350 research outputs found

    Efficient Path Interpolation and Speed Profile Computation for Nonholonomic Mobile Robots

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    This paper studies path synthesis for nonholonomic mobile robots moving in two-dimensional space. We first address the problem of interpolating paths expressed as sequences of straight line segments, such as those produced by some planning algorithms, into smooth curves that can be followed without stopping. Our solution has the advantage of being simpler than other existing approaches, and has a low computational cost that allows a real-time implementation. It produces discretized paths on which curvature and variation of curvature are bounded at all points, and preserves obstacle clearance. Then, we consider the problem of computing a time-optimal speed profile for such paths. We introduce an algorithm that solves this problem in linear time, and that is able to take into account a broader class of physical constraints than other solutions. Our contributions have been implemented and evaluated in the framework of the Eurobot contest

    Efficient trajectory of a car-like mobile robot

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    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-

    Robust Model Predictive Control for Linear Parameter Varying Systems along with Exploration of its Application in Medical Mobile Robots

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    This thesis seeks to develop a robust model predictive controller (MPC) for Linear Parameter Varying (LPV) systems. LPV models based on input-output display are employed. We aim to improve robust MPC methods for LPV systems with an input-output display. This improvement will be examined from two perspectives. First, the system must be stable in conditions of uncertainty (in signal scheduling or due to disturbance) and perform well in both tracking and regulation problems. Secondly, the proposed method should be practical, i.e., it should have a reasonable computational load and not be conservative. Firstly, an interpolation approach is utilized to minimize the conservativeness of the MPC. The controller is calculated as a linear combination of a set of offline predefined control laws. The coefficients of these offline controllers are derived from a real-time optimization problem. The control gains are determined to ensure stability and increase the terminal set. Secondly, in order to test the system's robustness to external disturbances, a free control move was added to the control law. Also, a Recurrent Neural Network (RNN) algorithm is applied for online optimization, showing that this optimization method has better speed and accuracy than traditional algorithms. The proposed controller was compared with two methods (robust MPC and MPC with LPV model based on input-output) in reference tracking and disturbance rejection scenarios. It was shown that the proposed method works well in both parts. However, two other methods could not deal with the disturbance. Thirdly, a support vector machine was introduced to identify the input-output LPV model to estimate the output. The estimated model was compared with the actual nonlinear system outputs, and the identification was shown to be effective. As a consequence, the controller can accurately follow the reference. Finally, an interpolation-based MPC with free control moves is implemented for a wheeled mobile robot in a hospital setting, where an RNN solves the online optimization problem. The controller was compared with a robust MPC and MPC-LPV in reference tracking, disturbance rejection, online computational load, and region of attraction. The results indicate that our proposed method surpasses and can navigate quickly and reliably while avoiding obstacles

    Cellular Automata Applications in Shortest Path Problem

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    Cellular Automata (CAs) are computational models that can capture the essential features of systems in which global behavior emerges from the collective effect of simple components, which interact locally. During the last decades, CAs have been extensively used for mimicking several natural processes and systems to find fine solutions in many complex hard to solve computer science and engineering problems. Among them, the shortest path problem is one of the most pronounced and highly studied problems that scientists have been trying to tackle by using a plethora of methodologies and even unconventional approaches. The proposed solutions are mainly justified by their ability to provide a correct solution in a better time complexity than the renowned Dijkstra's algorithm. Although there is a wide variety regarding the algorithmic complexity of the algorithms suggested, spanning from simplistic graph traversal algorithms to complex nature inspired and bio-mimicking algorithms, in this chapter we focus on the successful application of CAs to shortest path problem as found in various diverse disciplines like computer science, swarm robotics, computer networks, decision science and biomimicking of biological organisms' behaviour. In particular, an introduction on the first CA-based algorithm tackling the shortest path problem is provided in detail. After the short presentation of shortest path algorithms arriving from the relaxization of the CAs principles, the application of the CA-based shortest path definition on the coordinated motion of swarm robotics is also introduced. Moreover, the CA based application of shortest path finding in computer networks is presented in brief. Finally, a CA that models exactly the behavior of a biological organism, namely the Physarum's behavior, finding the minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From software to wetware. Springer, 201

    Assessment of the Effect of Energy Consumption on Trajectory Improvement for a Car-like Robot

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    [EN] Reducing the energy consumed by a car-like mobile robot makes it possible to move at a lower cost, yet it takes more working time. This paper proposes an optimization algorithm for trajectories with optimal times and analyzes the consequences of restricting the energy consumed on the trajectory obtained for a car-like robot. When modeling the dynamic behavior of the vehicle, it is necessary to consider its inertial parameters, the behavior of the motor, and the basic properties of the tire in its interaction with the ground. To obtain collision-free, minimum-time trajectories quadratic sequential optimization techniques are used, where the objective function is the time taken by the robot to move between two given configurations. This is subject to constraints relating to the vehicle and tires as well as the energy consumed, which is the basis for this paper. We work with a real random distribution of consumed energy values following a normal Gaussian distribution in order to analyze its influence on the trajectories obtained by the vehicle. The energy consumed, the time taken, the maximum velocity reached, and the distance traveled are analyzed in order to characterize the properties of the trajectories obtained. The proposed algorithm has been applied to 101 examples, showing that the computational times needed to obtain the solutions are always lower than those required to realize the trajectories. The results obtained allow us to reach conclusions about the energy efficiency of the trajectories.Valero Chuliá, FJ.; Rubio Montoya, FJ.; 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/S0263574719000407S199820093711Rubio, 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:10.1108/01439911011063263Liu, S., & Sun, D. (2014). Minimizing Energy Consumption of Wheeled Mobile Robots via Optimal Motion Planning. IEEE/ASME Transactions on Mechatronics, 19(2), 401-411. doi:10.1109/tmech.2013.2241777Renny 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.2016.02.00

    Feasible, Robust and Reliable Automation and Control for Autonomous Systems

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    The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences

    Practical application of pseudospectral optimization to robot path planning

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    To obtain minimum time or minimum energy trajectories for robots it is necessary to employ planning methods which adequately consider the platform’s dynamic properties. A variety of sampling, graph-based or local receding-horizon optimisation methods have previously been proposed. These typically use simplified kino-dynamic models to avoid the significant computational burden of solving this problem in a high dimensional state-space. In this paper we investigate solutions from the class of pseudospectral optimisation methods which have grown in favour amongst the optimal control community in recent years. These methods have high computational efficiency and rapid convergence properties. We present a practical application of such an approach to the robot path planning problem to provide a trajectory considering the robot’s dynamic properties. We extend the existing literature by augmenting the path constraints with sensed obstacles rather than predefined analytical functions to enable real world application

    Motion planning for constrained mobile robots in unknown environments

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    Ph.DDOCTOR OF PHILOSOPH
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