5,678 research outputs found

    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-

    Negotiating the Probabilistic Satisfaction of Temporal Logic Motion Specifications

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    We propose a human-supervised control synthesis method for a stochastic Dubins vehicle such that the probability of satisfying a specification given as a formula in a fragment of Probabilistic Computational Tree Logic (PCTL) over a set of environmental properties is maximized. Under some mild assumptions, we construct a finite approximation for the motion of the vehicle in the form of a tree-structured Markov Decision Process (MDP). We introduce an efficient algorithm, which exploits the tree structure of the MDP, for synthesizing a control policy that maximizes the probability of satisfaction. For the proposed PCTL fragment, we define the specification update rules that guarantee the increase (or decrease) of the satisfaction probability. We introduce an incremental algorithm for synthesizing an updated MDP control policy that reuses the initial solution. The initial specification can be updated, using the rules, until the supervisor is satisfied with both the updated specification and the corresponding satisfaction probability. We propose an offline and an online application of this method.Comment: 9 pages, 4 figures; The results in this paper were presented without proofs in IEEE/RSJ International Conference on Intelligent Robots and Systems November 3-7, 2013 at Tokyo Big Sight, Japa

    Iterative Temporal Motion Planning for Hybrid Systems in Partially Unknown Environments

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    This paper considers the problem of motion planning for a hybrid robotic system with complex and nonlinear dynamics in a partially unknown environment given a temporal logic specification. We employ a multi-layered synergistic framework that can deal with general robot dynamics and combine it with an iterative planning strategy. Our work allows us to deal with the unknown environmental restrictions only when they are discovered and without the need to repeat the computation that is related to the temporal logic specification. In addition, we define a metric for satisfaction of a specification. We use this metric to plan a trajectory that satisfies the specification as closely as possible in cases in which the discovered constraint in the environment renders the specification unsatisfiable. We demonstrate the efficacy of our framework on a simulation of a hybrid second-order car-like robot moving in an office environment with unknown obstacles. The results show that our framework is successful in generating a trajectory whose satisfaction measure of the specification is optimal. They also show that, when new obstacles are discovered, the reinitialization of our framework is computationally inexpensive

    A Hierarchical Extension of the D ∗ Algorithm

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    In this paper a contribution to the practice of path planning using a new hierarchical extension of the D ∗ algorithm is introduced. A hierarchical graph is stratified into several abstraction levels and used to model environments for path planning. The hierarchical D∗ algorithm uses a downtop strategy and a set of pre-calculated trajectories in order to improve performance. This allows optimality and specially lower computational time. It is experimentally proved how hierarchical search algorithms and on-line path planning algorithms based on topological abstractions can be combined successfully

    Hierarchical D ∗ algorithm with materialization of costs for robot path planning

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    In this paper a new hierarchical extension of the D ∗ algorithm for robot path planning is introduced. The hierarchical D ∗ algorithm uses a down-top strategy and a set of precalculated paths (materialization of path costs) in order to improve performance. This on-line path planning algorithm allows optimality and specially lower computational time. H-Graphs (hierarchical graphs) are modified and adapted to support on-line path planning with materialization of costs and multiple hierarchical levels. Traditional on-line robot path planning focused in horizontal spaces is also extended to vertical and interbuilding spaces. Some experimental results are showed and compared to other path planning algorithms

    Learning visual docking for non-holonomic autonomous vehicles

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    This paper presents a new method of learning visual docking skills for non-holonomic vehicles by direct interaction with the environment. The method is based on a reinforcement algorithm, which speeds up Q-learning by applying memorybased sweeping and enforcing the “adjoining property”, a filtering mechanism to only allow transitions between states that satisfy a fixed distance. The method overcomes some limitations of reinforcement learning techniques when they are employed in applications with continuous non-linear systems, such as car-like vehicles. In particular, a good approximation to the optimal behaviour is obtained by a small look-up table. The algorithm is tested within an image-based visual servoing framework on a docking task. The training time was less than 1 hour on the real vehicle. In experiments, we show the satisfactory performance of the algorithm

    Sampling-Based Temporal Logic Path Planning

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    In this paper, we propose a sampling-based motion planning algorithm that finds an infinite path satisfying a Linear Temporal Logic (LTL) formula over a set of properties satisfied by some regions in a given environment. The algorithm has three main features. First, it is incremental, in the sense that the procedure for finding a satisfying path at each iteration scales only with the number of new samples generated at that iteration. Second, the underlying graph is sparse, which guarantees the low complexity of the overall method. Third, it is probabilistically complete. Examples illustrating the usefulness and the performance of the method are included.Comment: 8 pages, 4 figures; extended version of the paper presented at IROS 201
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