We propose a generic multi-robot planning mechanism that combines an optimal task planner and an optimal path planner to provide a scalable solution for complex multi-robot planning problems. The Integrated planner, through the interaction of the task planner and the path planner, produces optimal collision-free trajectories for the robots. We illustrate our general algorithm on an object pick-and-drop planning problem in a warehouse scenario where a group of robots is entrusted with moving objects from one location to another in the workspace. We solve the task planning problem by reducing it into an SMT-solving problem and employing the highly advanced SMT solver Z3 to solve it. To generate collision-free movement of the robots, we extend the state-of-the-art algorithm Conflict Based Search with Precedence Constraints with several domain-specific constraints. We evaluate our integrated task and path planner extensively on various instances of the object pick-and-drop planning problem and compare its performance with a state-of-the-art multi-robot classical planner. Experimental results demonstrate that our planning mechanism can deal with complex planning problems and outperforms a state-of-the-art classical planner both in terms of computation time and the quality of the generated plan.

翻译：我们提出一种通用的多机器人规划机制，该机制将最优任务规划器与最优路径规划器相结合，为复杂的多机器人规划问题提供可扩展的解决方案。集成规划器通过任务规划器与路径规划器的交互，为机器人生成无碰撞的最优轨迹。我们以仓库场景中的物体拾取-放置规划问题为例阐述该通用算法：在该场景中，一组机器人负责将工作空间中的物体从一个位置搬运至另一位置。我们将任务规划问题规约为SMT求解问题，并采用先进的SMT求解器Z3进行求解。为生成机器人的无碰撞运动，我们扩展了前沿的带有优先级约束的冲突搜索算法，并引入多个特定领域约束。我们针对物体拾取-放置规划问题的多种实例对集成任务与路径规划器进行全面评估，并将其性能与先进的多机器人经典规划器进行比较。实验结果表明，我们的规划机制能够处理复杂规划问题，在计算时间和生成计划质量两方面均优于先进的经典规划器。