Real-Time Adaptive Motion Planning (RAMP) of Mobile Manipulators in Dynamic Environments With Unforeseen Changes
Real-Time Adaptive Motion Planning (RAMP) of Mobile Manipulators in Dynamic Environments With Unforeseen Changes
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DOI：
10.1109/tro.2008.2003277
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发表时间：
2008-10
影响因子：
7.8
通讯作者：
John Vannoy;J. Xiao
中科院分区：
文献类型：
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作者：
John Vannoy;J. Xiao
本文介绍了一种新颖且通用的实时自适应运动规划（RAMP）方法，该方法适用于在存在未知轨迹移动障碍物的动态环境中规划高自由度或冗余机器人（如移动操作臂）的轨迹。RAMP方法能够同时进行路径和轨迹规划以及实时的运动规划和执行。它有助于在各种优化标准下对轨迹进行实时优化，例如最小化能量和时间以及最大化可操作性。它还能轻松适应机器人部分指定的任务目标。该方法通过机器人配置变量的松散耦合来利用冗余机器人中的冗余（例如移动操作臂中的移动与操作），以最佳地实现避障和优化目标。RAMP方法已经在多种任务环境的模拟中实现和测试，包括有多个移动操作臂的环境。结果（以及附带的视频）表明，RAMP规划器具有高效性和灵活性，不仅能在除静态障碍物外还存在各种未知运动障碍物的动态环境中很好地处理单个移动操作臂，而且还能在多个移动操作臂和其他移动障碍物共享的环境中轻松有效地为每个移动操作臂规划运动。
This paper introduces a novel and general real-time adaptive motion planning (RAMP) approach suitable for planning trajectories of high-DOF or redundant robots, such as mobile manipulators, in dynamic environments with moving obstacles of unknown trajectories. The RAMP approach enables simultaneous path and trajectory planning and simultaneous planning and execution of motion in real time. It facilitates real-time optimization of trajectories under various optimization criteria, such as minimizing energy and time and maximizing manipulability. It also accommodates partially specified task goals of robots easily. The approach exploits redundancy in redundant robots (such as locomotion versus manipulation in a mobile manipulator) through loose coupling of robot configuration variables to best achieve obstacle avoidance and optimization objectives. The RAMP approach has been implemented and tested in simulation over a diverse set of task environments, including environments with multiple mobile manipulators. The results (and also the accompanying video) show that the RAMP planner, with its high efficiency and flexibility, not only handles a single mobile manipulator well in dynamic environments with various obstacles of unknown motions in addition to static obstacles, but can also readily and effectively plan motions for each mobile manipulator in an environment shared by multiple mobile manipulators and other moving obstacles.