用图结构规划+混合整数预测控制，让机器人在未知环境中高效重规划路径。
Integration of a Graph-Based Path Planner and Mixed-Integer MPC for Robot Navigation in Cluttered Environments
- 基于中轴图的全局路径规划，每条边对应一个通行通道。
- 通过混合整数预测控制检测约束不可行性，触发重规划。
- 适合动态障碍物多、环境部分已知的移动机器人导航场景。
自主移动机器人在不确定环境中需具备路径重规划能力。本文提出一种多层规划与控制框架，针对环境部分已知的情况设计重规划策略。基于中轴图的规划器根据已知障碍物生成全局路径，图中每条边对应一个独立通道。采用混合整数模型预测控制（MPC）方法，检测由全局路径导出的终端约束在局部非凸环境下的可行性。若约束不可行，则触发高效的全局重规划，通过删除中轴图中的边实现。该策略已在实验中得到验证。
原文摘要 · Abstract (English)
The ability to update a path plan is a required capability for autonomous mobile robots navigating through uncertain environments. This paper proposes a re-planning strategy using a multilayer planning and control framework for cases where the robot's environment is partially known. A medial axis graph-based planner defines a global path plan based on known obstacles, where each edge in the graph corresponds to a unique corridor. A mixed-integer model predictive control (MPC) method detects if a terminal constraint derived from the global plan is infeasible, subject to a non-convex description of the local environment. Infeasibility detection is used to trigger efficient global re-planning via medial axis graph edge deletion. The proposed re-planning strategy is demonstrated experimentally.
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