让机器人在狭小空间安全穿行，不依赖障碍物识别
Safe Polytope-in-Polytope Motion Planning and Control with Control Barrier Functions
- 用动态凸自由域约束多面体机器人形状，保证全程不碰壁
- 计算量随障碍物增多仅增1/91，实测10Hz实时运行
- 无需障碍物分割，适合嵌入式平台和动态避障场景
自主移动机器人在狭窄环境中运行时，需考虑其真实多面体外形。将机器人简化为点或圆会过于保守，并丢失通过狭窄通道所需信息。本文提出一种安全的局部运动规划与控制方法，确保多面体机器人始终位于持续更新的凸自由空间内。该约束以离散时间控制屏障函数形式嵌入模型预测控制器中，安全约束数量取决于局部自由空间几何复杂度和机器人形状，而非障碍物数量。该自由空间方法无需任何障碍物检测或分割。与基于多面体障碍物避障的方法相比，计算效率显著提升，在障碍物数量增加时计算时间减少91倍。方法在仿真中使用自主水面车辆验证，在硬件上通过非完整移动机器人结合占用栅格与激光雷达传感进行测试。实验表明，可在机载嵌入式计算机上实现10 Hz的实时安全运动规划与控制，包括对动态障碍物的反应式避障。
原文摘要 · Abstract (English)
Autonomous mobile robots operating in tight environments require motion planning frameworks that account for the physical footprint of the robot. Simplifying the geometry to a point or a circle is conservative and discards information needed to successfully and safely traverse narrow passages. This work proposes a safe local motion planning and control method that guarantees that a polytopic robot footprint stays inside a continuously updated convex free-space region. The containment condition is formulated as a set of discrete-time control barrier function constraints within a model predictive controller. The number of safety constraints depends on the complexity of the local free-space geometry and the robot shape, instead of the number of obstacles. The proposed free-space formulation does not need any obstacle detection or segmentation. A comparative analysis against a polytope-based obstacle avoidance formulation confirms favorable scaling up to a reduction of 91$\times$ in computation time as the number of obstacles increases. The approach is validated in simulation with an autonomous surface vehicle and on hardware with a non-holonomic mobile robot, using both occupancy grids and LiDAR sensing. The experiments demonstrate safe real-time motion planning and control at 10~Hz on an onboard embedded computer, including reactive avoidance of dynamic obstacles.
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