让机器人在遮挡物密集区安全导航，还能保持运动平滑。
Occlusion-Aware Consistent Model Predictive Control for Robot Navigation in Occluded Obstacle-Dense Environments
- 用可调风险区预测遮挡物位置，动态生成安全约束。
- 生成多条轨迹分支并共享过渡段，实现安全与性能平衡。
- 基于ADMM并行求解，计算高效适合实时应用。
在遮挡物密集环境中保障机器人导航的安全性与运动一致性是一项关键挑战。本文提出一种考虑遮挡的连续模型预测控制（CMPC）策略。为应对遮挡障碍物，该方法引入可调节的风险区域以表征其潜在未来位置，并在线构建动态风险边界约束以增强安全性。基于这些约束，CMPC生成多条局部最优轨迹分支（每条对应不同风险区域），在安全与性能间取得平衡。同时，通过生成共享共识段，确保分支间过渡平滑，避免显著速度波动，保持运动一致性。为提升计算效率并保证局部轨迹协调，采用交替方向乘子法（ADMM）将CMPC分解为可并行求解的子问题。所提策略在阿克曼转向机器人平台上通过仿真与真实实验验证，结果表明其在遮挡、障碍物密集环境下优于基线方法。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。