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Hold Or take Optimal Plan (HOOP): A quadratic programming approach to multi-robot trajectory generation

机译:保持或采取最佳计划(HOOP):二次编程方法来生成多机器人轨迹

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摘要

In this work, we present Hold Or take Optimal Plan (HOOP), a centralized trajectory generation algorithm for labeled multi-robot systems operating in obstacle-free, two-dimensional, continuous workspaces. Given a team of N robots, each with nth-order dynamics, our algorithm finds trajectories that navigate vehicles from their start positions to non-interchangeable goal positions in a collision-free manner. The algorithm operates in two phases. In the motion planning step, a geometric algorithm finds a collision-free, piecewise-linear trajectory for each robot. In the trajectory generation step, each robot’s trajectory is refined into a higher-order piecewise polynomial with a quadratic program. The novelty of our method is in this problem decomposition. The motion plan, through abstracting away robots’ dynamics, can be found quickly. It is then subsequently leveraged to construct collision avoidance constraints for N decoupled quadratic programs instead of a single, coupled optimization problem, decreasing computation time. We prove that this method is safe, complete, and generates smooth trajectories that respect robots’ dynamics. We demonstrate the algorithm’s practicality through extensive quadrotor experiments.
机译:在这项工作中,我们提出了“保持或采取最佳计划”(HOOP),这是一种集中式轨迹生成算法,用于在无障碍,二维,连续工作空间中运行的带标签的多机器人系统。给定一个由N个机器人组成的团队,每个机器人都具有n阶动力学,我们的算法会找到以无碰撞方式将车辆从其起始位置导航到不可互换的目标位置的轨迹。该算法分两个阶段运行。在运动计划步骤中,几何算法为每个机器人找到了无碰撞的分段线性轨迹。在轨迹生成步骤中,使用二次程序将每个机器人的轨迹细化为高阶分段多项式。我们方法的新颖之处在于该问题的分解。通过抽象出机器人的动态,可以快速找到运动计划。然后,随后利用它来构造N个解耦二次程序的碰撞避免约束,而不是单个耦合优化问题,从而减少了计算时间。我们证明了这种方法是安全,完整的,并且可以生成尊重机器人动力学的平滑轨迹。我们通过广泛的四旋翼实验证明了该算法的实用性。

著录项

  • 来源
    《The International journal of robotics research》 |2018年第9期|1062-1084|共23页
  • 作者单位

    General Robotics, Automation, Sensing & Perception (GRASP) Laboratory, University of Pennsylvania, USA;

    General Robotics, Automation, Sensing & Perception (GRASP) Laboratory, University of Pennsylvania, USA;

    General Robotics, Automation, Sensing & Perception (GRASP) Laboratory, University of Pennsylvania, USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Multirobot planning; aerial robotics; motion control;

    机译:多机器人计划;航空机器人;运动控制;

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