Crowd navigation in a multi-room environment: a model predictive control framework for mobile robots
TL;DR
The results demonstrate that integrating vision-based semantic data with geometric constraints significantly improves collision avoidance and success rates in cluttered, multi-room scenarios in cluttered, multi-room scenarios.
Abstract
Mobile robots operating in human-populated environments must navigate complex, multi-room spaces while ensuring safety, i.e., generating collision-free motion. In this study, we present a sensor-based model predictive control (MPC) scheme designed for safe crowd navigation in such non-convex environments. The proposed framework decomposes the free space into a set of overlapping convex regions to construct a topological graph, enabling a high-level planner to compute optimal sequences of traversable areas. To effectively perceive the crowd, the system employs a robust perception pipeline that fuses 2D LiDAR data with semantic information from an RGB-D camera, utilizing Kalman filters (KFs) to estimate and predict human motion. These predictions are integrated into an MPC controller which generates robot commands by enforcing safety through discrete-time control barrier function (DT-CBF), ensuring that the robot avoids collisions while remaining within navigable regions. The approach is validated through high-fidelity simulations and real-world experiments using the TIAGo mobile manipulator. The results demonstrate that integrating vision-based semantic data with geometric constraints significantly improves collision avoidance and success rates in cluttered, multi-room scenarios.