We propose a shared semantic map architecture to construct and configure Model Predictive Controllers (MPC) dynamically, that solve navigation problems for multiple robotic agents sharing parts of the same environment. The navigation task is represented as a sequence of semantically labeled areas in the map, that must be traversed sequentially, i.e. a route. Each semantic label represents one or more constraints on the robots' motion behaviour in that area. The advantages of this approach are: (i) an MPC-based motion controller in each individual robot can be (re-)configured, at runtime, with the locally and temporally relevant parameters; (ii) the application can influence, also at runtime, the navigation behaviour of the robots, just by adapting the semantic labels; and (iii) the robots can reason about their need for coordination, through analyzing over which horizon in time and space their routes overlap. The paper provides simulations of various representative situations, showing that the approach of runtime configuration of the MPC drastically decreases computation time, while retaining task execution performance similar to an approach in which each robot always includes all other robots in its MPC computations.

翻译：我们提出一种共享语义地图架构，用于动态构建和配置模型预测控制器（MPC），以解决共享同一环境部分区域的多个机器人智能体的导航问题。导航任务被表示为地图中一系列具有语义标注的区域，这些区域必须按顺序穿越，即构成一条路径。每个语义标签代表该区域内对机器人运动行为的一项或多项约束。该方法具有以下优势：（i）每个独立机器人中的基于MPC的运动控制器可在运行时被（重新）配置为局部且时间相关的参数；（ii）应用层同样可在运行时仅通过调整语义标签来影响机器人的导航行为；（iii）机器人可通过分析其路径在时间和空间上的重叠范围，来推理是否需要协调。本文提供了多种代表性场景的仿真，结果表明运行时配置MPC的方法能大幅降低计算时间，同时保持与每台机器人始终将所有其他机器人纳入其MPC计算的方法相当的任务执行性能。