We present a generalised architecture for reactive mobile manipulation while a robot's base is in motion toward the next objective in a high-level task. By performing tasks on-the-move, overall cycle time is reduced compared to methods where the base pauses during manipulation. Reactive control of the manipulator enables grasping objects with unpredictable motion while improving robustness against perception errors, environmental disturbances, and inaccurate robot control compared to open-loop, trajectory-based planning approaches. We present an example implementation of the architecture and investigate the performance on a series of pick and place tasks with both static and dynamic objects and compare the performance to baseline methods. Our method demonstrated a real-world success rate of over 99%, failing in only a single trial from 120 attempts with a physical robot system. The architecture is further demonstrated on other mobile manipulator platforms in simulation. Our approach reduces task time by up to 48%, while also improving reliability, gracefulness, and predictability compared to existing architectures for mobile manipulation. See https://benburgesslimerick.github.io/ManipulationOnTheMove for supplementary materials.
翻译:当机器人基地正在向下一个高层次任务的下一个目标移动时,我们提出了一个用于被动移动操纵的通用结构。 通过在移动中执行任务,整个周期的时间比在操作期间停止基点的方法要缩短。 对操纵器的回动控制使得能够以无法预测的动作捕捉物体,同时提高抗感知错误、环境扰动和不精确机器人控制的能力,与开放环、基于轨迹的规划方法相比,我们展示了一个执行结构的范例,并调查一系列带有静态和动态对象的选取和定位任务的执行情况,并将性能与基线方法进行比较。我们的方法显示,实际世界的成功率超过99%,仅一次试验中,从实际机器人系统的120次尝试失败。在模拟中其他移动操纵平台上进一步展示了这一结构。我们的方法将任务时间缩短到48%,同时提高可靠性、宽度和可预测性,与现有的移动操纵结构相比,同时提高可靠性、宽度和可预测性。见 补充材料 https://benburgesslimerick.github.io/ManipulateONOV。