In this letter, an integrated task planning and reactive motion planning framework termed Multi-FLEX is presented that targets real-world, industrial multi-robot applications. Reactive motion planning has been attractive for the purposes of collision avoidance, particularly when there are sources of uncertainty and variation. Most industrial applications, though, typically require parts of motion to be at least partially non-reactive in order to achieve functional objectives. Multi-FLEX resolves this dissonance and enables such applications to take advantage of reactive motion planning. The Multi-FLEX framework achieves 1) coordination of motion requests to resolve task-level conflicts and overlaps, 2) incorporation of application-specific task constraints into online motion planning using the new concepts of task dependency accommodation, task decomposition, and task bundling, and 3) online generation of robot trajectories using a custom, online reactive motion planner. This planner combines fast-to-create, sparse dynamic roadmaps (to find a complete path to the goal) with fast-to-execute, short-horizon, online, optimization-based local planning (for collision avoidance and high performance). To demonstrate, we use two six-degree-of-freedom, high-speed industrial robots in a deburring application to show the ability of this approach to not just handle collision avoidance and task variations, but to also achieve industrial applications.

翻译：本文提出了一种名为Multi-FLEX的集成任务规划与反应式运动规划框架，旨在解决实际工业多机器人应用中的问题。反应式运动规划在碰撞规避方面具有显著优势，尤其适用于存在不确定性和变化因素的场景。然而，大多数工业应用通常要求部分运动具有至少一定程度的非反应性，以实现功能性目标。Multi-FLEX解决了这一矛盾，使此类应用能够充分利用反应式运动规划的优势。该框架实现了：1）协同运动请求以解决任务级冲突与重叠；2）借助任务依赖适配、任务分解与任务捆绑等新概念，将应用特定任务约束融入在线运动规划；3）利用定制的在线反应式运动规划器在线生成机器人轨迹。该规划器将快速构建的稀疏动态路标图（用于寻找通往目标的完整路径）与快速执行的短时域在线优化局部规划（用于碰撞规避与高性能运行）相结合。为验证效果，我们采用两台六自由度高速工业机器人进行去毛刺应用演示，结果表明该方法不仅能处理碰撞规避与任务变化，更能满足工业应用的实际需求。