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 controller parameters with respect to a composite navigation objective. In addition, a Gaussian Process surrogate is used to analyze parameter sensitivity and provide insight into the optimization landscape. The proposed framework is robot-agnostic and is evaluated on the Unitree Go2 quadruped in simulation using Gazebo, followed by deployment on the physical robot. Experimental results show that parameters tuned in simulation transfer effectively to hardware, maintaining comparable performance without additional tuning. The full system achieves up to a 90.0\% navigation success rate when deployed, along with a 38.9\% average improvement in the evaluation metrics across simulated environments.

翻译：在动态未知环境中实现实时自主导航仍是移动机器人技术面临的根本性挑战。我们提出一种无地图框架，将反应式滚动时域规划与非线性模型预测控制（MPC）紧密结合。在每个控制周期内，基于激光雷达的高斯占用表示被构建并用于通过A*搜索生成无碰撞轨迹，随后由采用平滑S形障碍屏障的CasADi/IPOPT MPC公式进行跟踪。为增强对参数敏感性的鲁棒性，我们采用基于树状结构Parzen估计器（TPE）的离线贝叶斯优化方案，该方案能针对复合导航目标识别出近优控制器参数。此外，通过高斯过程代理模型分析参数敏感性并揭示优化景观的深层特征。该框架具有机器人无关性，在Gazebo仿真环境中以Unitree Go2四足机器人为载体进行验证，并部署至实体机器人。实验结果表明，仿真调参结果可有效迁移至硬件平台，在无需额外调优的情况下保持可比性能。完整系统在实际部署中实现了高达90.0%的导航成功率，且在不同仿真环境下评估指标平均提升38.9%。