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 ensure safety. The CMPC then constructs multiple locally optimal trajectory branches (each tailored to different risk regions) to balance between exploitation and exploration. A shared consensus trunk is generated to ensure smooth transitions between branches without significant velocity fluctuations, further 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 simulation 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：在障碍物密集且存在遮挡的环境中确保机器人导航的安全性与运动一致性是一项关键挑战。在此背景下，本研究提出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以表征其潜在的未来位置。随后，在线构建动态风险边界约束以确保安全性。CMPC 进而构建多条局部最优轨迹分支（每条分支针对不同的风险区域进行定制），以在利用与探索之间取得平衡。同时生成一个共享的共识主干，以确保分支间的平滑过渡而不产生显著的速度波动，从而进一步保持运动一致性。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与实物实验，验证了所提策略的有效性。通过与基线方法在遮挡密集的障碍物环境中的对比，结果证明了所提出的 CMPC 策略的有效性。