Structure-aware constraint configuration for distributed online trajectory generators: An application to mobile robot navigation

We present a framework for the automatic configuration of constraints in a disturbed model predictive control (MPC) formulation for multi-agent trajectory generation, leveraging structural knowledge encoded in a robot world model. Agent routes are planned as sequences of areas to be traversed, enabling the systematic selection of relevant environmental and inter-agent constraints through queries of the world model. The introduction of sub-areas, such as lanes, further reduces computational complexity by limiting the activation of coupling constraints. In addition, the framework supports reasoning over discrete actions, such as yielding to other agents, allowing agents to satisfy high-level task and environmental constraints within the continuous MPC formulation. The proposed framework is validated in both simulation and hardware-in-the-loop experiments using a distributed computing setup. The results demonstrate improved task execution performance while significantly reducing computation time when compared with baseline distributed formulations.