son capaces de resolver los problemas de planificación mencionados. El modelado de los obstáculos se ha realizado mediante el uso de combinaciones de objetos geométricos simples (esferas, cilindros, y los planos), de modo que se obtiene un algoritmo eficiente para la prevención de colisiones. El algoritmo de planificación de caminos se basa en técnicas de optimización globales, usando algoritmos genéticos para minimizar una función objetivo considerando restricciones para evitar las colisiones con los obstáculos. El camino está compuesto de configuraciones adyacentes obtenidas mediante una técnica de optimización construida con algoritmos genéticos, buscando minimizar una función multiobjetivo donde intervienen la distancia entre los puntos significativos de las dos configuraciones adyacentes, así como la distancia desde los puntos de la configuración actual a la final. El planteamiento del problema mediante algoritmos genéticos requiere de una modelización acorde al procedimiento, definiendo los individuos y operadores capaces de proporcionar soluciones eficientes para el problema.Abu-Dakka, FJM. (2011). Trajectory planning for industrial robot using genetic algorithms [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10294Palanci

A Hierarchal Planning Framework for AUV Mission Management in a Spatio-Temporal Varying Ocean

The purpose of this paper is to provide a hierarchical dynamic mission planning framework for a single autonomous underwater vehicle (AUV) to accomplish task-assign process in a limited time interval while operating in an uncertain undersea environment, where spatio-temporal variability of the operating field is taken into account. To this end, a high level reactive mission planner and a low level motion planning system are constructed. The high level system is responsible for task priority assignment and guiding the vehicle toward a target of interest considering on-time termination of the mission. The lower layer is in charge of generating optimal trajectories based on sequence of tasks and dynamicity of operating terrain. The mission planner is able to reactively re-arrange the tasks based on mission/terrain updates while the low level planner is capable of coping unexpected changes of the terrain by correcting the old path and re-generating a new trajectory. As a result, the vehicle is able to undertake the maximum number of tasks with certain degree of maneuverability having situational awareness of the operating field. The computational engine of the mentioned framework is based on the biogeography based optimization (BBO) algorithm that is capable of providing efficient solutions. To evaluate the performance of the proposed framework, firstly, a realistic model of undersea environment is provided based on realistic map data, and then several scenarios, treated as real experiments, are designed through the simulation study. Additionally, to show the robustness and reliability of the framework, Monte-Carlo simulation is carried out and statistical analysis is performed. The results of simulations indicate the significant potential of the two-level hierarchical mission planning system in mission success and its applicability for real-time implementation

Goal Set Inverse Optimal Control and Iterative Re-planning for Predicting Human Reaching Motions in Shared Workspaces

To enable safe and efficient human-robot collaboration in shared workspaces it is important for the robot to predict how a human will move when performing a task. While predicting human motion for tasks not known a priori is very challenging, we argue that single-arm reaching motions for known tasks in collaborative settings (which are especially relevant for manufacturing) are indeed predictable. Two hypotheses underlie our approach for predicting such motions: First, that the trajectory the human performs is optimal with respect to an unknown cost function, and second, that human adaptation to their partner's motion can be captured well through iterative re-planning with the above cost function. The key to our approach is thus to learn a cost function which "explains" the motion of the human. To do this, we gather example trajectories from pairs of participants performing a collaborative assembly task using motion capture. We then use Inverse Optimal Control to learn a cost function from these trajectories. Finally, we predict reaching motions from the human's current configuration to a task-space goal region by iteratively re-planning a trajectory using the learned cost function. Our planning algorithm is based on the trajectory optimizer STOMP, it plans for a 23 DoF human kinematic model and accounts for the presence of a moving collaborator and obstacles in the environment. Our results suggest that in most cases, our method outperforms baseline methods when predicting motions. We also show that our method outperforms baselines for predicting human motion when a human and a robot share the workspace.Comment: 12 pages, Accepted for publication IEEE Transaction on Robotics 201