Bayesian Optimization for Learning Nonlinear MPC in Autonomous Agent Navigation
Real-time autonomous navigation in dynamic, unknown environments remains a fundamental challenge for mobile robotics. We propose a map-free framework that tightly integrates reactive rolling-horizon planning with nonlinear Model Predictive Control (MPC). At each control cycle, a LiDAR-based Gaussian occupancy representation is constructed and used to generate collision-free trajectories via A* search, which are then tracked by a CasADi/IPOPT MPC formulation incorporating a smooth sigmoid obstacle barrier. To improve robustness to parameter sensitivity, we adopt an offline Bayesian optimization scheme based on Tree-structured Parzen Estimators (TPE), which identifies near-optimal controlle...
| Authors | Lorenzo Ortolani, Gabriel Voss, Gabriele Beltrami et al. |
|---|---|
| Primary category | cs.RO |
| Published | 8 June 2026 |
| Last updated at source | 8 June 2026 |
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