将最短路径规划嵌入MPC，实现机器人在复杂环境中的快速避障导航。
An MPC framework for efficient navigation of mobile robots in cluttered environments
- 在MPC中融合分段最短路径规划，实时生成安全轨迹。
- 硬件实验显示响应时间仅50-100毫秒，2-3秒内抵达新目标。
- 适用于高动态场景，适合需快速反应的移动机器人系统。
本文提出一种用于复杂环境中移动机器人高效导航的模型预测控制（MPC）框架。该方法将有限段最短路径规划集成到MPC的有限时域轨迹优化中，确保在一般非线性动力学和杂乱环境中收敛至动态选择的目标，并保证碰撞避免。通过在小型地面机器人上的硬件实验验证：人类操作员动态设定目标位置，机器人在2-3秒内到达新目标，对新指令的响应时间为50-100毫秒，即使在高速运动中仍能立即调整行为。该框架有效提升了复杂场景下的实时导航性能。
原文摘要 · Abstract (English)
We present a model predictive control (MPC) framework for efficient navigation of mobile robots in cluttered environments. The proposed approach integrates a finite-segment shortest path planner into the finite-horizon trajectory optimization of the MPC. This formulation ensures convergence to dynamically selected targets and guarantees collision avoidance, even under general nonlinear dynamics and cluttered environments. The approach is validated through hardware experiments on a small ground robot, where a human operator dynamically assigns target locations that a robot should reach while avoiding obstacles. The robot reached new targets within 2-3 seconds and responded to new commands within 50 ms to 100 ms, immediately adjusting its motion even while still moving at high speeds toward a previous target.
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