论文标题
OA-MPC：可保证的安全机器人导航的咬合感知的MPC和看不见的动态障碍
OA-MPC: Occlusion-Aware MPC for Guaranteed Safe Robot Navigation with Unseen Dynamic Obstacles
论文作者
论文摘要
为了在动态不确定环境中进行安全导航，机器人系统依赖于其他代理的感知和预测。特别是，在摄像机和激光镜头没有数据的封闭区域中，机器人必须能够推理隐形动态剂的潜在运动。这项工作为实时导航提供了一种可证明的安全运动计划计划，在存在遮挡的动态代理的先验环境中。根据可达性分析提供安全保证。与潜在的遮挡药物（例如行人）相关的可远程到达集被计算并纳入计划中。提出了基于迭代优化的计划者，该计划者在两个优化之间交替：非线性模型预测性控制（NMPC）和避免碰撞。通过引入终端停止约束来保证MPC的递归可行性。通过使用Turtlebot机器人进行的模拟研究和硬件实验证明了所提出算法的有效性。可在\ url {https://youtu.be/ounkb5feyuk}上获得实验结果的视频。
For safe navigation in dynamic uncertain environments, robotic systems rely on the perception and prediction of other agents. Particularly, in occluded areas where cameras and LiDAR give no data, the robot must be able to reason about potential movements of invisible dynamic agents. This work presents a provably safe motion planning scheme for real-time navigation in an a priori unmapped environment, where occluded dynamic agents are present. Safety guarantees are provided based on reachability analysis. Forward reachable sets associated with potential occluded agents, such as pedestrians, are computed and incorporated into planning. An iterative optimization-based planner is presented that alternates between two optimizations: nonlinear Model Predictive Control (NMPC) and collision avoidance. Recursive feasibility of the MPC is guaranteed by introducing a terminal stopping constraint. The effectiveness of the proposed algorithm is demonstrated through simulation studies and hardware experiments with a TurtleBot robot. A video of experimental results is available at \url{https://youtu.be/OUnkB5Feyuk}.
