Authors
Giovanbattista Gravina, Francesco D'Orazio, Michele Cipriano, Tommaso Belvedere, Giuseppe Oriolo
Published in
Frontiers in robotics and AI. Volume 13. Pages 1812386. Epub Jul 27, 2026.
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.
PMID:
42577218
Bibliographic data and abstract were imported from PubMed on 11 Aug 2026.
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