541 research outputs found

    Kinematic Modeling And Path Planning With Collision Avoidance For Multiple Cartesian Arms

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    Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2006Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2006Kartezyen robotlar, endüstride geniş kullanım alanı bulmaktadır. Birden fazla kartezyen robotun ortak bir iş yapmasına gerek duyulan durumlar vardır. Bu tezde yapılan çalışmanın temeli, aynı çalışma uzayındaki kartezyen robotların çarpışma olmaksızın yörünge planlamasıdır. Bu çalışmanın amacı, aynı çalışma uzayındaki kartezyen robotların konumlandırılması için gerekli algoritmaları bulmak veya türetmektir. Çarpışma sakınımlı yörünge planlaması algoritmalarını kullanarak istenen işin başarılması uzaysal işlem cebriyle kinematik olarak modellenmiş robotik sisteme dayanır. Yörünge planlaması metodları kartezyen robotlara uygulanarak çarpışma olmayan yörüngenin bulunması için algoritmalar geliştirilir.Cartesian robots are already being widely used in industry and their use will substantially increase as the developing technology brings the prices down of high payload and high precision linear motors. There are applications where more than one cartesian robots are required to perform a common task. The focus of the research presented in this thesis is the collision free path planning for multiple cartesian robots sharing the same task space. The objective of this research is to obtain or derive necessary algorithms to coordinate multiple cartesian robots sharing the same workspace. Using path planning algorithms with collision avoidance, the desired task is achieved based on the kinematic model of the complete robotic system which is rooted in the spatial operator algebra. Path planning methods are applied to the cartesian robots and the algorithms to find collision-free path for the cartesian robots are developed.Yüksek LisansM.Sc

    Non-singular assembly mode changing trajectories in the workspace for the 3-RPS parallel robot

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    Having non-singular assembly modes changing trajectories for the 3-RPS parallel robot is a well-known feature. The only known solution for defining such trajectory is to encircle a cusp point in the joint space. In this paper, the aspects and the characteristic surfaces are computed for each operation mode to define the uniqueness of the domains. Thus, we can easily see in the workspace that at least three assembly modes can be reached for each operation mode. To validate this property, the mathematical analysis of the determinant of the Jacobian is done. The image of these trajectories in the joint space is depicted with the curves associated with the cusp points

    PAMPC: Perception-Aware Model Predictive Control for Quadrotors

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    We present the first perception-aware model predictive control framework for quadrotors that unifies control and planning with respect to action and perception objectives. Our framework leverages numerical optimization to compute trajectories that satisfy the system dynamics and require control inputs within the limits of the platform. Simultaneously, it optimizes perception objectives for robust and reliable sens- ing by maximizing the visibility of a point of interest and minimizing its velocity in the image plane. Considering both perception and action objectives for motion planning and control is challenging due to the possible conflicts arising from their respective requirements. For example, for a quadrotor to track a reference trajectory, it needs to rotate to align its thrust with the direction of the desired acceleration. However, the perception objective might require to minimize such rotation to maximize the visibility of a point of interest. A model-based optimization framework, able to consider both perception and action objectives and couple them through the system dynamics, is therefore necessary. Our perception-aware model predictive control framework works in a receding-horizon fashion by iteratively solving a non-linear optimization problem. It is capable of running in real-time, fully onboard our lightweight, small-scale quadrotor using a low-power ARM computer, to- gether with a visual-inertial odometry pipeline. We validate our approach in experiments demonstrating (I) the contradiction between perception and action objectives, and (II) improved behavior in extremely challenging lighting conditions

    Movimientos simétrico lineales esféricos segmentados para interpolación de orientaciones en planificación de trayectorias de herramienta en CNC de 5 Ejes

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    RESUMEN: Este artículo emplea biarcos cuaterniónicos para interpolar un conjunto de orientaciones con restricciones de velocidad angular. La curva cuaterniónica resultante representa un movimiento simétrico lineal esférico segmentado con continuidad C1 . El propósito de este esfuerzo es poner en uso los movimientos simétrico lineales desde el punto de vista de aproximación e interpolación de movimiento y presentar su potencial aplicación en la simulación de mecanizado por Control Numérico Computarizado (CNC) y planeación de trayectorias de herramienta. Los biarcos cuaterniónicos pueden ser usados para aproximar curvas B-spline cuaterniónicas que representan movimientos esféricos racionales, los cuales tienen aplicaciones en planeación de trayectorias de robots, en CAD/CAM y en gráficas por computador.ABSTRACT: This paper employs quaternion biarcs to interpolate a set of orientations with angular velocity constraints. The resulting quaternion curve represents a piecewise line-symmetric spherical motion with C1 continuity. The purpose of this effort is to put line-symmetric motions into use from the viewpoint of motion approximation and interpolation, and to present their potential applications in Computerized Numerical Control (CNC) machining simulation and tool path planning. Quaternion biarcs may be used to approximate B-spline quaternion curves that represent rational spherical motions that have applications in robot path planning, CAD/CAM and computer graphics

    Enhancing 3D Autonomous Navigation Through Obstacle Fields: Homogeneous Localisation and Mapping, with Obstacle-Aware Trajectory Optimisation

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    Small flying robots have numerous potential applications, from quadrotors for search and rescue, infrastructure inspection and package delivery to free-flying satellites for assistance activities inside a space station. To enable these applications, a key challenge is autonomous navigation in 3D, near obstacles on a power, mass and computation constrained platform. This challenge requires a robot to perform localisation, mapping, dynamics-aware trajectory planning and control. The current state-of-the-art uses separate algorithms for each component. Here, the aim is for a more homogeneous approach in the search for improved efficiencies and capabilities. First, an algorithm is described to perform Simultaneous Localisation And Mapping (SLAM) with physical, 3D map representation that can also be used to represent obstacles for trajectory planning: Non-Uniform Rational B-Spline (NURBS) surfaces. Termed NURBSLAM, this algorithm is shown to combine the typically separate tasks of localisation and obstacle mapping. Second, a trajectory optimisation algorithm is presented that produces dynamically-optimal trajectories with direct consideration of obstacles, providing a middle ground between path planners and trajectory smoothers. Called the Admissible Subspace TRajectory Optimiser (ASTRO), the algorithm can produce trajectories that are easier to track than the state-of-the-art for flight near obstacles, as shown in flight tests with quadrotors. For quadrotors to track trajectories, a critical component is the differential flatness transformation that links position and attitude controllers. Existing singularities in this transformation are analysed, solutions are proposed and are then demonstrated in flight tests. Finally, a combined system of NURBSLAM and ASTRO are brought together and tested against the state-of-the-art in a novel simulation environment to prove the concept that a single 3D representation can be used for localisation, mapping, and planning

    Soft Motion Trajectory Planner for Service Manipulator Robot

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    Human interaction introduces two main constraints: Safety and Comfort. Therefore service robot manipulator can't be controlled like industrial robotic manipulator where personnel is isolated from the robot's work envelope. In this paper, we present a soft motion trajectory planner to try to ensure that these constraints are satisfied. This planner can be used on-line to establish visual and force control loop suitable in presence of human. The cubic trajectories build by this planner are good candidates as output of a manipulation task planner. The obtained system is then homogeneous from task planning to robot control. The soft motion trajectory planner limits jerk, acceleration and velocity in cartesian space using quaternion. Experimental results carried out on a Mitsubishi PA10-6CE arm are presented

    Resolved Motion Control for 3D Underactuated Bipedal Walking using Linear Inverted Pendulum Dynamics and Neural Adaptation

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    We present a framework to generate periodic trajectory references for a 3D under-actuated bipedal robot, using a linear inverted pendulum (LIP) based controller with adaptive neural regulation. We use the LIP template model to estimate the robot's center of mass (CoM) position and velocity at the end of the current step, and formulate a discrete controller that determines the next footstep location to achieve a desired walking profile. This controller is equipped on the frontal plane with a Neural-Network-based adaptive term that reduces the model mismatch between the template and physical robot that particularly affects the lateral motion. Then, the foot placement location computed for the LIP model is used to generate task space trajectories (CoM and swing foot trajectories) for the actual robot to realize stable walking. We use a fast, real-time QP-based inverse kinematics algorithm that produces joint references from the task space trajectories, which makes the formulation independent of the knowledge of the robot dynamics. Finally, we implemented and evaluated the proposed approach in simulation and hardware experiments with a Digit robot obtaining stable periodic locomotion for both cases.Comment: 7 pages, to appear in IROS 202

    Conferring robustness to path-planning for image-based control

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    Path-planning has been proposed in visual servoing for reaching the desired location while fulfilling various constraints. Unfortunately, the real trajectory can be significantly different from the reference trajectory due to the presence of uncertainties on the model used, with the consequence that some constraints may not be fulfilled hence leading to a failure of the visual servoing task. This paper proposes a new strategy for addressing this problem, where the idea consists of conferring robustness to the path-planning scheme by considering families of admissible models. In order to obtain these families, uncertainty in the form of random variables is introduced on the available image points and intrinsic parameters. Two families are considered, one by generating a given number of admissible models corresponding to extreme values of the uncertainty, and one by estimating the extreme values of the components of the admissible models. Each model of these families identifies a reference trajectory, which is parametrized by design variables that are common to all the models. The design variables are hence determined by imposing that all the reference trajectories fulfill the required constraints. Discussions on the convergence and robustness of the proposed strategy are provided, in particular showing that the satisfaction of the visibility and workspace constraints for the second family ensures the satisfaction of these constraints for all models bounded by this family. The proposed strategy is illustrated through simulations and experiments. © 2011 IEEE.published_or_final_versio
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