基于控制障碍函数的安全多面体在多面体内的运动规划与控制
Safe Polytope-in-Polytope Motion Planning and Control with Control Barrier Functions
AI总结：
提出一种安全局部运动规划与控制方法，通过模型预测控制器中的离散时间控制障碍函数约束，保证多面体机器人足迹始终位于连续更新的凸自由空间内，计算时间随障碍物数量增加最多降低91倍。
摘要：
在狭窄环境中运行的自主移动机器人需要考虑机器人物理足迹的运动规划框架。将几何形状简化为点或圆是保守的，并且丢弃了成功安全通过狭窄通道所需的信息。本文提出了一种安全的局部运动规划与控制方法，保证多面体机器人足迹始终位于连续更新的凸自由空间内。包含条件被表述为模型预测控制器内的一组离散时间控制障碍函数约束。安全约束的数量取决于局部自由空间的复杂性和机器人形状，而不是障碍物的数量。所提出的自由空间公式不需要任何障碍物检测或分割。与基于多面体的避障公式的比较分析证实，随着障碍物数量的增加，计算时间最多减少91倍。该方法在自主水面车辆的仿真中和使用占用网格和LiDAR传感的非完整移动机器人的硬件上得到了验证。实验证明了在机载嵌入式计算机上以10 Hz进行安全的实时运动规划与控制，包括对动态障碍物的反应性避让。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
