Ensuring safety and motion consistency for robot navigation in occluded, obstacle-dense environments is a critical challenge. In this context, this study presents an occlusion-aware Consistent Model Predictive Control (CMPC) strategy. To account for the occluded obstacles, it incorporates adjustable risk regions that represent their potential future locations. Subsequently, dynamic risk boundary constraints are developed online to enhance safety. Based on these constraints, the CMPC constructs multiple locally optimal trajectory branches (each tailored to different risk regions) to strike a balance between safety and performance. A shared consensus segment is generated to ensure smooth transitions between branches without significant velocity fluctuations, preserving motion consistency. To facilitate high computational efficiency and ensure coordination across local trajectories, we use the alternating direction method of multipliers (ADMM) to decompose the CMPC into manageable sub-problems for parallel solving. The proposed strategy is validated through simulations and real-world experiments on an Ackermann-steering robot platform. The results demonstrate the effectiveness of the proposed CMPC strategy through comparisons with baseline approaches in occluded, obstacle-dense environments.

翻译：在遮挡严重、障碍物密集的环境中确保机器人导航的安全性和运动一致性是一项关键挑战。在此背景下，本研究提出了一种遮挡感知的一致性模型预测控制（CMPC）策略。为应对被遮挡的障碍物，该策略引入了可调风险区域来表示其潜在的未来位置。随后，在线生成动态风险边界约束以增强安全性。基于这些约束，CMPC构建多个局部最优轨迹分支（每个分支针对不同的风险区域），以在安全性与性能之间取得平衡。生成一个共享共识段，以确保分支间的平滑切换而无显著速度波动，从而保持运动一致性。为实现高计算效率并确保局部轨迹间的协调，我们采用交替方向乘子法（ADMM）将CMPC分解为可并行求解的子问题。通过在阿克曼转向机器人平台上进行仿真和实物实验，验证了所提策略的有效性。结果表明，通过与基线方法在遮挡严重、障碍物密集环境中的比较，所提出的CMPC策略具有显著优势。